Vol.72, No.2, 2022-Table of Contents
  • EACR-LEACH: Energy-Aware Cluster-based Routing Protocol for WSN Based IoT
  • Abstract Internet of Things (IoT) is a recent paradigm to improve human lifestyle. Nowadays, number devices are connected to the Internet drastically. Thus, the people can control and monitor the physical things in real-time without delay. The IoT plays a vital role in all kind of fields in our world such as agriculture, livestock, transport, and healthcare, grid system, connected home, elderly people carrying system, cypher physical system, retail, and intelligent systems. In IoT energy conservation is a challenging task, as the devices are made up of low-cost and low-power sensing devices and local processing. IoT networks have significant challenges in… More
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  • Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm
  • Abstract Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic… More
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  • Fuzzy MCDM Model for Selection of Infectious Waste Management Contractors
  • Abstract Healthcare supply chains are under pressure to drive down costs because of digital business, shifting customer needs and new competition. Medical waste generated from medical facilities includes medical activities and daily-life activities of patients and their family members. According to statistics of the Department of Health Environmental Management, Vietnam currently has more than 13,500 medical facilities, including hospitals from central to provincial and district levels and private hospitals and medical facilities. Preventive medicine generates about 590 tons of medical waste/day and is estimated to be about 800 tons/day. Medical waste includes ordinary medical waste and hazardous medical waste; in which… More
  •   Views:493       Downloads:394        Download PDF
  • An Efficient Scheme for Data Pattern Matching in IoT Networks
  • Abstract The Internet has become an unavoidable trend of all things due to the rapid growth of networking technology, smart home technology encompasses a variety of sectors, including intelligent transportation, allowing users to communicate with anybody or any device at any time and from anywhere. However, most things are different now. Background: Structured data is a form of separated storage that slows down the rate at which everything is connected. Data pattern matching is commonly used in data connectivity and can help with the issues mentioned above. Aim: The present pattern matching system is ineffective due to the heterogeneity and rapid… More
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  • Feedline Separation for Independent Control of Simultaneously Different Tx/Rx Radiation Patterns
  • Abstract The shortcoming of Wi-Fi networks is that one user can access the router at a time. This drawback limits the system throughput and delay. This paper proposes a concept of Simultaneously Different Tx/Rx (SDTR) radiation patterns with only one antenna set at the router. Furthermore, these two patterns have to be simultaneously operated at the same time so that the system delay can be eased. An omni-directional pattern is employed at router for receiving mode so that the router can sense carrier signal from all directions. At the same time, the router launches a directional beam pointed to another user.… More
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  • Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features
  • Abstract Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature… More
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  • Thermomechanical Behavior of Brake Drums Under Extreme Braking Conditions
  • Abstract Braking efficiency is characterized by reduced braking time and distance, and therefore passenger safety depends on the design of the braking system. During the braking of a vehicle, the braking system must dissipate the kinetic energy by transforming it into heat energy. A too high temperature can lead to an almost total loss of braking efficiency. An excessive rise in brake temperature can also cause surface cracks extending to the outside edge of the drum friction surface. Heat transfer and temperature gradient, not to forget the vehicle's travel environment (high speed, heavy load, and steeply sloping road conditions), must thus… More
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  • Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures
  • Abstract Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively. Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes. The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc. Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an effective forensic investigation process becomes… More
  •   Views:397       Downloads:231        Download PDF
  • Supplier Selection Fuzzy Model in Supply Chain Management: Vietnamese Cafe Industry Case
  • Abstract Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization. Choosing the right supplier can help businesses increase productivity, competitiveness in the market, and profits without having to lower the quality of the products. However, choosing a supplier is not a simple matter, it requires businesses to consider many aspects about their suppliers. Therefore, the goal of this study is to propose an integrated model consisting of two models: Fuzzy Analytics Network Process (Fuzzy-ANP) model and Weighted Aggregated Sum Product… More
  •   Views:409       Downloads:253        Download PDF
  • Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning
  • Abstract The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trial-and-error processes. However, the prediction accuracy depends heavily on the quality of… More
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  • Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification
  • Abstract In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu… More
  •   Views:398       Downloads:306        Download PDF
  • Intelligent Dynamic Inversion Controller Design for Ball and Beam System
  • Abstract The Ball and beam system (BBS) is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties. Designing an effective ball and beam system controller is a real challenge for researchers and engineers. In this paper, the control design technique is investigated by using Intelligent Dynamic Inversion (IDI) method for this nonlinear and unstable system. The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it. The Moore-Penrose Generalized Inverse (MPGI) is used to invert the prescribed constraint dynamics to realize the baseline control law. A sliding… More
  •   Views:363       Downloads:250        Download PDF
  • Bio-Inspired Computational Methods for the Polio Virus Epidemic Model
  • Abstract In 2021, most of the developing countries are fighting polio, and parents are concerned with the disabling of their children. Poliovirus transmits from person to person, which can infect the spinal cord, and paralyzes the parts of the body within a matter of hours. According to the World Health Organization (WHO), 18 million currently healthy people could have been paralyzed by the virus during 1988–2020. Almost all countries but Pakistan, Afghanistan, and a few more have been declared polio-free. The mathematical modeling of poliovirus is studied in the population by categorizing it as susceptible individuals (S), exposed individuals (E), infected… More
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  • A Modified-Simplified MPPT Technique for Three-Phase Single-State Grid-Connected PV Systems
  • Abstract Nowadays, the single state inverter for the grid-connected photovoltaic (PV) systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter. This paper focuses on the use of model predictive control (MPC) to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point (MPP). The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow. The reference current (Id*) was used to control the distribution of electrical power from the solar cell to the… More
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  • Linearly Polarized Millimeter Wave Reflectarray with Mutual Coupling Optimization
