Vol.30, No.3, 2021-Table of Contents
  • Performance Comparison of PoseNet Models on an AIoT Edge Device
  • Abstract In this paper, we present an oneM2M-compliant system including an artificial intelligence of things (AIoT) edge device whose principal function is to estimate human poses by using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. Although MobileNet v1 is generally known to be much faster but less accurate than ResNet50, it is necessary to analyze the performances of whole PoseNet models carefully and select one of them suitable for the AIoT edge device. For this reason, we first investigate the computational complexity of the models about their neural network layers and parameters and then compare their performances… More
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  • Early Detection of Lung Carcinoma Using Machine Learning
  • Abstract Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through preprocessing strategies, ordinal to cardinal… More
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  • Machine Learning-based Detection and Classification of Walnut Fungi Diseases
  • Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization.… More
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  • Radio Labeling Associated with a Class of Commutative Rings Using Zero-Divisor Graph
  • Abstract Graph labeling is useful in networks because each transmitter has a different transmission capacity to send or receive wired or wireless links. An interference of signals can occur when transmitters that are close together receive close frequencies. This problem has been modeled mathematically in the radio labeling problem on graphs, where vertices represent transmitters and edges indicate closeness of the transmitters. For this purpose, each vertex is labeled with a unique positive integer, and to minimize the interference, the difference between maximum and minimum used labels has to be minimized. A radio labeling for a graph is a function from… More
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  • Association between Temperature and Relative Humidity in Relation to COVID-19
  • Abstract The aim of this study is to determine the association between temperature and humidity in relation to COVID-19 above 3°C. This was carried out in the cities of Bandung and Surabaya which have temperatures of about 22°C to 31°C. Data was analyzed using descriptive analysis and the Pearson and Spearman correlation for normally and abnormally distributed data. The results showed that there was no association between people under monitoring (ODP)/close contact, patients under surveillance (PDP)/suspect, and COVID-19 confirmed cases in relation to the temperature and humidity in Bandung and Surabaya. Furthermore, there was no relationship between temperature and humidity with… More
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  • Sentiment Analytics: Extraction of Challenging Influencing Factors from COVID-19 Pandemics
  • Abstract The advancement in electronic devices and communication technologies in social media have introduced major changes in today’s communication and people have accepted such communicational habits at a rapid pace. The changes involve the way people started interacting with each other, and modern mean of discovering new groups of people, and individuals with similar mindsets, mutual interests, and ideas to share with. As far as the communities are concerned, there are so many social drives (such as “Say No to Plastic”) that need to be discussed on a certain platform for their promotion. Although, it’s quit is challenging, but with the… More
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  • Robust Optimal Proportional–Integral Controller for an Uncertain Unstable Delay System: Wind Process Application
  • Abstract In industrial practice, certain processes are unstable, such as different types of reactors, distillation columns, and combustion systems. To ensure greater maneuverability and improve the speed of response command, certain systems in the military and aviation fields are purposely configured to be unstable. These systems are often more difficult to control than stable systems and are of particular interest to designers and control engineers. Despite all advances in process control over the past six decades, the proportional–integral–derivative (PID) controller is still the most common. The main reasons are the simplicity, robustness, and successful applications provided by PID-based control structures. The… More
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  • A Two-Step Approach for Improving Sentiment Classification Accuracy
  • Abstract Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is… More
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  • Image Denoising Using a Nonlinear Pixel-Likeness Weighted-Frame Technique
  • Abstract Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hardware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a nonlinear modified pixel-likeness weighted-frame technique. The proposed architecture works in two stages.… More
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  • Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm
  • Abstract The real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appropriate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent… More
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  • Adversarial Examples Generation Algorithm through DCGAN
  • Abstract In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM and establish a generation model,… More
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  • Development of a Web-Based Telemedicine System for Covid-19 Patients
  • Abstract In the Covid-19 pandemic, people have been very concerned about the safety and are avoiding crowded places like hospitals. An online telemedicine web-based technology can help to overcome this situation. This paper presents an online telemedicine system that helps to promote collaboration between doctors, hospitals, and patients. The system allows doctors to serve patients from remote areas. The system also allows both doctors and patients to communicate through video calls or text messages. Patients using the system can store information about their health, search for doctors, and consult medical professionals using text messages and video calls. Doctors can also register… More
  •   Views:1530       Downloads:673       Cited by:1        Download PDF
  • A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease
  • Abstract The liver is considered an essential organ in the human body. Liver disorders have risen globally at an unprecedented pace due to unhealthy lifestyles and excessive alcohol consumption. Chronic liver disease is one of the principal causes of death affecting large portions of the global population. An accumulation of liver-damaging factors deteriorates this condition. Obesity, an undiagnosed hepatitis infection, alcohol abuse, coughing or vomiting blood, kidney or hepatic failure, jaundice, liver encephalopathy, and many more disorders are responsible for it. Thus, immediate intervention is needed to diagnose the ailment before it is too late. Therefore, this work aims to evaluate… More
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  • Predicting Heart Disease Based on Influential Features with Machine Learning
  • Abstract Heart disease is a major health concern worldwide. The chances of recovery are bright if it is detected at an early stage. The present report discusses a comparative approach to the classification of heart disease data using machine learning (ML) algorithms and linear regression and classification methods, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), SVM with grid search (SVMG), k-nearest neighbor (KNN), and naive Bayes (NB). The ANOVA F-test feature selection (AFS) method was used to select influential features. For experimentation, two standard benchmark datasets of heart diseases, Cleveland and Statlog, were obtained… More
