CMC-Computers, Materials & Continua

About the Journal

Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.

Indexing and Abstracting

SCI: 2020 Impact Factor 3.772; Scopus CiteScore (Impact per Publication 2020): 4.6; SNIP (Source Normalized Impact per Paper 2020): 3.089; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.

  • Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification
  • Abstract In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data… More
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  • Optimized Convolutional Neural Network Models for Skin Lesion Classification
  • Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and… More
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  • Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network
  • Abstract Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all… More
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  • Anomaly Based Camera Prioritization in Large Scale Surveillance Networks
  • Abstract Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a… More
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  • Effectiveness Assessment of the Search-Based Statistical Structural Testing
  • Abstract Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to… More
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  • Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies
  • Abstract In this study, it is proposed that the diffusion least mean square (LMS) algorithm can be improved by applying the fractional order signal processing methodologies. Application of Caputo’s fractional derivatives are considered in the optimization of cost function. It is suggested to derive a fractional order variant of the diffusion LMS algorithm. The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium. The topology of the network is selected such that a smaller number of nodes are informed. In the network, a random sleep strategy is followed… More
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  • Efficient Resource Allocation in Fog Computing Using QTCS Model
  • Abstract Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available… More
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  • New Modified Controlled Bat Algorithm for Numerical Optimization Problem
  • Abstract Bat algorithm (BA) is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems. BA leverages the echolocation feature of bats produced by imitating the bats’ searching behavior. BA faces premature convergence due to its local search capability. Instead of using the standard uniform walk, the Torus walk is viewed as a promising alternative to improve the local search capability. In this work, we proposed an improved variation of BA by applying torus walk to improve diversity and convergence. The proposed. Modern Computerized Bat Algorithm (MCBA) approach has been examined for fifteen well-known benchmark… More
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  • A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification
  • Abstract Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can… More
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  • Deterministic and Stochastic Fractional-Order Hastings-Powell Food Chain Model
  • Abstract In this paper, a deterministic and stochastic fractional-order model of the tri-trophic food chain model incorporating harvesting is proposed and analysed. The interaction between prey, middle predator and top predator population is investigated. In order to clarify the characteristics of the proposed model, the analysis of existence, uniqueness, non-negativity and boundedness of the solutions of the proposed model are examined. Some sufficient conditions that ensure the local and global stability of equilibrium points are obtained. By using stability analysis of the fractional-order system, it is proved that if the basic reproduction number , the predator free equilibrium point is globally… More
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  • Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment
  • Abstract Security is an important component in the process of developing healthcare web applications. We need to ensure security maintenance; therefore the analysis of healthcare web application's security risk is of utmost importance. Properties must be considered to minimise the security risk. Additionally, security risk management activities are revised, prepared, implemented, tracked, and regularly set up efficiently to design the security of healthcare web applications. Managing the security risk of a healthcare web application must be considered as the key component. Security is, in specific, seen as an add-on during the development process of healthcare web applications, but not as the… More
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  • Neural Network and Fuzzy Control Based 11-Level Cascaded Inverter Operation
  • Abstract This paper presents a combined control and modulation technique to enhance the power quality (PQ) and power reliability (PR) of a hybrid energy system (HES) through a single-phase 11-level cascaded H-bridge inverter (11-CHBI). The controller and inverter specifically regulate the HES and meet the load demand. To track optimum power, a Modified Perturb and Observe (MP&O) technique is used for HES. Ultra-capacitor (UCAP) based energy storage device and a novel current control strategy are proposed to provide additional active power support during both voltage sag and swell conditions. For an improved PQ and PR, a two-way current control strategy such… More
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  • Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG
  • Abstract In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D… More
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  • A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare
  • Abstract COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time… More
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  • Proxy-Based Hierarchical Distributed Mobility Management for Tactical Networks
  • Abstract An important requirement in a military domain is a highly reliable mobility management method, especially when components of the networks are moving in tactical network environments. To increase reliability, the mobility management technology of the tactical network should be able to reflect the characteristics of the tactical network, such as a limited environment, failure, and hierarchical unit structure. In this paper, we propose a proxy-based hierarchical distributed mobility management scheme, which is highly focused on tactical networks. Considering the characteristics of tactical networks, the proposed scheme is composed of the following: 1) a proxy-based method, 2) a distributed mobility management… More