  • Abstract This work provides the design and analysis of a single layer, linearly polarized millimeter wave reflectarray antenna with mutual coupling optimization. Detailed analysis was carried out at 26 GHz design frequency using the simulations of the reflectarray unit cells as well as the periodic reflectarray antenna. The simulated results were verified by the scattering parameter and far-field measurements of the unit cell and periodic arrays, respectively. A close agreement between the simulated and measured results was observed in all the cases. Apart from the unit cells and reflectarray, the waveguide and horn antenna were also fabricated to be used in the… More
  •   Views:540       Downloads:260        Download PDF
  • Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates
  • Abstract With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety,… More
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  • Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model
  • Abstract Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to… More
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  • Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure
  • Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common… More
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  • Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation
  • Abstract The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved… More
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  • QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities
  • Abstract In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is… More
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  • Sign Language to Sentence Formation: A Real Time Solution for Deaf People
  • Abstract Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular… More
  •   Views:383       Downloads:233        Download PDF
  • Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors
  • Abstract In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM,… More
  •   Views:380       Downloads:224        Download PDF
  • Efficient Deep Learning Modalities for Object Detection from Infrared Images
  • Abstract For military warfare purposes, it is necessary to identify the type of a certain weapon through video stream tracking based on infrared (IR) video frames. Computer vision is a visual search trend that is used to identify objects in images or video frames. For military applications, drones take a main role in surveillance tasks, but they cannot be confident for long-time missions. So, there is a need for such a system, which provides a continuous surveillance task to support the drone mission. Such a system can be called a Hybrid Surveillance System (HSS). This system is based on a distributed… More
  •   Views:363       Downloads:252        Download PDF
  • Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19
  • Abstract The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours… More
  •   Views:375       Downloads:230        Download PDF
  • Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining
  • Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique to choose an optimal subset… More
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  • An Intelligent HealthCare Monitoring Framework for Daily Assistant Living
  • Abstract Human Activity Recognition (HAR) plays an important role in life care and health monitoring since it involves examining various activities of patients at homes, hospitals, or offices. Hence, the proposed system integrates Human-Human Interaction (HHI) and Human-Object Interaction (HOI) recognition to provide in-depth monitoring of the daily routine of patients. We propose a robust system comprising both RGB (red, green, blue) and depth information. In particular, humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map. To track the movement of humans, we proposed… More
  •   Views:337       Downloads:223        Download PDF
  • A Robust Emergency Messages Routing Scheme for Urban VANETs
  • Abstract Vehicular ad-hoc networks (VANETs) play an essential role in enhancing transport infrastructure by making vehicles intelligent and proficient in preventing traffic fatalities. Direction-based greedy protocols pick the next route vehicle for transmitting emergency messages (EMs) depending upon the present location of adjacent vehicles towards sink vehicles by using an optimal uni-directional road traffic approach. Nevertheless, such protocols suffer performance degradation by ignoring the moving directions of vehicles in bi-directional road traffic where topological changes happen continuously. Due to the high number of vehicles, it is essential to broadcast EMs to all vehicles to prevent traffic delays and collisions. A cluster-based… More
  •   Views:401       Downloads:218        Download PDF
  • Deep Learning with Image Classification Based Secure CPS for Healthcare Sector
  • Abstract Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a… More
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  • A Deep Learning Hierarchical Ensemble for Remote Sensing Image Classification
  • Abstract Artificial intelligence, which has recently emerged with the rapid development of information technology, is drawing attention as a tool for solving various problems demanded by society and industry. In particular, convolutional neural networks (CNNs), a type of deep learning technology, are highlighted in computer vision fields, such as image classification and recognition and object tracking. Training these CNN models requires a large amount of data, and a lack of data can lead to performance degradation problems due to overfitting. As CNN architecture development and optimization studies become active, ensemble techniques have emerged to perform image classification by combining features extracted… More
  •   Views:342       Downloads:232        Download PDF
  • Machine Learning with Dimensionality Reduction for DDoS Attack Detection
  • Abstract With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However,… More
  •   Views:376       Downloads:239        Download PDF
  • Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images
  • Abstract Due to latest advancements in the field of remote sensing, it becomes easier to acquire high quality images by the use of various satellites along with the sensing components. But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images. Therefore, image compression techniques can be utilized to process remote sensing images. In this aspect, vector quantization (VQ) can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray (LBG) which creates a local optimum codebook for image construction. The process of constructing the codebook can be treated as… More
  •   Views:457       Downloads:240        Download PDF
  • Hybrid Machine Learning Model for Face Recognition Using SVM
  • Abstract Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN. PCA-SVM, however,… More
  •   Views:409       Downloads:243        Download PDF
  • Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System
  • Abstract Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to… More
  •   Views:346       Downloads:211        Download PDF
  • Embedded Coded Relay System for Molecular Communications
  • Abstract With the emergence of the COVID-19 pandemic, the World Health Organization (WHO) has urged scientists and industrialists to explore modern information and communication technology (ICT) as a means to reduce or even eliminate it. The World Health Organization recently reported that the virus may infect the organism through any organ in the living body, such as the respiratory, the immunity, the nervous, the digestive, or the cardiovascular system. Targeting the abovementioned goal, we envision an implanted nanosystem embedded in the intra living-body network. The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or… More
  •   Views:357       Downloads:246        Download PDF
  • A Post-Processing Algorithm for Boosting Contrast of MRI Images
  • Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More
  •   Views:336       Downloads:204        Download PDF
  • Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings