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  • Fault-Tolerant Communication Induced Checkpointing and Recovery Protocol Using IoT
  • Abstract In mobile computing systems, nodes in the network take checkpoints to survive failures. Certain characteristics of mobile computing systems such as mobility, low bandwidth, disconnection, low power consumption, and limited memory make these systems more prone to failures. In this paper, a novel minimum process communication-induced checkpointing algorithm that makes full use of the computation ability and implementation of effective stable storage in a mobile computing system is proposed. The said approach initiates by taking spontaneous checkpoints by each node in phase 1 using a logistic function that is specifically used to estimate the time interval between two checkpoints and… More
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  • Decision Making Based on Fuzzy Soft Sets and Its Application in COVID-19
  • Abstract Real-world applications are now dealing with a huge amount of data, especially in the area of high-dimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union - Intersection decision making method. Using these new principles, the decision-making strategy… More
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  • Face Image Compression and Reconstruction Based on Improved PCA
  • Abstract Face recognition technology has many usages in the real-world applications, and it has generated extensive interest in recent years. However, the amount of data in a digital image is growing explosively, taking up a lot of storage and transmission resources. There is a lot of redundancy in an image data representation. Thus, image compression has become a hot topic. The principal component analysis (PCA) can effectively remove the correlation of an image and condense the image information into a characteristic image with several main components. At the same time, it can restore different data images according to their principal components… More
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  • Cryptanalysis of an Online/Offline Certificateless Signature Scheme for Internet of Health Things
  • Abstract Recently, Khan et al. [An online-offline certificateless signature scheme for internet of health things,” Journal of Healthcare Engineering, vol. 2020] presented a new certificateless offline/online signature scheme for Internet of Health Things (IoHT) to fulfill the authenticity requirements of the resource-constrained environment of (IoHT) devices. The authors claimed that the newly proposed scheme is formally secured against Type-I adversary under the Random Oracle Model (ROM). Unfortunately, their scheme is insecure against adaptive chosen message attacks. It is demonstrated that an adversary can forge a valid signature on a message by replacing the public key. Furthermore, we performed a comparative analysis… More
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  • Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications
  • Abstract The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general image datasets from ImageNet. In… More
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  • Control Charts for the Shape Parameter of Skewed Distribution
  • Abstract The weighted distributions are useful when the sampling is done using an unequal probability of the sampling units. The Weighted Power function distribution (WPFD) has applications in the fields of reliability engineering, management sciences and survival analysis. WPFD is more beneficial in Statistical process control (SPC). SPC is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help to monitor process behaviour, discover problems in internal systems, and find solutions for production issues. To identify and remove the variation in different reliability processes and also to monitor the reliability of… More
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  • Compression of Grayscale Images in DRPE-based Encrypted Domain
  • Abstract Compressing the encrypted images is considered an important issue in many applications such as cloud computing. From this perspective, this paper introduces an efficient approach for compression processing of images in the encrypted domain. The images are optically encrypted using the Double Random Phase Encoding (DRPE). The Joint Photographic Experts Group (JPEG) and the Set Partitioning in Hierarchical Trees (SPIHT) compression schemes have been used to compress the encrypted images. The process starts by converting the original image into an optical signal by an optical emitter like an optical source and encrypting it with DRPE. The DRPE applies two-phase modulations… More
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  • Lowest-Opportunities User First-Based Subcarrier Allocation Algorithm for Downlink NOMA Systems
  • Abstract Non-orthogonal multiple access (NOMA) is one of the promising 5G technologies to improve spectral efficiency massive connectivity and cell-edge throughput. The performance of NOMA systems mainly depends on the efficiency of the subcarrier allocation algorithm. This paper aims to jointly optimize spectral efficiency (SE), outage probability, and fairness among users with respect to the subcarrier allocation for downlink NOMA systems. We propose a low-complexity greedy-based subcarrier allocation algorithm based on the lowest-opportunities user’s first precept. This precept is based on computing the number of opportunities for each user to select a subcarrier with good channel gain by counting the number… More
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  • The Data Acquisition and Control System Based on IoT-CAN Bus
  • Abstract Presently, the adoption of Internet of things(IOT)-related technologies in the Smart Farming domain is rapidly emerging. The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms, which we will need pressingly in the future. This paper designs a heterogeneous data acquisition and control system for differentiated agricultural information monitoring terminal. Based on the IoT-CAN bus architecture, the system can… More
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  • Hardware Acceleration of Image and Video Processing on Xilinx Zynq Platform
  • Abstract Advancements in image and video processing are growing over the years for industrial robots, autonomous vehicles, indexing databases, surveillance, medical imaging and computer-human interaction applications. One of the major challenges in real-time image and video processing is the execution of complex functions and high computational tasks. In this paper, the hardware acceleration of different filter algorithms for both image and video processing is implemented on Xilinx Zynq®-7000 System on-Chip (SoC) device. It consists of Dual-core Cortex-A9 processors which provide computing ability to perform I/O and processing functions and software libraries using Vivado® High-Level Synthesis (HLS). In the proposed work, Sobel-Feldman… More
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  • On Parametric Fuzzy Linear Programming Formulated by a Fractal
  • Abstract Fractal strategy is an important tool in manufacturing proposals, including computer design, conserving, power supplies and decorations. In this work, a parametric programming, analysis is proposed to mitigate an optimization problem. By employing a fractal difference equation of the spread functions (local fractional calculus operator) in linear programming, we aim to analyze the restraints and the objective function. This work proposes a new technique of fractal fuzzy linear programming (FFLP) model based on the symmetric triangular fuzzy number. The parameter fuzzy number is selected from the fractal power of the difference equation. Note that this number indicates the fractal parameter,… More
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