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  • A New Reward System Based on Human Demonstrations for Hard Exploration Games
  • Abstract The main idea of reinforcement learning is evaluating the chosen action depending on the current reward. According to this concept, many algorithms achieved proper performance on classic Atari 2600 games. The main challenge is when the reward is sparse or missing. Such environments are complex exploration environments like Montezuma’s Revenge, Pitfall, and Private Eye games. Approaches built to deal with such challenges were very demanding. This work introduced a different reward system that enables the simple classical algorithm to learn fast and achieve high performance in hard exploration environments. Moreover, we added some simple enhancements to several hyperparameters, such as… More
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  • Process Modelling and Experimental Analysis of Optimal Specimen Selection in Organic CMCs
  • Abstract Bone grafting is a surgical restructuring procedure of replacing broken bones and reconstructing missing bone pieces so that complex bone fractures can be repaired to avoid any serious health risk as well as permanent bone disfiguration. Normally, human bones tend to regenerate and heal completely from fracture. But it needs a small scaffold to provide the necessary space to grow. Bone implants allow a broken bone to grow seamlessly. Traditionally, non-corrosive metal alloys are used for fixing broken bones. A metal plate is fastened between two ends of broken bones to join them. However, issues like high weight, high cost,… More
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  • Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem
  • Abstract The first step in the design phase of the Brushless Direct Current (BLDC) motor is the formulation of the mathematical framework and is often used due to its analytical structure. Therefore, the BLDC motor design problem is considered to be an optimization problem. In this paper, the analytical model of the BLDC motor is presented, and it is considered to be a basis for emphasizing the optimization methods. The analytical model used for the experimentation has 78 non-linear equations, two objective functions, five design variables, and six non-linear constraints, so the BLDC motor design problem is considered as highly non-linear… More
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  • Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging
  • Abstract A robust smile recognition system could be widely used for many real-world applications. Classification of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face images. In this paper, an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade classifiers. Our model takes advantage of the intrinsic association between face detection, smile, and other face features to alleviate the over-fitting issue on the limited training set and increase classification results. The features are extracted taking into account to exclude any unnecessary coefficients in the… More
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  • A Real-Time Automatic Translation of Text to Sign Language
  • Abstract Communication is a basic need of every human being; by this, they can learn, express their feelings and exchange their ideas, but deaf people cannot listen and speak. For communication, they use various hands gestures, also known as Sign Language (SL), which they learn from special schools. As normal people have not taken SL classes; therefore, they are unable to perform signs of daily routine sentences (e.g., what are the specifications of this mobile phone?). A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf people. This paper presents an architecture for… More
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  • Cross Intelligence Evaluation for Effective Emotional Intelligence Estimation
  • Abstract A famous psychologist or researcher, Daniel Goleman, gave a theory on the importance of Emotional Intelligence for the success of an individual’s life. Daniel Goleman quoted in the research that “The contribution of an individual’s Intelligence Quotient (IQ) is only 20% for their success, the remaining 80% is due to Emotional Intelligence (EQ)”. However, in the absence of a reliable technique for EQ evaluation, this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms. This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools. The proposed cross intelligence evaluation… More
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  • COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis
  • Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and… More
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  • Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications
  • Abstract Teleradiology plays a vital role in the medical field, which permits transmitting medical and imaging data over a communication network. It ensures data reliability and provides convenient communication for clinical interpretation and diagnostic purposes. The transmission of this medical data over a network raises the problems of legal, ethical issues, privacy, and copyright authenticity. The copyright protection of medical images is a significant issue in the medical field. Watermarking schemes are used to address these issues. A gray-level or binary image is used as a watermark frequently in color image watermarking schemes. In this paper, the authors propose a novel… More
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  • Efficient Energy Optimized Faithful Adder with Parallel Carry Generation
  • Abstract Approximate computing has received significant attention in the design of portable CMOS hardware for error-tolerant applications. This work proposes an approximate adder that to optimize area delay and achieve energy efficiency using Parallel Carry (PC) generation logic. For ‘n’ bits in input, the proposed algorithm use approximate addition for least n/2 significant bits and exact addition for most n/2 significant bits. A simple OR logic with no carry propagation is used to implement the approximate part. In the exact part, addition is performed using 4-bit adder blocks that implement PC at block level to reduce node capacitance in the critical… More
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  • Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics
  • Abstract Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network… More