  • Abstract When firefighters are engaged in search and rescue missions inside a building at a risk of collapse, they have difficulty in field command and rescue because they can only simply monitor the situation inside the building utilizing old building drawings or robots. To propose an efficient solution for fast search and rescue work of firefighters, this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation, and identifying the information of obstacles which are risk factors, using an artificial intelligence algorithm based on low-cost robots. Our research separates the floor by using the mask… More
  •   Views:386       Downloads:227        Download PDF
  • Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems
  • Abstract Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling… More
  •   Views:361       Downloads:216        Download PDF
  • A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks
  • Abstract Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for the fifth-generation (5G) wireless networks. It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station. However, increasing the number of antennas requires a large number of radio frequency (RF) chains which results in high power consumption. In order to reduce the RF chain's energy, cost and provide desirable quality-of-service (QoS) to the subscribers, this paper proposes an energy-efficient hybrid precoding algorithm for mmWave massive MIMO networks based on the idea of RF chains… More
  •   Views:393       Downloads:235        Download PDF
  • Deep Learning Control for Autonomous Robot
  • Abstract Several applications of machine learning and artificial intelligence, have acquired importance and come to the fore as a result of recent advances and improvements in these approaches. Autonomous cars are one such application. This is expected to have a significant and revolutionary influence on society. Integration with smart cities, new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles. The autonomous automobile, often known as self-driving systems or driverless vehicles, is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement. Cars are on the verge of evolving into… More
  •   Views:439       Downloads:257        Download PDF
  • Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing
  • Abstract As a new mode and means of smart manufacturing, smart cloud manufacturing (SCM) faces great challenges in massive supply and demand, dynamic resource collaboration and intelligent adaptation. To address the problem, this paper proposes an SCM-oriented dynamic supply-demand (S-D) intelligent adaptation model for massive manufacturing services. In this model, a collaborative network model is established based on the properties of both the supply-demand and their relationships; in addition, an algorithm based on deep graph clustering (DGC) and aligned sampling (AS) is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused… More
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  • Blockchain-based Distributed Power Market Trading Mechanism
  • Abstract Distributed power market trading has the characteristics of large number of participants, scattered locations, small single trading scale, and point-to-point trading. The traditional centralized power trading model has the problems of large load, low efficiency, high cost, reliance on third parties and unreliable data. With the characteristics of decentralization and non-tampering, blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems. Therefore, this paper proposed a distributed power market trading framework based on blockchain. In this framework, the distributed power supply characteristics and trading needs of each participant are analyzed, a complete distributed trading… More
  •   Views:360       Downloads:255        Download PDF
  • Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification
  • Abstract Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer… More
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  • Modified Bat Algorithm for Optimal VM's in Cloud Computing
  • Abstract All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized. The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing. The cloud computing environment is the subject of this study. It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms). This intricate structure is interconnected to provide services to users and improve the overall system's performance. This case study is… More
  •   Views:395       Downloads:228       Cited by:1        Download PDF
  • Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions
  • Abstract As higher education institutions (HEIs) go online, several benefits are attained, and also it is vulnerable to several kinds of attacks. To accomplish security, this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security. The incorporation of the strategies into business is a tendency among several distinct industries, comprising education, have recognized as game changer. Consequently, the HEIs are highly related to the requirement and knowledge of the learner, making the education procedure highly effective. Thus, artificial intelligence (AI) and machine learning (ML) models have shown significant interest in HEIs. This study designs a novel Artificial Intelligence… More
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  • Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty
  • Abstract Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning. Herein, the validity… More
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  • Vehicle Positioning Based on Optical Camera Communication in V2I Environments
  • Abstract Demand for precise vehicle positioning (VP) increases as autonomous vehicles have recently been drawing attention. This paper proposes a scheme for positioning vehicles on the move based on optical camera communication (OCC) technology in the vehicle-to-infrastructure (V2I) environment. Light-emitting diode (LED) streetlights and vehicle cameras are used as transmitters and receivers respectively. Regions of streetlights are detected and traced by examining images that are obtained from cameras of vehicles. Then, a scheme for analyzing visible light data extracted from the images is proposed. The proposed vehicle positioning scheme uses information on angles between vectors that are formed under the collinearity… More
  •   Views:350       Downloads:213        Download PDF
  • Optimized Hybrid Block Adams Method for Solving First Order Ordinary Differential Equations
  • Abstract Multistep integration methods are being extensively used in the simulations of high dimensional systems due to their lower computational cost. The block methods were developed with the intent of obtaining numerical results on numerous points at a time and improving computational efficiency. Hybrid block methods for instance are specifically used in numerical integration of initial value problems. In this paper, an optimized hybrid block Adams block method is designed for the solutions of linear and nonlinear first-order initial value problems in ordinary differential equations (ODEs). In deriving the method, the Lagrange interpolation polynomial was employed based on some data points… More
  •   Views:347       Downloads:233        Download PDF
  • Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks
  • Abstract Resource management in Underground Wireless Sensor Networks (UWSNs) is one of the pillars to extend the network lifetime. An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization. This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data, received from the buried source nodes through a lossy soil medium, to the aboveground base station. A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover (HCSSC) algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency. The… More
  •   Views:315       Downloads:217        Download PDF
  • Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor
  • Abstract Individuals and PCs (personal computers) can be recognized using CAPTCHAs (Completely Automated Public Turing test to distinguish Computers and Humans) which are mechanized for distinguishing them. Further, CAPTCHAs are intended to be solved by the people, but are unsolvable by the machines. As a result, using Convolutional Neural Networks (CNNs) these tests can similarly be unraveled. Moreover, the CNNs quality depends majorly on: the size of preparation set and the information that the classifier is found out on. Next, it is almost unmanageable to handle issue with CNNs. A new method of detecting CAPTCHA has been proposed, which simultaneously solves… More
