Home / Journals / CSSE / Vol.46, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    Adversarial Examples Protect Your Privacy on Speech Enhancement System

    Mingyu Dong, Diqun Yan*, Rangding Wang
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568
    Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in… More >

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    ARTICLE

    SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

    Yudong Zhang1, Muhammad Attique Khan2, Ziquan Zhu1, Shuihua Wang1,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172
    Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due… More >

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    ARTICLE

    Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

    D. Viji*, S. Revathy
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 27-42, 2023, DOI:10.32604/csse.2023.030259
    Abstract Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this… More >

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    ARTICLE

    Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

    R. Gayathri1,*, K. Sheela Sobana Rani2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 43-56, 2023, DOI:10.32604/csse.2023.032491
    Abstract Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are… More >

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    ARTICLE

    A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

    Maha Sabir1, Jawad Ahmad2,*, Daniyal Alghazzawi1
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277
    Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based… More >

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    ARTICLE

    Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

    Mohamed Amine Ouamri1,2, Reem Alkanhel3,*, Daljeet Singh4, El-sayed M. El-kenaway5, Sherif S. M. Ghoneim6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 73-92, 2023, DOI:10.32604/csse.2023.034461
    Abstract Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive… More >

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    ARTICLE

    Ring Oscillator for 60 Meter Bandwidth

    Rachana Arya1,*, B. K. Singh2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 93-105, 2023, DOI:10.32604/csse.2023.029220
    Abstract The 60-meter band range is tremendously useful in telecommunication, military and governmental applications. The I. T. U. (International Telecommunication Union) required isolationism to former radio frequency services because the various frequency bands are extremely overloaded. The allocation of new frequency bands are a lengthy procedure as well as time taking. As a result, the researchers use bidirectional, amateur radio frequency communication for 60-meter band, usually the frequency slot of 5250–5450 KHz, although the entire band is not essentially obtainable for all countries. For transmission and reception of these frequencies, a local oscillator is used in… More >

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    ARTICLE

    Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

    Abdullah Alqahtani1, Shtwai Alsubai1, Mohemmed Sha1,*, Muhammad Attique Khan2, Majed Alhaisoni3, Syed Rameez Naqvi2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 107-123, 2023, DOI:10.32604/csse.2023.030727
    Abstract White blood cells (WBC) are immune system cells, which is why they are also known as immune cells. They protect the human body from a variety of dangerous diseases and outside invaders. The majority of WBCs come from red bone marrow, although some come from other important organs in the body. Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique. Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection. We… More >

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    ARTICLE

    The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

    S. Navaneethan1,*, P. Siva Satya Sreedhar2, S. Padmakala3, C. Senthilkumar4
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 125-135, 2023, DOI:10.32604/csse.2023.034546
    Abstract The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN… More >

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    ARTICLE

    Classification of Request-Based Mobility Load Balancing in Fog Computing

    D. Deepa, K. R. Jothi*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 137-151, 2023, DOI:10.32604/csse.2023.032485
    Abstract Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is… More >

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    ARTICLE

    Anomaly Detection Based on Discrete Wavelet Transformation for Insider Threat Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589
    Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete… More >

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    ARTICLE

    An Immutable Framework for Smart Healthcare Using Blockchain Technology

    Faneela1, Muazzam A. Khan1, Suliman A. Alsuhibany2,*, Walid El-Shafai3,4, Mujeeb Ur Rehman5, Jawad Ahmad6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 165-179, 2023, DOI:10.32604/csse.2023.035066
    Abstract The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a More >

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    ARTICLE

    Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

    V. S. Vaisakhi1,*, D. Surendran2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 181-193, 2023, DOI:10.32604/csse.2023.032514
    Abstract In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage… More >