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  • Cloud Security Service for Identifying Unauthorized User Behaviour
  • Abstract Recently, an innovative trend like cloud computing has progressed quickly in Information Technology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system’s virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments… More
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  • Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices
  • Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior… More
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  • Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy
  • Abstract People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality… More
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  • Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs
  • Abstract In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance… More
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  • Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN
  • Abstract This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and… More
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  • User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework
  • Abstract As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with… More
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  • Reconfigurable Pattern Patch Antenna for Mid-Band 5G: A Review
  • Abstract New requirements in communication technologies make it imperative to rehash conventional features such as reconfigurable antennas to adapt with the future adaptability advancements. This paper presents a comprehensive review of reconfigurable antennas, specifically in terms of radiation patterns for adaptation in the upcoming Fifth Generation (5G) New Radio frequency bands. They represent the key of antenna technology for materializing a high rate transmission, increased spectral and energy efficiency, reduced interference, and improved the beam steering and beam shaping, thereby land a great promise for planar antennas to boost the mid-band 5G. This review begins with an overview of the underlying… More
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  • SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection
  • Abstract The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security… More
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  • Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning
  • Abstract Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and… More
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  • Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s
  • Abstract In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail… More
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  • Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker
  • Abstract Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select… More
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  • Deep Rank-Based Average Pooling Network for Covid-19 Recognition
  • Abstract (Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive; community-acquired pneumonia; second pulmonary tuberculosis; and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-conv rank-based average pooling module (NRAPM) was proposed in which rank-based pooling—particularly, rank-based average pooling (RAP)—was employed to avoid overfitting. Third, a novel DRAPNet was proposed… More
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  • Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection
  • Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More
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  • Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique
  • Abstract The blockchain technology plays a significant role in the present era of information technology. In the last few years, this technology has been used effectively in several domains. It has already made significant differences in human life, as well as is intended to have noticeable impact in many other domains in the forthcoming years. The rapid growth in blockchain technology has created numerous new possibilities for use, especially for healthcare applications. The digital healthcare services require highly effective security methodologies that can integrate data security with the available management strategies. To test and understand this goal of security management in… More
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  • Handling Class Imbalance in Online Transaction Fraud Detection
  • Abstract With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an… More
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  • Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks
  • Abstract The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third… More
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  • Analysis of Distance-Based Topological Polynomials Associated with Zero-Divisor Graphs
  • Abstract Chemical compounds are modeled as graphs. The atoms of molecules represent the graph vertices while chemical bonds between the atoms express the edges. The topological indices representing the molecular graph corresponds to the different chemical properties of compounds. Let be are two positive integers, and be the zero-divisor graph of the commutative ring . In this article some direct questions have been answered that can be utilized latterly in different applications. This study starts with simple computations, leading to a quite complex ring theoretic problems to prove certain properties. The theory of finite commutative rings is useful due to its… More
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  • AMBO: All Members-Based Optimizer for Solving Optimization Problems
  • Abstract There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the… More
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  • An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks
  • Abstract With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks.… More
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  • A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals
  • Abstract This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital… More
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  • Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling
  • Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by… More
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  • A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation
  • Abstract Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the… More