  •   Views:304       Downloads:212        Download PDF
  • Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security
  • Abstract As fifth generation technology standard (5G) technology develops, the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing. The existing personal recognition method used for granting permission is a password-based method, which causes security problems. Therefore, personal recognition studies using bio-signals are being conducted as a method to access control to devices. Among bio-signal, surface electromyogram (sEMG) can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation. Furthermore, as an advantage, sEMG… More
  •   Views:323       Downloads:222        Download PDF
  • Metaheuristic Lightweight Cryptography for Security Enhancement inInternet of Things
  • Abstract The advancements made in Internet of Things (IoT) is projected to alter the functioning of healthcare industry in addition to increased penetration of different applications. However, data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure operation. With this background, the current paper proposes a novel lightweight cryptography method for enhance the security in IoT. The proposed encryption algorithm is a blend of Cross Correlation Coefficient (CCC) and Black Widow Optimization (BWO) algorithm. In the presented encryption technique, CCC operation is utilized to optimize the encryption process of cryptography method.… More
  •   Views:363       Downloads:247        Download PDF
  • Secret Key Optimization for Secure Speech Communications
  • Abstract This paper answers three essential questions for audio speech cryptosystems in time and discrete transform domains. The first question is, what are the best values of sub-keys that must be used to get the best quality and security for the audio cryptosystem in time and discrete transform domains. The second question is the relation between the number of sub-keys, the number of secret keys used, and the audio speech signal block’s size. Finally, how many possible secret keys can be used to get the best quality and security results for the audio speech cryptosystem in time and discrete transform domains.… More
  •   Views:305       Downloads:197        Download PDF
  • Sammon Quadratic Recurrent Multilayer Deep Classifier for Legal Document Analytics
  • Abstract In recent years, machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain. The legal field is strongly affected by the problem of information overload, due to the large amount of legal material stored in textual form. Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions. With an increasing number of digitally available documents, legal text processing is essential to analyze documents which helps to automate various legal domain tasks. Legal document classification is a valuable tool… More
  •   Views:312       Downloads:202        Download PDF
  • Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems
  • Abstract Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process takes place by the use… More
  •   Views:307       Downloads:205        Download PDF
  • Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding
  • Abstract In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first… More
  •   Views:387       Downloads:244        Download PDF
  • VPN and Non-VPN Network Traffic Classification Using Time-Related Features
  • Abstract The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for… More
  •   Views:361       Downloads:215        Download PDF
  • Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen
  • Abstract Energy is an essential element for any civilized country's social and economic development, but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the ecosystem. The Republic of Yemen has very good potential to use renewable energy. Unfortunately, we find few studies on renewable wind energy in Yemen. Given the lack of a similar analysis for the coastal city, this research newly investigates wind energy's potential near the Almukalla area by analyzing wind characteristics. Thus, evaluation, model identification, determination of available energy density, computing the capacity factors for several wind turbines and… More
  •   Views:307       Downloads:196        Download PDF
  • Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions
  • Abstract Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique… More
  •   Views:358       Downloads:236        Download PDF
  • Deep Learning Framework for Classification of Emoji Based Sentiments
  • Abstract Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS). Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts. Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision. This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments. Proposed methodology consists upon three… More
  •   Views:312       Downloads:204        Download PDF
  • Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4
  • Abstract In construction, it is important to check whether workers wear safety helmets in real time. We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time. As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved. (2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the… More
  •   Views:427       Downloads:308        Download PDF
  • An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers
  • Abstract Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve net-zero carbon by 2060, NEOM city has been proposed to be the “Saudi hub” for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global… More
  •   Views:388       Downloads:205        Download PDF
  • Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques
  • Abstract Heart disease is one of the leading causes of death in the world today. Prediction of heart disease is a prominent topic in the clinical data processing. To increase patient survival rates, early diagnosis of heart disease is an important field of research in the medical field. There are many studies on the prediction of heart disease, but limited work is done on the selection of features. The selection of features is one of the best techniques for the diagnosis of heart diseases. In this research paper, we find optimal features using the brute-force algorithm, and machine learning techniques are… More
  •   Views:363       Downloads:215       Cited by:1        Download PDF
  • Bio-Inspired Numerical Analysis of COVID-19 with Fuzzy Parameters
  • Abstract Fuzziness or uncertainties arise due to insufficient knowledge, experimental errors, operating conditions and parameters that provide inaccurate information. The concepts of susceptible, infectious and recovered are uncertain due to the different degrees in susceptibility, infectivity and recovery among the individuals of the population. The differences can arise, when the population groups under the consideration having distinct habits, customs and different age groups have different degrees of resistance, etc. More realistic models are needed which consider these different degrees of susceptibility infectivity and recovery of the individuals. In this paper, a Susceptible, Infected and Recovered (SIR) epidemic model with fuzzy parameters… More
  •   Views:382       Downloads:211        Download PDF
  • Solving Cauchy Issues of Highly Nonlinear Elliptic Equations Using a Meshless Method
  • Abstract In this paper, we address 3D inverse Cauchy issues of highly nonlinear elliptic equations in large cuboids by utilizing the new 3D homogenization functions of different orders to adapt all the specified boundary data. We also add the average classification as an approximate solution to the nonlinear operator part, without requiring to cope with nonlinear equations to resolve the weighting coefficients because these constructions are owned many conditions about the true solution. The unknown boundary conditions and the result can be retrieved straightway by coping with a small-scale linear system when the outcome is described by a new 3D homogenization… More
