Home / Journals / CSSE / Vol.46, No.3, 2023
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    ARTICLE

    Read-Write Dependency Aware Register Allocation

    Sheng Xiao1,*, Yong Chen2, Jing He3, Xi Yang4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3527-3540, 2023, DOI:10.32604/csse.2023.027081
    Abstract Read-write dependency is an important factor restricting software efficiency. Timing Speculative (TS) is a processing architecture aiming to improve energy efficiency of microprocessors. Timing error rate, influenced by the read-write dependency, bottlenecks the voltage down-scaling and so the energy efficiency of TS processors. We proposed a method called Read-Write Dependency Aware Register Allocation. It is based on the Read-Write Dependency aware Interference Graph (RWDIG) conception. Registers are reallocated to loosen the read-write dependencies, so resulting in a reduction of timing errors. The traditional no operation (Nop) padding method is also redesigned to increase the distance More >

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    ARTICLE

    Automated Leukemia Screening and Sub-types Classification Using Deep Learning

    Chaudhary Hassan Abbas Gondal1,*, Muhammad Irfan2, Sarmad Shafique3, Muhammad Salman Bashir4, Mansoor Ahmed1, Osama M.Alshehri5, Hassan H. Almasoudi5, Samar M. Alqhtani6, Mohammed M. Jalal7, Malik A. Altayar7, Khalaf F. Alsharif8
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3541-3558, 2023, DOI:10.32604/csse.2023.036476
    Abstract Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears… More >

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    ARTICLE

    Fuzzy Logic-Based System for Liver Fibrosis Disease

    Tamim Alkhalifah1,*, Jimmy Singla2, Fahad Alurise1
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3559-3582, 2023, DOI:10.32604/csse.2023.036534
    Abstract The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients’ health as experts can. The historical data of a patient’s health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of… More >

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    ARTICLE

    Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

    Manal Abdullah Alohali1, Muna Elsadig1, Fahd N. Al-Wesabi2,*, Mesfer Al Duhayyim3, Anwer Mustafa Hilal4, Abdelwahed Motwakel4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3583-3598, 2023, DOI:10.32604/csse.2023.037545
    Abstract With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) More >

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    ARTICLE

    A Multi-Stage Security Solution for Medical Color Images in Healthcare Applications

    Walid El-Shafai1,2,*, Fatma Khallaf2,3, El-Sayed M. El-Rabaie2, Fathi E. Abd El-Samie2, Iman Almomani1,4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3599-3618, 2023, DOI:10.32604/csse.2023.037655
    Abstract This paper presents a robust multi-stage security solution based on fusion, encryption, and watermarking processes to transmit color healthcare images, efficiently. The presented solution depends on the features of discrete cosine transform (DCT), lifting wavelet transform (LWT), and singular value decomposition (SVD). The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks. During watermark embedding, the host color medical image is transformed into four sub-bands by employing three stages of LWT. The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed… More >

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    ARTICLE

    Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

    Jianguo Zheng, Shuilin Chen*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3619-3635, 2023, DOI:10.32604/csse.2023.038025
    Abstract To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At More >

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    ARTICLE

    Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

    Samar Elbedwehy1,3,*, T. Medhat2, Taher Hamza3, Mohammed F. Alrahmawy3
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3637-3652, 2023, DOI:10.32604/csse.2023.038376
    Abstract One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the More >

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    ARTICLE

    Improved QoS-Secure Routing in MANET Using Real-Time Regional ME Feature Approximation

    Y. M. Mahaboob John1,*, G. Ravi2
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3653-3666, 2023, DOI:10.32604/csse.2023.036916
    Abstract Mobile Ad-hoc Network (MANET) routing problems are thoroughly studied several approaches are identified in support of MANET. Improve the Quality of Service (QoS) performance of MANET is achieving higher performance. To reduce this drawback, this paper proposes a new secure routing algorithm based on real-time partial ME (Mobility, energy) approximation. The routing method RRME (Real-time Regional Mobility Energy) divides the whole network into several parts, and each node’s various characteristics like mobility and energy are randomly selected neighbors accordingly. It is done in the path discovery phase, estimated to identify and remove malicious nodes. In… More >

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    ARTICLE

    BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

    Sahar Badri1, Sana Ullah Jan2,*, Daniyal Alghazzawi1, Sahar Aldhaheri1, Nikolaos Pitropakis2
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3667-3684, 2023, DOI:10.32604/csse.2023.037531
    Abstract Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospital management systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating More >