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  • Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank
  • Abstract One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for… More
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  • Torsional Wave in a Dissipative Cylindrical Shell Under Initial Stresses
  • Abstract The dispersion relation of torsional wave in a dissipative, incompressible cylindrical shell of infinite length incorporating initial stresses effects is investigated. The governing equation and closed form solutions are derived with the aid of Biot's principle. Phase velocity and damping of torsional wave are obtained analytically and the influences of dissipation and initial stresses are studied in details. We proposed a new method for obtaining the phase and damping velocities of torsional wave in a complex form. Numerical results analyzing the torsional wave propagation incorporating initial stress effects are analyzed and presented in graphs. The analytical and numerical solutions reveal… More
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  • Flow-Shop Scheduling with Transportation Capacity and Time Consideration
  • Abstract Planning and scheduling is one of the most important activity in supply chain operation management. Over the years, there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains. This includes two commonly used methods which are mathematical programming models and heuristics algorithms. Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain. A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between two… More
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  • Modified Differential Box Counting in Breast Masses for Bioinformatics Applications
  • Abstract Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the… More
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  • An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications
  • Abstract In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key steps for generating the reference… More
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  • VISPNN: VGG-Inspired Stochastic Pooling Neural Network
  • Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More
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  • Unified Detection of Obfuscated and Native Android Malware
  • Abstract The Android operating system has become a leading smartphone platform for mobile and other smart devices, which in turn has led to a diversity of malware applications. The amount of research on Android malware detection has increased significantly in recent years and many detection systems have been proposed. Despite these efforts, however, most systems can be thwarted by sophisticated Android malware adopting obfuscation or native code to avoid discovery by anti-virus tools. In this paper, we propose a new static analysis technique to address the problems of obfuscating and native malware applications. The proposed system provides a unified technique for… More
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  • DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code
  • Abstract Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language… More
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  • A Fractional Fourier Based Medical Image Authentication Approach
  • Abstract Patient medical information in all forms is crucial to keep private and secure, particularly when medical data communication occurs through insecure channels. Therefore, there is a bad need for protecting and securing the color medical images against impostors and invaders. In this paper, an optical medical image security approach is introduced. It is based on the optical bit-plane Jigsaw Transform (JT) and Fractional Fourier Transform (FFT). Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach. A preceding bit-plane scrambling process is conducted on the input color medical images prior to the… More
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  • Generating Synthetic Data to Reduce Prediction Error of Energy Consumption
  • Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the… More
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  • Methods for the Efficient Energy Management in a Smart Mini Greenhouse
  • Abstract To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption. One of the possible ways is proposed in this research. A multi-level hierarchical system for energy efficiency management of the enterprise is designed, it is based on the modular principle providing rapid modernization. The novelty of the work is the development of new and improvement of the existing methods and models, in particular: 1) models for dynamic analysis of IT tools for data acquisition and processing (DAAP) in multilevel energy management systems, which are based on Petri nets; 2) method of synthesis of… More
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  • Data Analytics for the Identification of Fake Reviews Using Supervised Learning
  • Abstract Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper… More
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  • Database Recovery Technique for Mobile Computing: A Game Theory Approach
  • Abstract Contact between mobile hosts and database servers presents many problems in the Mobile Database System (MDS). It is harmed by a variety of causes, including handoff, inadequate capacity, frequent transaction updates, and repeated failures, both of which contribute to serious issues with the information system’s consistency. However, error tolerance technicality allows devices to continue performing their functions in the event of a failure. The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory. Several of the presented recovery protocols are chosen and evaluated in order to… More
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  • Smart Devices Based Multisensory Approach for Complex Human Activity Recognition
  • Abstract Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex… More
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  • A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification
  • Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two… More
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  • Multi-Scale Network for Thoracic Organs Segmentation