  •   Views:311       Downloads:222        Download PDF
  • Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity
  • Abstract For Internet forum Points of Interest (PoI), existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation, which lead to blindness in method selection. To address this problem, this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows. Based on the framework, this paper presented 5 PoI analysis algorithms which can be categorized into 2 types, i.e., the traditional sequence analysis methods such as autoregressive integrated moving average model (ARIMA), support vector regressor (SVR), and the deep learning methods such as convolutional neural network (CNN), long-short… More
  •   Views:352       Downloads:239        Download PDF
  • Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN
  • Abstract In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does… More
  •   Views:349       Downloads:203        Download PDF
  • Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites
  • Abstract Artificial neural networks (ANNs) are attracting attention for their high performance in various fields, because increasing the network size improves its functioning. Since large-scale neural networks are difficult to implement on custom hardware, a two-dimensional (2D) structure is applied to an ANN in the form of a crossbar. We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips. The system is designed using two-dimensional structures, graphene quantum dots (GQDs) and graphene oxide (GO). Raman spectrum analysis results indicate a D-band of 1421 cm−1 that occurs in the disorder; band is expressed as an atomic… More
  •   Views:309       Downloads:223        Download PDF
  • DWT-SVD Based Image Steganography Using Threshold Value Encryption Method
  • Abstract Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual system. This paper presents an image scrambling method that is very useful for grayscale secret images. In this method, the secret image decomposes in three parts based on the pixel's threshold value. The division of the color image into three parts is very easy based on the color channel but in the grayscale image, it is difficult to implement. The proposed image scrambling method is implemented in image steganography using discrete wavelet transform… More
  •   Views:352       Downloads:203        Download PDF
  • Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification
  • Abstract Image classification always has open challenges for computer vision research. Nowadays, deep learning has promoted the development of this field, especially in Convolutional Neural Networks (CNNs). This article proposes the development of efficiently scaled dilation of DropBlock optimization in CNNs for the fungus classification, which there are five species in this experiment. The proposed technique adjusts the convolution size at 35, 45, and 60 with the max-polling size 2 × 2. The CNNs models are also designed in 12 models with the different BlockSizes and KeepProp. The proposed techniques provide maximum accuracy of 98.30% for the training set. Moreover, three accurate models,… More
  •   Views:325       Downloads:218        Download PDF
  • Modeling and Verification of Aircraft Takeoff Through Novel Quantum Nets
  • Abstract The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical operation. The task of aircraft takeoff is distributed amongst various computer-based controllers, however, with the growing malicious threats a secure communication between aircraft and controllers becomes highly important. This research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff procedure. The integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri nets. To model certain important properties of BB84, a new variant of petri nets… More
  •   Views:329       Downloads:237        Download PDF
  • Reversible Video Steganography Using Quick Response Codes and Modified ElGamal Cryptosystem
  • Abstract The rapid transmission of multimedia information has been achieved mainly by recent advancements in the Internet's speed and information technology. In spite of this, advancements in technology have resulted in breaches of privacy and data security. When it comes to protecting private information in today's Internet era, digital steganography is vital. Many academics are interested in digital video because it has a great capability for concealing important data. There have been a vast number of video steganography solutions developed lately to guard against the theft of confidential data. The visual imperceptibility, robustness, and embedding capacity of these approaches are all… More
  •   Views:570       Downloads:236        Download PDF
  • Finger Vein Authentication Based on Wavelet Scattering Networks
  • Abstract Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two decades. Multiple biometrics such as fingerprint, palm, iris, palm vein and finger vein and other biometrics have been introduced. One of the challenges in biometrics is physical injury. Biometric of finger vein is of the biometrics least exposed to physical damage. Numerous methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification rate. This paper presents a novel method of scattering wavelet-based identity identification. Scattering wavelet extracts image… More
  •   Views:345       Downloads:188        Download PDF
  • Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution
  • Abstract Education acts as an important part of economic growth and improvement in human welfare. The educational sectors have transformed a lot in recent days, and Information and Communication Technology (ICT) is an effective part of the education field. Almost every action in university and college, right from the process from counselling to admissions and fee deposits has been automated. Attendance records, quiz, evaluation, mark, and grade submissions involved the utilization of the ICT. Therefore, security is essential to accomplish cybersecurity in higher security institutions (HEIs). In this view, this study develops an Automated Outlier Detection for CyberSecurity in Higher Education… More
  •   Views:376       Downloads:315        Download PDF
  • Ransomware Classification Framework Using the Behavioral Performance Visualization of Execution Objects
  • Abstract A ransomware attack that interrupted the operation of Colonial Pipeline (a large U.S. oil pipeline company), showed that security threats by malware have become serious enough to affect industries and social infrastructure rather than individuals alone. The agents and characteristics of attacks should be identified, and appropriate strategies should be established accordingly in order to respond to such attacks. For this purpose, the first task that must be performed is malware classification. Malware creators are well aware of this and apply various concealment and avoidance techniques, making it difficult to classify malware. This study focuses on new features and classification… More
  •   Views:379       Downloads:249        Download PDF
  • Process Tolerant and Power Efficient SRAM Cell for Internet of Things Applications
  • Abstract The use of Internet of Things (IoT) applications become dominant in many systems. Its on-chip data processing and computations are also increasing consistently. The battery enabled and low leakage memory system at subthreshold regime is a critical requirement for these IoT applications. The cache memory designed on Static Random-Access Memory (SRAM) cell with features such as low power, high speed, and process tolerance are highly important for the IoT memory system. Therefore, a process tolerant SRAM cell with low power, improved delay and better stability is presented in this research paper. The proposed cell comprises 11 transistors designed with symmetric… More
  •   Views:315       Downloads:212        Download PDF
  • Image Encryption Using Multi-Scroll Attractor and Chaotic Logistic Map