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    ARTICLE

    On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach

    Abeeda Akram1, Kashif Zafar1, Adnan Noor Mian2, Abdul Rauf Baig3, Riyad Almakki3, Lulwah AlSuwaidan3, Shakir Khan3,4,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3685-3701, 2023, DOI:10.32604/csse.2023.032024
    Abstract One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions… More >

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    ARTICLE

    Deep Learning Algorithm for Detection of Protein Remote Homology

    Fahriye Gemci1,*, Turgay Ibrikci2, Ulus Cevik3
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3703-3713, 2023, DOI:10.32604/csse.2023.032706
    Abstract The study aims to find a successful solution by using computer algorithms to detect remote homologous proteins, which is a significant problem in the bioinformatics field. In this experimental study, structural classification of proteins (SCOP) 1.53, SCOP benchmark, and the newly created SCOP protein database from the structural classification of proteins—extended (SCOPe) 2.07 were used to detect remote homolog proteins. N-gram method and then Term Frequency-Inverse Document Frequency (TF-IDF) weighting were performed to extract features of the protein sequences taken from these databases. Next, a smoothing process on the obtained features was performed to avoid… More >

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    ARTICLE

    Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

    Saeed Ali Alsareii1, Muhammad Awais2,*, Abdulrahman Manaa Alamri1, Mansour Yousef AlAsmari1, Muhammad Irfan3, Mohsin Raza2, Umer Manzoor4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3715-3728, 2023, DOI:10.32604/csse.2023.035687
    Abstract Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing… More >

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    ARTICLE

    Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

    Babangida Isyaku1,2,*, Kamalrulnizam Bin Abu Bakar1, Wamda Nagmeldin3, Abdelzahir Abdelmaboud4, Faisal Saeed5,6, Fuad A. Ghaleb1
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3729-3748, 2023, DOI:10.32604/csse.2023.034509
    Abstract Software Defined Networks (SDN) introduced better network management by decoupling control and data plane. However, communication reliability is the desired property in computer networks. The frequency of communication link failure degrades network performance, and service disruptions are likely to occur. Emerging network applications, such as delay-sensitive applications, suffer packet loss with higher Round Trip Time (RTT). Several failure recovery schemes have been proposed to address link failure recovery issues in SDN. However, these schemes have various weaknesses, which may not always guarantee service availability. Communication paths differ in their roles; some paths are critical because… More >

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    ARTICLE

    Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

    Iyad Katib1, Mahmoud Ragab2,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3749-3765, 2023, DOI:10.32604/csse.2023.036796
    Abstract The concept of smart healthcare has seen a gradual increase with the expansion of information technology. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things (IoT), cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient. This work designs a new Heap Based Optimization with Deep Quantum Neural Network (HBO-DQNN) model for decision-making in smart healthcare applications. The presented HBO-DQNN model majorly focuses on identifying and classifying healthcare data. In the presented HBO-DQNN… More >

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    ARTICLE

    Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

    Kaushlendra Yadav*, Anuj Singh, Arvind Kumar Tiwari
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3767-3782, 2023, DOI:10.32604/csse.2023.033991
    Abstract Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate. However, effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology. The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage. Currently, most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere. In this work, sensors are used to collect real-time data for a… More >

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    ARTICLE

    Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

    Mohammed Basheri, Mahmoud Ragab*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3783-3798, 2023, DOI:10.32604/csse.2023.037130
    Abstract The Internet of Things (IoT) is one of the emergent technologies with advanced developments in several applications like creating smart environments, enabling Industry 4.0, etc. As IoT devices operate via an inbuilt and limited power supply, the effective utilization of available energy plays a vital role in designing the IoT environment. At the same time, the communication of IoT devices in wireless mediums poses security as a challenging issue. Recently, intrusion detection systems (IDS) have paved the way to detect the presence of intrusions in the IoT environment. With this motivation, this article introduces a… More >

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    ARTICLE

    An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

    Ejaz Khan1, Muhammad Zia Ur Rehman2, Fawad Ahmed3, Suliman A. Alsuhibany4,*, Muhammad Zulfiqar Ali5, Jawad Ahmad6
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3799-3814, 2023, DOI:10.32604/csse.2023.037131
    Abstract In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To… More >

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    ARTICLE

    Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

    Abeera Ilyas1,*, Saeed Mahfooz1, Zahid Mehmood2,3, Gauhar Ali4, Muhammad ElAffendi4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3815-3829, 2023, DOI:10.32604/csse.2023.036316
    Abstract Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and use while increasing real-time data transmission of device gateways used for More >