  • Abstract Medical Imaging Segmentation is an essential technique for modern medical applications. It is the foundation of many aspects of clinical diagnosis, oncology, and computer-integrated surgical intervention. Although significant successes have been achieved in the segmentation of medical images, DL (deep learning) approaches. Manual delineation of OARs (organs at risk) is vastly dominant but it is prone to errors given the complex irregularities in shape, low texture diversity between tissues and adjacent blood area, patient-wide location of organisms, and weak soft tissue contrast across adjacent organs in CT images. Till now several models have been implemented on multi organs segmentation but… More
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  • Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images
  • Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the… More
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  • Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection
  • Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3… More
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  • Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model
  • Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More
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  • An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity
  • Abstract Self-driving and semi-self-driving cars play an important role in our daily lives. The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information. However, external infrastructures also play significant roles in the transmission and reception of control data, cooperative awareness messages, and caution notifications. In this case, roadside units are considered one of the most important communication peripherals. Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost, bandwidth, connectivity, and radio coverage area. In this paper, a new distributed roadside unit is proposed… More
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  • A Compact Tri-Band Antenna Based on Inverted-L Stubs for Smart Devices
  • Abstract We designed and constructed a novel, compact tri-band monopole antenna for intelligent devices. Multiband behavior was achieved by placing inverted-L shaped stubs of various lengths in a triangular monopole antenna fed by a coplanar waveguide. The resonance frequency of each band can be controlled by varying the length of the corresponding stub. Three bands, at 2.4 (2.37–2.51), 3.5 (3.34–3.71), and 5.5 (4.6–6.4) GHz, were easily obtained using three stubs of different lengths. For miniaturization, a portion of the longest stub (at 2.4 GHz) was printed on the opposite side of the substrate, and connected to the main stub via a… More
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  • AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles
  • Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More
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  • Allocation and Migration of Virtual Machines Using Machine Learning
  • Abstract Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet… More
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  • Magneto-Thermoelasticity with Thermal Shock Considering Two Temperatures and LS Model
  • Abstract The present investigation is intended to demonstrate the magnetic field, relaxation time, hydrostatic initial stress, and two temperature on the thermal shock problem. The governing equations are formulated in the context of Lord-Shulman theory with the presence of bodily force, two temperatures, thermal shock, and hydrostatic initial stress. We obtained the exact solution using the normal mode technique with appropriate boundary conditions. The field quantities are calculated analytically and displayed graphically under thermal shock problem with effect of external parameters respect to space coordinates. The results obtained are agreeing with the previous results obtained by others when the new parameters… More
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  • Identification of Composite-Metal Bolted Structures with Nonlinear Contact Effect
  • Abstract The middle layer model has been used in recent years to better describe the connection behavior in composite structures. The influencing parameters including low pre-screw and high preload have the main effects on nonlinear behavior of the connection as well as the amplitude of the excitation force applied to the structure. Therefore, in this study, the effects of connection behavior on the general structure in two sections of increasing damping and reducing the stiffness of the structures that lead to non-linear phenomena have been investigated. Due to the fact that in composite structure we are faced to the limitation of… More
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  • Data Fusion-Based Machine Learning Architecture for Intrusion Detection
  • Abstract In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices’ connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion… More
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  • Treatment of Polio Delayed Epidemic Model via Computer Simulations
  • Abstract Through the study, the nonlinear delayed modelling has vital significance in the different field of allied sciences like computational biology, computational chemistry, computational physics, computational economics and many more. Polio is a contagious viral illness that in its most severe form causes nerve injury leading to paralysis, difficulty breathing and sometimes death. In recent years, developing regions like Asia, Africa and sub-continents facing a dreadful situation of poliovirus. That is the reason we focus on the treatment of the polio epidemic model with different delay strategies in this article. Polio delayed epidemic model is categorized into four compartments like susceptible,… More
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  • A Study of Cellular Neural Networks with Vertex-Edge Topological Descriptors
  • Abstract The Cellular Neural Network (CNN) has various parallel processing applications, image processing, non-linear processing, geometric maps, high-speed computations. It is an analog paradigm, consists of an array of cells that are interconnected locally. Cells can be arranged in different configurations. Each cell has an input, a state, and an output. The cellular neural network allows cells to communicate with the neighbor cells only. It can be represented graphically; cells will represent by vertices and their interconnections will represent by edges. In chemical graph theory, topological descriptors are used to study graph structure and their biological activities. It is a single… More
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  • An Optimal DF Based Method for Transient Stability Analysis
