Vol.28, No.2, 2021-Table of Contents
  • An Enhanced Convolutional Neural Network for COVID-19 Detection
  • Abstract The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 pneumonia from healthy cases. In… More
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  • Predicting COVID-19 Based on Environmental Factors With Machine Learning
  • Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases… More
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  • Optimizing Service Composition (SC) Using Smart Multistage Forward Search (SMFS)
  • Abstract

    Service Oriented Architecture (SOA) is a style of software design where Web Services (WS) provide services to the other components through a communication protocol over a network. WS components are managed, updated, and rearranged at runtime to provide the business processes as SCs, which consist of a set of WSs that can be invoked in a specific order to fulfill the clients’ requests. According to the Service Level Agreement (SLA) requirements, WS selection and composition are significant perspectives of research to meet the clients’ expectations. This paper presents an effective technique using SMFS that attempts to improve the WS selection… More

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  • Evolution of Influential Developer’s Communities in OSS and its Impact on Quality
  • Abstract The high turnover of developers in the Open-Source Software (OSS) systems is due to the lack of restriction on a developer’s involvement and contributions. The primary developers start and administer an OSS project. However, they do not manage those who contribute. The literature shows that 80% of issues are resolved by 20% of developers when developing an OSS. Therefore, identifying influential developer communities is quite necessary for OSS stakeholders to reduce the efforts required to solve the issue through releases and predict quality. The purpose of this proposed empirical study is to explore influential communities by analyzing the relationship between… More
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  • Efficient Three-Dimensional Video Cybersecurity Framework Based on Double Random Phase Encoding
  • Abstract With the rapidly increasing rate of using online services and social media websites, cybercriminals have caused a great deterioration in the network security with enormous undesired consequences. Encryption techniques may be utilized to achieve data robustness and security in digital multimedia communication systems. From this perspective, this paper presents an optical ciphering framework using Double Random Phase Encoding (DRPE) for efficient and secure transmission of Three-Dimensional Videos (3DVs). Firstly, in the DRPE-based 3DV cybersecurity framework proposed in the paper, an optical emitter converts each frame of the transmitted 3DV into an optical signal. Then, the DRPE technique encrypts the obtained… More
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  • Short-Term Stock Price Forecasting Based on an SVD-LSTM Model
  • Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations,… More
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  • Designing an Online Appointment System for Semiliterate Users
  • Abstract Information and Communications Technology (ICT) has revolutionized the healthcare leading to provision of eHealth facilities remotely. During the peak time of COVID-19, as the long queues at health care facilities can result in spread of the virus. ICT can play an effective role especially for reducing the extended waiting time of patients to consult a medical practitioner which is considered as a source of hazard during the pandemic. However, in developing countries where majority population is semiliterate so find difficulty when come into contact with appointment systems which are not particularly designed keeping in consideration the requirements of semiliterate users.… More
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  • CAMNet: DeepGait Feature Extraction via Maximum Activated Channel Localization
  • Abstract As the models with fewer operations help realize the performance of intelligent computing systems, we propose a novel deep network for DeepGait feature extraction with less operation for video sensor-based gait representation without dimension decomposition. The DeepGait has been known to have outperformed the hand-crafted representations, such as the frequency-domain feature (FDF), gait energy image (GEI), and gait flow image (GFI), etc. More explicitly, the channel-activated mapping network (CAMNet) is composed of three progressive triplets of convolution, batch normalization, max-pooling layers, and an external max pooling to capture the Spatio-temporal information of multiple frames in one gait period. We conducted… More
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  • A Fog Covered Object Recognition Algorithm Based On Space And Frequency Network
  • Abstract It is difficult to recognize a target object from foggy images. Aiming at solving this problem, a new algorithm is thereby proposed. Fog concentration estimation is the prerequisite for image defogging. Due to the uncertainty of fog distribution, a fog concentration estimation model is accordingly proposed. Establish the brightness and gradient model in the spatial domain, and establish the FFT model in the frequency domain. Also, a multiple branch network framework is established to realize the qualitative estimation of the fog concentration in images based on comprehensive analysis from spatial and frequency domain levels. In the aspect of foggy image… More
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  • Multi-Stage Intelligent Smart Lockdown using SIR Model to Control COVID 19
  • Abstract Corona Virus (COVID-19) is a contagious disease. Unless an effective vaccine is available, various techniques such as lockdown, social distancing, or business Standard operating procedures (SOPs) must be implemented. Lockdown is an effective technique for controlling the spread of the virus, but it severely affects the economy of developing countries. No single technique for controlling a pandemic situation has ever returned a promising result; therefore, using a combination of techniques would be best for controlling COVID-19. The South asian association of regional corporation (SAARC), region contains populous and developing countries that have a unique social-cultural lifestyle that entails a higher… More
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  • Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing
  • Abstract Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then… More
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  • Solid Waste Collection System Selection Based on Sine Trigonometric Spherical Hesitant Fuzzy Aggregation Information