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    ARTICLE

    Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

    Mohammed Jasim Mohammed Jasim1, Shakir Fattah Kak2, Zainab Salih Ageed3, Subhi R. M. Zeebaree4,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3831-3845, 2023, DOI:10.32604/csse.2023.037580
    Abstract Nowadays, smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature, computational approaches, and discoveries, owing to which a massive quantity of experimental datasets was published and generated (Big Data) for describing and validating such novelties. Drug-drug interaction (DDI) significantly contributed to drug administration and development. It continues as the main obstacle in offering inexpensive and safe healthcare. It normally happens for patients with extensive medication, leading them to take many drugs simultaneously. DDI may cause side effects, either mild or severe health problems. This reduced… More >

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    ARTICLE

    Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

    Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana murthy3, Ravuri Daniel4, E. Laxmi Lydia5, Mohamad Khairi Ishak6, Hend Khalid Alkahtani7,*, Ayman Aljarbouh8, Samih M. Mostafa9
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3847-3864, 2023, DOI:10.32604/csse.2023.036552
    Abstract Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer… More >

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    ARTICLE

    Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

    Abdulaziz Aldribi1,2,*, Aman Singh2,3, Jose Breñosa3,4
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3865-3881, 2023, DOI:10.32604/csse.2023.037748
    Abstract Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve… More >

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    ARTICLE

    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050
    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

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    ARTICLE

    Fusing Satellite Images Using ABC Optimizing Algorithm

    Nguyen Hai Minh1, Nguyen Tu Trung2,*, Tran Thi Ngan2, Tran Manh Tuan2
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311
    Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the More >

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    ARTICLE

    Detection Algorithm of Surface Defect Word on Printed Circuit Board

    Min Zhang*, Haixu Xi
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3911-3923, 2023, DOI:10.32604/csse.2023.036709
    Abstract For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial… More >

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    ARTICLE

    Battle Royale Optimization-Based Resource Scheduling Scheme for Cloud Computing Environment

    Lenin Babu Russeliah1,*, R. Adaline Suji2, D. Bright Anand3
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3925-3938, 2023, DOI:10.32604/csse.2023.034727
    Abstract Cloud computing (CC) is developing as a powerful and flexible computational structure for providing ubiquitous service to users. It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment. The variation of software and hardware resources were combined and composed as a resource pool. The software no more resided in the single hardware environment, it can be executed on the schedule of resource pools to optimize resource consumption. Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation. This… More >

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    ARTICLE

    An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

    Sareer Ul Amin1, Yongjun Kim2, Irfan Sami3, Sangoh Park1,*, Sanghyun Seo4,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805
    Abstract In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance… More >

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    ARTICLE

    FST-EfficientNetV2: Exceptional Image Classification for Remote Sensing

    Huaxiang Song*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3959-3978, 2023, DOI:10.32604/csse.2023.038429
    Abstract Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex procedures (e.g., multi-stages), excessive hardware budgets (e.g., multi-models), and an extreme reliance on domain knowledge (e.g., handcrafted features) for the pure purpose of improving accuracy. It obviously goes against the superiority of DL, i.e., simplicity and automation. Meanwhile, these algorithms come with unnecessarily expensive overhead on parameters and hardware costs. As a solution, the author proposed a fast and simple training algorithm based… More >

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    ARTICLE

    High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

    G. Venkatesan1,*, N. Ramadass2
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3979-3991, 2023, DOI:10.32604/csse.2023.031627
    Abstract Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years. Nodes must advance their energy use for expanding network lifetime. The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results. Due to different network resource constraints and malicious attacks, security assurance in wireless sensor networks has been a difficult task. The implementation of these features requires larger space due to distributed module. This research work proposes new sensor node architecture integrated with More >

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    ARTICLE

    An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

    Shahid Mohammad Ganie1, Pijush Kanti Dutta Pramanik2, Majid Bashir Malik3, Anand Nayyar4, Kyung Sup Kwak5,*
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244
    Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and… More >

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    ARTICLE

    Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

    Muhammad Tahir1,*, Farhan Riaz2, Imran Usman1,3, Mohamed Ibrahim Habib1
    Computer Systems Science and Engineering, Vol.46, No.3, pp. 4007-4022, 2023, DOI:10.32604/csse.2023.032359
    Abstract The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant… More >

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