  • Abstract The effect of energy on the natural environment has become increasingly severe as human consumption of fossil energy has increased. The capacity of the synchronous generators to keep working without losing synchronization when the system is exposed to severe faults such as short circuits is referred to as the power system's transient stability. As the power system's safe and stable operation and mechanism of action become more complicated, higher demands for accurate and rapid power system transient stability analysis are made. Current methods for analyzing transient stability are less accurate because they do not account for misclassification of unstable samples.… More
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  • Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods
  • Abstract Price prediction of goods is a vital point of research due to how common e-commerce platforms are. There are several efforts conducted to forecast the price of items using classic machine learning algorithms and statistical models. These models can predict prices of various financial instruments, e.g., gold, oil, cryptocurrencies, stocks, and second-hand items. Despite these efforts, the literature has no model for predicting the prices of seasonal goods (e.g., Christmas gifts). In this context, we framed the task of seasonal goods price prediction as a regression problem. First, we utilized a real online trailer dataset of Christmas gifts and then… More
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  • Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects
  • Abstract Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve… More
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  • Applying Apache Spark on Streaming Big Data for Health Status Prediction
  • Abstract Big data applications in healthcare have provided a variety of solutions to reduce costs, errors, and waste. This work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health issues. In the proposed scalable system, medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning algorithm. In this way, healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare providers. The proposed work also aims to provide an effective recommendation system by using streaming medical data, historical… More
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  • Utilizing the Improved QPSO Algorithm to Build a WSN Monitoring System
  • Abstract This research uses the improved Quantum Particle Swarm Optimization (QPSO) algorithm to build an Internet of Things (IoT) life comfort monitoring system based on wireless sensing networks. The purpose is to improve the quality of intelligent life. The functions of the system include automatic basketball court lighting system, monitoring of infants’ sleeping posture and accidental falls of the elderly, human thermal comfort measurement and other related life comfort services, etc. On the hardware system of the IoT, this research is based on the latest version of ZigBee 3.0, which uses optical sensors, 3-axis accelerometers, and temperature/humidity sensors in the IoT… More
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  • Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization
  • Abstract Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast… More
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  • Efficient Autonomous Defense System Using Machine Learning on Edge Device
  • Abstract As a large amount of data needs to be processed and speed needs to be improved, edge computing with ultra-low latency and ultra-connectivity is emerging as a new paradigm. These changes can lead to new cyber risks, and should therefore be considered for a security threat model. To this end, we constructed an edge system to study security in two directions, hardware and software. First, on the hardware side, we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array (FPGA) which is suitable for edge computing and identifying communication status to… More
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  • Epilepsy Radiology Reports Classification Using Deep Learning Networks
  • Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More
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  • Reversible Logic Based MOS Current Mode Logic Implementation in Digital Circuits
  • Abstract Now a days, MOS Current Mode Logic (MCML) has emerged as a better alternative to Complementary Metal Oxide Semiconductor (CMOS) logic in digital circuits. Recent works have only traditional logic gates that have issues with information loss. Reversible logic is incorporated with MOS Current Mode Logic (MCML) in this proposed work to solve this problem, which is used for multiplier design, D Flip-Flop (DFF) and register. The minimization of power and area is the main aim of the work. In reversible logic, the count of outputs and inputs is retained as the same value for creating one-to-one mapping. A unique… More
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  • Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System
  • Abstract This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal… More
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  • An IoT Based Secure Patient Health Monitoring System
  • Abstract Internet of things (IoT) field has emerged due to the rapid growth of artificial intelligence and communication technologies. The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients, proper administration of patient information, and healthcare management. However, the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintained while transferring over an insecure network or storing at the administrator end. In this manuscript, the authors have developed a secure IoT healthcare monitoring system using the Blockchain-based XOR… More
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  • Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS
  • Abstract Most interesting area is the growing demand of flying-IoT mergers with smart cities. However, aerial vehicles, especially unmanned aerial vehicles (UAVs), have limited capabilities for maintaining node energy efficiency. In order to communicate effectively, IoT is a key element for smart cities. While improving network performance, routing protocols can be deployed in flying-IoT to improve latency, packet drop rate, packet delivery, power utilization, and average-end-to-end delay. Furthermore, in literature, proposed techniques are very much complex which cannot be easily implemented in real-world applications. This issue leads to the development of lightweight energy-efficient routing in flying-IoT networks. This paper addresses the… More
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  • An Efficient AES 32-Bit Architecture Resistant to Fault Attacks