  • Abstract In the current scenario, data transmission over the network is a challenging task as there is a need for protecting sensitive data. Traditional encryption schemes are less sensitive and less complex thus prone to attacks during transmission. It has been observed that an encryption scheme using chaotic theory is more promising due to its non-linear and unpredictable behavior. Hence, proposed a novel hybrid image encryption scheme with multi-scroll attractors and quantum chaos logistic maps (MSA-QCLM). The image data is classified as inter-bits and intra-bits which are permutated separately using multi scroll attractor & quantum logistic maps to generate random keys.… More
  •   Views:350       Downloads:203        Download PDF
  • Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems
  • Abstract Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS)… More
  •   Views:358       Downloads:211        Download PDF
  • Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System
  • Abstract Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response, less human interference, high dependability, improved hygiene, and reduced aging effects. That is why, in recent years, robotic aid has emerged as a blossoming solution to many challenges in the medical industry. In this manuscript, meta-heuristics (MH) algorithms, specifically the Firefly Algorithm (FF) and Genetic Algorithm (GA), are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd. The controller is used to control Mobile Robot System (MRS) at the required set… More
  •   Views:349       Downloads:220        Download PDF
  • Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc Networks
  • Abstract Nowadays, video streaming applications are becoming one of the tendencies driving vehicular network users. In this work, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different vehicles included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over vehicular ad-hoc networks (VANET). The proposed algorithm takes advantage of the small cells concept and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)-Advanced network. Vehicles… More
  •   Views:299       Downloads:178        Download PDF
  • Detection of Lung Tumor Using ASPP-Unet with Whale Optimization Algorithm
  • Abstract The unstructured growth of abnormal cells in the lung tissue creates tumor. The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment. Various medical image modalities can help the physicians in the diagnosis of disease. Many research works have been proposed for the early detection of lung tumor. High computation time and misidentification of tumor are the prevailing issues. In order to overcome these issues, this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling (ASPP)-Unet architecture with Whale Optimization Algorithm (ASPP-Unet -WOA). To get a fine tuning detection of tumor… More
  •   Views:409       Downloads:209        Download PDF
  • An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications
  • Abstract Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare extensively between these five algorithms.… More
  •   Views:433       Downloads:234        Download PDF
  • Blockchain-Based Robust Data Security Scheme in IoT-Enabled Smart Home
  • Abstract The recent surge in development of smart homes and smart cities can be observed in many developed countries. While the idea to control devices that are in home (embedded with the Internet of Things (IoT) smart devices) by the user who is outside the home might sound fancy, but it comes with a lot of potential threats. There can be many attackers who will be trying to take advantage of this. So, there is a need for designing a secure scheme which will be able to distinguish among genuine/authorized users of the system and attackers. And knowing about the details… More
  •   Views:358       Downloads:334        Download PDF
  • A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns
  • Abstract The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future. This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed. Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss. Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number. A ranking process therefore needs to be applied prior to use, which causes… More
  •   Views:343       Downloads:200        Download PDF
  • Design of Energy Efficient WSN Using a Noble SMOWA Algorithm
  • Abstract In this paper, the establishment of efficient Wireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficient Wireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization,… More
  •   Views:332       Downloads:239        Download PDF
  • 5G Antenna Gain Enhancement Using a Novel Metasurface
  • Abstract This article presents a Sub-6 GHz microstrip patch antenna (MPA) with enhanced gain using metamaterial (MTM) superstrate. The source MPA operates at 4.8 GHz and has a peak gain of 5.3 dBi at the resonance frequency. A window-shaped unit cell is designed and investigated through the material wave propagation technique. The unit cell shows an Epsilon Near Zero (ENZ)-Mu Very Large (MVL) behavior around 4.8 GHz. The unit cell has a fourfold geometry which makes it a polarization independent metamaterial. A double layer antenna is designed by placing a 4 × 4 MTM slab as a superstrate above the MPA at a… More
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  • Selecting Best Software Vulnerability Scanner Using Intuitionistic Fuzzy Set TOPSIS
  • Abstract Software developers endeavor to build their products with the least number of bugs. Despite this, many vulnerabilities are detected in software that threatens its integrity. Various automated software i.e., vulnerability scanners, are available in the market which helps detect and manage vulnerabilities in a computer, application, or a network. Hence, the choice of an appropriate vulnerability scanner is crucial to ensure efficient vulnerability management. The current work serves a dual purpose, first, to identify the key factors which affect the vulnerability discovery process in a network. The second, is to rank the popular vulnerability scanners based on the identified attributes.… More
  •   Views:356       Downloads:248        Download PDF
  • AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments
  • Abstract The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,… More
  •   Views:394       Downloads:238       Cited by:2        Download PDF
  • Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT
  • Abstract In the last few years, videos became the most common form of information transmitted over the internet, and a lot of the traffic is confidential and must be protected and delivered safely to its intended users. This introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same time. This paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map (LASM) in the Fractional Fourier Transform (FrFT). In the presented opto-video LASM-based FrFT scheme, the encoded video is split into distinct frames and transformed into optical signals utilizing an optical supply. Each… More
  •   Views:322       Downloads:198        Download PDF
  • Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data
  • Abstract Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research. However, publishing and processing big data poses a privacy concern related to protecting individuals’ sensitive information while maintaining the usability of the published data. Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data. However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure. Moreover,… More
  •   Views:399       Downloads:217        Download PDF
  • Cancellable Multi-Biometric Template Generation Based on Arnold Cat Map and Aliasing
  • Abstract The cancellable biometric transformations are designed to be computationally difficult to obtain the original biometric data. This paper presents a cancellable multi-biometric identification scheme that includes four stages: biometric data collection and processing, Arnold's Cat Map encryption, decimation process to reduce the size, and final merging of the four biometrics in a single generated template. First, a 2D matrix of size 128 × 128 is created based on Arnold's Cat Map (ACM). The purpose of this rearrangement is to break the correlation between pixels to hide the biometric patterns and merge these patterns together for more security. The decimation is performed to… More