  • Abstract Spherical fuzzy set (SFS) as one of several non-standard fuzzy sets, it introduces a number triplet (a,b,c) that satisfies the requirement to express membership grades. Due to the expression, SFS has a more extensive description space when describing fuzzy information, which attracts more attention in scientific research and engineering practice. Just for this reason, how to describe the fuzzy information more reasonably and perfectly is the hot that scholars pay close attention to. In view of this hot, in this paper, the notion of spherical hesitant fuzzy set is introduced as a generalization of spherical fuzzy sets. Some basic operations… More
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  • Earth Fault Management for Smart Grids Interconnecting Sustainable Wind Generation
  • Abstract In this study, the active traveling-wave fault location function is incorporated into the management of earth faults for smart unearthed and compensated distribution networks associated with distributed renewable generation. Unearthed and compensated networks are implemented mainly to attain service continuity, specifically during earth faults. This advantage is valued for service continuity of grid-interconnected renewable resources. However, overcurrent-based fault indicators are not efficient in indicating the fault path in these distribution networks. Accordingly, in this study, the active traveling-wave fault location is complemented using distributed Rogowski coil-based fault passage indicators. Active traveling waves are injected by switching the neutral point of… More
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  • ECG Encryption Enhancement Technique with Multiple Layers of AES and DNA Computing
  • Abstract Over the decades, protecting the privacy of a health cloud using the design of a fog computing network is a very important field and will be more important in the near future. Current Internet of Things (IoT) research includes security and privacy due to their extreme importance in any growing technology that involves the implementation of cryptographic Internet communications (ICs) for protected IC applications such as fog computing and cloud computing devices. In addition, the implementation of public-key cryptography for IoT-based DNA sequence testing devices requires considerable expertise. Any key can be broken by using a brute-force attack with ample… More
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  • HPMC: A Multi-target Tracking Algorithm for the IoT
  • Abstract With the rapid development of the Internet of Things and advanced sensors, vision-based monitoring and forecasting applications have been widely used. In the context of the Internet of Things, visual devices can be regarded as network perception nodes that perform complex tasks, such as real-time monitoring of road traffic flow, target detection, and multi-target tracking. We propose the High-Performance detection and Multi-Correlation measurement algorithm (HPMC) to address the problem of target occlusion and perform trajectory correlation matching for multi-target tracking. The algorithm consists of three modules: 1) For the detection module, we proposed the You Only Look Once(YOLO)v3_plus model, which… More
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  • AI/ML in Security Orchestration, Automation and Response: Future Research Directions
  • Abstract Today’s cyber defense capabilities in many organizations consist of a diversity of tools, products, and solutions, which are very challenging for Security Operations Centre (SOC) teams to manage in current advanced and dynamic cyber threat environments. Security researchers and industry practitioners have proposed security orchestration, automation, and response (SOAR) solutions designed to integrate and automate the disparate security tasks, processes, and applications in response to security incidents to empower SOC teams. The next big step for cyber threat detection, mitigation, and prevention efforts is to leverage AI/ML in SOAR solutions. AI/ML will act as a force multiplier empowering SOC analysts… More
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  • Exact Analysis of Second Grade Fluid with Generalized Boundary Conditions
  • Abstract Convective flow is a self-sustained flow with the effect of the temperature gradient. The density is non-uniform due to the variation of temperature. The effect of the magnetic flux plays a major role in convective flow. The process of heat transfer is accompanied by mass transfer process; for instance condensation, evaporation and chemical process. Due to the applications of the heat and mass transfer combined effects in different field, the main aim of this paper is to do comprehensive analysis of heat and mass transfer of MHD unsteady second-grade fluid in the presence of time dependent generalized boundary conditions. The… More
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  • Tomato Leaf Disease Identification and Detection Based on Deep Convolutional Neural Network
  • Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then uses labeled data for supervised… More
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  • Secure Image Authentication Using Watermarking and Blockchain
  • Abstract Image authentication is an important field that employs many different approaches and has several significant applications. In the proposed approach, we used a combination of two techniques to achieve authentication. Image watermarking is one of the techniques that has been used in many studies but the authentication field still needs to be studied. Blockchain technology is a relatively new technology that has significant research potential related to image authentication. The watermark is embedded into the third-level discrete wavelet transform (DWT) in the middle frequency regions to achieve security and imperceptibility goals. Peak signal-to-noise ratio PSNR, structural similarity matrix (SSIM), normalized… More
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  • Internet of Things: Protection of Medical Data through Decentralized Ledgers
  • Abstract It is forecasted that billions of Internet of Things (IoT) and sensor devices will be installed worldwide by 2020. These devices can provide infrastructure-based services for various applications such as in smart hospitals, smart industry, smart grids, and smart industrial towns. Among them, the hospital service system needs to authenticate devices, and medical data are recorded for diagnostic purposes. In general, digital signatures are employed, but the computational power and their huge numbers pose many challenges to the digital signature system. To solve such problems, we developed a ledger system for authenticating IoT medical devices. It is a centralized ledger… More
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