  • Abstract The Advanced Encryption Standard cryptographic algorithm, named AES, is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information exchange. One of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret data. Until now, a several AES fault detection schemes against fault injection attacks have been proposed. In this paper, so as to ensure a high level of security against fault injection attacks, a new efficient fault detection scheme based on the AES architecture modification has been proposed. For this reason, the AES… More
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  • Cross-Layer Hidden Markov Analysis for Intrusion Detection
  • Abstract Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion… More
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  • IoT Devices Authentication Using Artificial Neural Network
  • Abstract User authentication is one of the critical concerns of information security. Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile phones. Textual passwords are slightly unprotected and are easily attackable. The attacks include dictionary, shoulder surfing, and brute force. Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use. This paper proposes a Process-based Pattern Authentication (PPA) system for Internet of Things (IoT) devices that does not require a server to maintain… More
  •   Views:35       Downloads:25        Download PDF
  • A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning
  • Abstract The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron… More
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  • Graphical Transformation of OWL Ontologies to Event-B Formal Models
  • Abstract Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, inconsistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal… More
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  • A Joint Resource Allocation Algorithm for D2D Communication
  • Abstract The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates. The result is that, cellular networks must technically evolve to support such higher rates, to be equipped with greater capacity, and to increase the spectral and energy efficiency. Compared with 4G technology, the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer. Therefore, this new generation of networks has adopted a heterogeneous and ultra-dense architecture, where different technological advances… More
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  • Fault Management Cyber-Physical Systems in Virtual Storage Model
  • Abstract On average, every two years, the amount of data existing globally doubles. Software development will be affected and improved by Cyber-Physical Systems (CPS). The number of problems remained even though developments helped Information Technology experts extract better value from their storage investments. Because of poor interoperability between different vendors and devices, countless numbers of Storage Area Networks were created. Network setup used for data storage includes a complex and rigid arrangement of routers, switch, hosts/servers, storage arrays. We have evaluated the performance of routing protocol Transmission Control Protocol (TCP) and Fibre Channel Protocol (FCP) under different network scenario by Network… More
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  • A Machine Learning Approach for Early COVID-19 Symptoms Identification
  • Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More
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  • Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm
  • Abstract Cognitive Radio (CR) has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency. To improve the overall performance of the CR system it is extremely important to adapt or reconfigure the system parameters. The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation. As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches, the performance of these algorithms is investigated in order to design an efficient CR system that is able… More
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  • Indoor Air Quality Control Using Backpropagated Neural Networks
  • Abstract Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying… More
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  • Sales Prediction and Product Recommendation Model Through User Behavior Analytics
  • Abstract The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for… More
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  • Energy and Bandwidth Based Link Stability Routing Algorithm for IoT
  • Abstract Internet of Things (IoT) is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links. Particularly, some of the nodes in IoT are mobile and dynamic in nature. Hence maintaining the link among the nodes, efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing. In such networks, many routing-problems arise due to difficulties in energy and bandwidth based quality of service. Due to the mobility and finite nature of the… More
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  • Bayesian Group Chain Sampling Plan for Poisson Distribution with Gamma Prior
  • Abstract Acceptance sampling is a statistical quality control technique that consists of procedures for sentencing one or more incoming lots of finished products. Acceptance or rejection is based on the inspection of sampled products drawn randomly from the lot. The theory of previous acceptance sampling was built upon the assumption that the process from which the lots are produced is stable and the process fraction nonconforming is a constant. Process variability is inevitable due to random fluctuations, which may inadvertently lead to quality variation. As an alternative to traditional sampling plans, Bayesian approach can be used by considering prior information of… More
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  • Fast Intra Mode Selection in HEVC Using Statistical Model
  • Abstract Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost… More
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  • HARTIV: Human Activity Recognition Using Temporal Information in Videos
  • Abstract Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data. The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos. Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology. In a long video, human activity may be present anywhere in the video. There can be a single or multiple human activities present… More
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  • Image Segmentation Based on Block Level and Hybrid Directional Local Extrema