  •   Views:361       Downloads:226        Download PDF
  • A Deep Learning Approach for Prediction of Protein Secondary Structure
  • Abstract The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based on machine learning with shallow frameworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed models is amino acid sequences… More
  •   Views:414       Downloads:250        Download PDF
  • Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques
  • Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely:… More
  •   Views:324       Downloads:217        Download PDF
  • Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G
  • Abstract With the obvious throughput shortage in traditional cellular radio networks, Device-to-Device (D2D) communications has gained a lot of attention to improve the utilization, capacity and channel performance of next-generation networks. In this paper, we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks. The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently, aiming to maximize the overall system throughput of D2D underlaied cellular network… More
  •   Views:330       Downloads:207        Download PDF
  • Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment
  • Abstract Industrial Internet of Things (IIoT) is an emerging field which connects digital equipment as well as services to physical systems. Intrusion detection systems (IDS) can be designed to protect the system from intrusions or attacks. In this view, this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection (HDL-MEID) technique for clustered IIoT environments. The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication. Primarily, the HDL-MEID technique designs a new chaotic mayfly optimization (CMFO) based clustering approach for the effective choice of the Cluster Heads (CH) and organize clusters. Moreover,… More
  •   Views:447       Downloads:307        Download PDF
  • An Interpretable Artificial Intelligence Based Smart Agriculture System
  • Abstract With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for… More
  •   Views:396       Downloads:251        Download PDF
  • Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images
  • Abstract Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the… More
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  • Optimized Image Multiplication with Approximate Counter Based Compressor
  • Abstract The processor is greatly hampered by the large dataset of picture or multimedia data. The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake. This study proposes various higher-order approximate counter-based compressor (CBC) using input shuffled 6:3 CBC. In the Wallace multiplier using a CBC is a significant factor in partial product reduction. So the design of 10-4, 11-4, 12-4, 13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage multiplications. The input shuffling aims to reduce the output combination of… More
  •   Views:396       Downloads:217        Download PDF
  • Wavelet Based Detection of Outliers in Volatility Time Series Models
  • Abstract We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wavelet Transform (MODWT) to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers. Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform (DWT). The series sample size does not need to be a power of 2 and the… More
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  • Developing Check-Point Mechanism to Protect Mobile Agent Free-Roaming Against Untrusted Hosts
  • Abstract Mobile Agent has many benefits over traditional distributed systems such as reducing latency, bandwidth, and costs. Mobile Agent Systems are not fully utilized due to security problems. This paper focuses on mobile agent protection against malicious hosts. A new security mechanism called Checkpoints has been proposed. Checkpoint Mechanism (CPM) aims to protect Mobile Agent against malicious hosts in case of Capturing and Integrity attacks. CPM assumes using a free-roaming mobility mechanism by Mobile agent systems. The main idea behind CPM is to generate multiple versions of Mobile Agent. The multiple version is used to recover Mobile Agent from Capturing and… More
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  • Enhance Vertical Handover Security During Execution Phase in Mobile Networks
  • Abstract The Vertical Handover (VHO) is one of the most vital features provided for the heterogeneous mobile networks. It allows Mobile Users (MUs) to keep ongoing sessions without disruption while they continuously move between different Radio Access Technologies (RATs) such as Wireless Fidelity (Wi-Fi), Global System for Mobile Communication (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE) and Fifth Generation (5G). In order to fulfill this goal, the VHO must comply to three main phases: starting of collecting the required information and then passing it for decision phase to obtain the best available RAT for performing VHO by execution… More
  •   Views:329       Downloads:242        Download PDF
  • Optimized Generative Adversarial Networks for Adversarial Sample Generation
  • Abstract Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP)… More
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  • Early Rehabilitation After Craniosynostosis Surgery
  • Abstract Craniosynostosis is a common congenital craniofacial deformity caused by premature ossification and closure of one or more cranial sutures. Craniosynostosis will not only affect the normal development of the skull, but also may cause a variety of complications, damage the nervous system, and cause long-term effects on the development of physical and mental health. Therefore, it is particularly important to provide new ideas for clinical treatment by studying the rehabilitation methods of craniosynostosis, and to improve the cure rate. To this end, this paper studies the early rehabilitation methods after craniosynostosis surgery and designs a comprehensive early rehabilitation process and… More
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  • Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing
  • Abstract In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities. The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages. Automatic lexical task participation might be elaborated to various applications in the NLP. It is apparent from the availability of effective machine recognition models and open access handwritten databases. Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left. Arabic handwritten Character Recognition (HCR) is… More
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  • Cost and Efficiency Analysis of Steganography in the IEEE 802.11ah IoT Protocol
  • Abstract The widespread use of the Internet of Things (IoT) applications has enormously increased the danger level of data leakage and theft in IoT as data transmission occurs through a public channel. As a result, the security of the IoT has become a serious challenge in the field of information security. Steganography on the network is a critical tool for preventing the leakage of private information and enabling secure and encrypted communication. The primary purpose of steganography is to conceal sensitive information in any form of media such as audio, video, text, or photos, and securely transfer it through wireless networks.… More
  •   Views:347       Downloads:220        Download PDF
  • Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical
  • Abstract Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies–machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others–to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier… More
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  • An Efficient Security Solution for Industrial Internet of Things Applications
  • Abstract The Industrial Internet of Things (IIoT) has been growing for presentations in industry in recent years. Security for the IIoT has unavoidably become a problem in terms of creating safe applications. Due to continual needs for new functionality, such as foresight, the number of linked devices in the industrial environment increases. Certification of fewer signatories gives strong authentication solutions and prevents trustworthy third parties from being publicly certified among available encryption instruments. Hence this blockchain-based endpoint protection platform (BCEPP) has been proposed to validate the network policies and reduce overall latency in isolation or hold endpoints. A resolver supports the… More
  •   Views:345       Downloads:226        Download PDF
  • Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications
  • Abstract This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in… More
  •   Views:430       Downloads:211        Download PDF
  • Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems
  • Abstract The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV)… More
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  • LAME: Layout-Aware Metadata Extraction Approach for Research Articles
  • Abstract The volume of academic literature, such as academic conference papers and journals, has increased rapidly worldwide, and research on metadata extraction is ongoing. However, high-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers. To accommodate the diversity of the layouts of academic journals, we propose a novel LAyout-aware Metadata Extraction (LAME) framework equipped with the three characteristics (e.g., design of automatic layout analysis, construction of a large meta-data training set, and implementation of metadata extractor). In the framework, we designed an automatic layout analysis using PDFMiner. Based on the layout analysis, a large volume… More
  •   Views:408       Downloads:225        Download PDF
  • Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment
  • Abstract Forested areas are extremely vulnerable to disasters leading to environmental destruction. Forest Fire is one among them which requires immediate attention. There are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire monitoring. So, towards monitoring the forest fire and managing the energy efficiently in IoT, Energy Efficient Routing Protocol for Low power lossy networks (E-RPL) was developed. There were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with Data Compression (CAA-ERPL). Though… More
  •   Views:333       Downloads:252        Download PDF
  • Optimization Agricultural Supply Chain: A Case Study of Fertilizer Supplier Selection
  • Abstract The 21st century is associated with the Industrial Revolution 4.0 and the organic agriculture trend, making the utilization of high-quality fertilizers, abundant nutritional content, economical, and no affect to environment pollution. According to the new concept, clean agricultural production and organic agricultural products are not allowed to excessively use synthetic chemicals such as chemical fertilizers, and plant protection drugs, but priority is to use manure, organic fertilizers, and natural mineral fertilizers. Fertilizer must meet the balanced nutritional requirements of crops, maintain, and improve the fertility of the ground, protect the surrounding ecosystem, and leave harmful effects in agricultural products, products… More
  •   Views:502       Downloads:233        Download PDF
  • Policy-Based Group Signature Scheme from Lattice
  • Abstract Although the existing group signature schemes from lattice have been optimized for efficiency, the signing abilities of each member in the group are relatively single. It may not be suitable for complex applications. Inspired by the pioneering work of Bellare and Fuchsbauer, we present a primitive called policy-based group signature. In policy-based group signatures, group members can on behalf of the group to sign documents that meet their own policies, and the generated signatures will not leak the identity and policies of the signer. Moreover, the group administrator is allowed to reveal the identity of signer when a controversy occurs.… More
  •   Views:428       Downloads:319        Download PDF
  • Resistance to Malicious Packet Droppers Through Enhanced AODV in a MANET
  • Abstract Packet dropping in a mobile ad hoc network can manifest itself as the data plane attacks as well as control plane attacks. The former deal with malicious nodes performing packet drop on the data packets following the route formation and the latter deal with those malicious nodes which either drop or manipulate the control packets to degrade the network performance. The idea of the proposed approach is that during the route establishment, each of the on-path nodes is provided with pre-computed hash values which have to be used to provide a unique acknowledgement value to the upstream neighbor which acts… More
  •   Views:300       Downloads:219        Download PDF
  • Two-Stage PLS-SEM and Fuzzy AHP Approach to Investigate Vietnamese SMEs’ Export Competitiveness
  • Abstract Vietnam is paying great to the seafood exporting sector, offering various significant production advantages, concluding that it is critical to understand the competitiveness of the target market and implement effective strategies. However, due to COVID 19, the value of Vietnamese pangasius exports resulted in low and unpredictable profits for pangasius farmers. It is obvious to recognize competitiveness as Multi-Criteria Decision Making (MCDM) problem in the uncertain business environment. Therefore, this study is the first to propose a two-staged Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Fuzzy Analytic Hierarchy Process (FAHP) analysis to identify potential criteria and comprehensively investigate the competitiveness… More
  •   Views:378       Downloads:232        Download PDF
  • An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS
  • Abstract Many businesses have experienced difficulties in selecting a cloud service provider (CSP) due to the rapid advancement of cloud computing services and the proliferation of CSPs. Many independent criteria should be considered when evaluating the services provided by different CSPs. It is a case of multi-criteria decision-making (MCDM). This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method (BOM) and technique for order of preference by similarity to ideal solution (TOPSIS). To obtain the weights of criteria and the relative importance of CSPs based on each criterion,… More
  •   Views:308       Downloads:200        Download PDF
  • Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction
  • Abstract Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes namely feature selection, prediction, and… More
  •   Views:417       Downloads:288        Download PDF
  • Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block
  • Abstract At present, underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system. However, the processed underwater terrain images have inconspicuous and few feature points. In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed, we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block. First, the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information. The accelerated-KAZE (A-KAZE) algorithm is used to combine… More
  •   Views:486       Downloads:370        Download PDF
  • Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security
  • Abstract Digital image security is a fundamental and tedious process on shared communication channels. Several methods have been employed for accomplishing security on digital image transmission, such as encryption, steganography, and watermarking. Image stenography and encryption are commonly used models to achieve improved security. Besides, optimal pixel selection process (OPSP) acts as a vital role in the encryption process. With this motivation, this study designs a new competitive swarm optimization with encryption based stenographic technique for digital image security, named CSOES-DIS technique. The proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding process. In addition, the CSOES-DIS… More
  •   Views:440       Downloads:330        Download PDF
  • Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model
  • Abstract Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data… More
  •   Views:398       Downloads:281        Download PDF
  • Deep Learning Framework for Precipitation Prediction Using Cloud Images
  • Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers… More
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