  • Abstract In the recent decade, the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities. Image segmentation is a key step in digitalization. Segmentation plays a key role in almost all areas of image processing, and various approaches have been proposed for image segmentation. In this paper, a novel approach is proposed for image segmentation using a nonuniform adaptive strategy. Region-based image segmentation along with a directional binary pattern generated a better segmented image. An adaptive mask of 8 × 8 was circulated over the pixels whose bit value was 1 in the… More
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  • Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization
  • Abstract The overall healthcare system has been prioritized within development top lists worldwide. Since many national populations are aging, combined with the availability of sophisticated medical treatments, healthcare expenditures are rapidly growing. Blood banks are a major component of any healthcare system, which store and provide the blood products needed for organ transplants, emergency medical treatments, and routine surgeries. Timely delivery of blood products is vital, especially in emergency settings. Hence, blood delivery process parameters such as safety and speed have received attention in the literature, as well as other parameters such as delivery cost. In this paper, delivery time and… More
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  • An Ensemble Methods for Medical Insurance Costs Prediction Task
  • Abstract The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’ health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used… More
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  • Enhancing Cloud Performance Using File Format Classifications
  • Abstract Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM).… More
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  • Improved KNN Imputation for Missing Values in Gene Expression Data
  • Abstract The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is… More
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  • Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired
  • Abstract The present work presents a statistical method to translate human voices across age groups, based on commonalities in voices of blood relations. The age-translated voices have been naturalized extracting the blood relation features e.g., pitch, duration, energy, using Mel Frequency Cepstrum Coefficients (MFCC), for social compatibility of the voice-impaired. The system has been demonstrated using standard English and an Indian language. The voice samples for resynthesis were derived from 12 families, with member ages ranging from 8–80 years. The voice-age translation, performed using the Pitch synchronous overlap and add (PSOLA) approach, by modulation of extracted voice features, was validated by… More
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  • Efficient Model for Emergency Departments: Real Case Study
  • Abstract There are several challenges that hospitals are facing according to the emergency department (ED). The main two issues are department capacity and lead time. However, the lack of consensus on performance criteria to evaluate ED increases the complication of this process. Thus, this study aims to evaluate the efficiency of the emergency department in 20 Egyptian hospitals (12 private and 8 general hospitals) based on 13 performance metrics. This research suggests an integrated evaluation model assess ED under a framework of plithogenic theory. The proposed framework addressed uncertainty and ambiguity in information with an efficient manner via presenting the evaluation… More
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  • MNN-XSS: Modular Neural Network Based Approach for XSS Attack Detection
  • Abstract The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing. A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic. In recent years, researchers have used different machine learning methods to detect network attacks without relying on those signatures. The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product. In this study, we propose and implement a new method that relies on a modular deep neural network for reducing the… More
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  • Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images
  • Abstract The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation… More
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  • Optimal Routing Protocol for Wireless Sensor Network Using Genetic Fuzzy Logic System
  • Abstract The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data… More
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  • Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing
  • Abstract Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images)… More
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  • A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks
  • Abstract Recently, Opportunistic Networks (OppNets) are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent devices. OppNets are characterized by a rough and dynamic topology as well as unpredictable contacts and contact times. Data is forwarded and stored in intermediate nodes until the next opportunity occurs. Therefore, achieving a high delivery ratio in OppNets is a challenging issue. It is imperative that any routing protocol use network resources, as far as they are available, in order to achieve higher network performance. In this article, we introduce the… More
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  • Designing Bayesian New Group Chain Sampling Plan For Quality Regions
  • Abstract Acceptance sampling is a well-established statistical technique in quality assurance. Acceptance sampling is used to decide, acceptance or rejection of a lot based on the inspection of its random sample. Experts concur that the Bayesian approach is the best approach to make a correct decision, when historical knowledge is available. This paper suggests a Bayesian new group chain sampling plan (BNGChSP) to estimate average probability of acceptance. Binomial distribution function is used to differentiate between defective and non-defective products. Beta distribution is considered as a suitable prior distribution. Derivation is completed for the estimation of the average proportion of defectives.… More
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  • Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics
  • Abstract In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing… More
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