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    ARTICLE

    Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

    Rakesh Kumar Pattanaik1, Mihir N. Mohanty1,*, Srikanta Ku. Mohapatra2, Binod Ku. Pattanayak3
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 195-208, 2023, DOI:10.32604/csse.2023.029591
    Abstract System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems. As most practical systems don’t have prior information about the system behaviour thus, mathematical modelling is required. The authors have proposed a stacked Bidirectional Long-Short Term Memory (Bi-LSTM) model to handle the problem of nonlinear dynamic system identification in this paper. The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions. The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:… More >

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    ARTICLE

    Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

    Abdullah M. Basahel, Mohammad Yamin*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449
    Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents… More >

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    ARTICLE

    A Derivative Matrix-Based Covert Communication Method in Blockchain

    Xiang Zhang1, Xiaona Zhang2,4,*, Xiaorui Zhang3,5,6, Wei Sun6,7, Ruohan Meng8, Xingming Sun1
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 225-239, 2023, DOI:10.32604/csse.2023.034915
    Abstract The data in the blockchain cannot be tampered with and the users are anonymous, which enables the blockchain to be a natural carrier for covert communication. However, the existing methods of covert communication in blockchain suffer from the predefined channel structure, the capacity of a single transaction is not high, and the fixed transaction behaviors will lower the concealment of the communication channel. Therefore, this paper proposes a derivation matrix-based covert communication method in blockchain. It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into… More >

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    ARTICLE

    A Multi-Module Machine Learning Approach to Detect Tax Fraud

    N. Alsadhan*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 241-253, 2023, DOI:10.32604/csse.2023.033375
    Abstract Tax fraud is one of the substantial issues affecting governments around the world. It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability. This is done by either reducing sales or increasing purchases. According to recent studies, governments lose over $500 billion annually due to tax fraud. A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies. Most of the work done in tax fraud using machine learning is centered on supervised models. A significant drawback of this approach is that… More >

  • Open AccessOpen Access

    ARTICLE

    Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

    T. Satyanarayana Murthy1, P. Udayakumar2, Fayadh Alenezi3, E. Laxmi Lydia4, Mohamad Khairi Ishak5,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 255-271, 2023, DOI:10.32604/csse.2023.034193
    Abstract The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets’ vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces… More >

  • Open AccessOpen Access

    ARTICLE

    Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

    Abdulaziz Alorf*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 273-286, 2023, DOI:10.32604/csse.2023.035437
    Abstract Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. More >

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    ARTICLE

    MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

    Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311
    Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to… More >

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    ARTICLE

    Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

    Imran Ali1, Zohaib Mushtaq2, Saad Arif3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Walid El-Shafai5,6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 303-319, 2023, DOI:10.32604/csse.2023.034374
    Abstract Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on… More >

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    ARTICLE

    Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

    Mohammad Yamin1, Saleh Bajaba2, Sarah B. Basahel3, E. Laxmi Lydia4,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 321-336, 2023, DOI:10.32604/csse.2023.034849
    Abstract Deep learning (DL) models have been useful in many computer vision, speech recognition, and natural language processing tasks in recent years. These models seem a natural fit to handle the rising number of biometric recognition problems, from cellphone authentication to airport security systems. DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems. Iris recognition was considered the more reliable and accurate biometric detection method accessible. Iris recognition has been an active research region in the last few decades due to its extensive applications, from security in airports to homeland… More >

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    ARTICLE

    A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

    N. Vasudevan*, T. Karthick
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242
    Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation,… More >

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    ARTICLE

    Behavioral Intention to Continue Using a Library Mobile App

    X. Zhang1, H. Liu1, Z. H. Liu1, J. R. Ming1,*, Y. Zhou2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 357-369, 2023, DOI:10.32604/csse.2023.033251
    Abstract To meet the needs of today’s library users, institutions are developing library mobile apps (LMAs), as their libraries are increasingly intelligent and rely on deep learning. This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA. A research model was constructed based on the technology acceptance model. A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data. The analysis was based on structural equation modeling. The empirical results show that the… More >

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    ARTICLE

    Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

    Musaed Alrashidi*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 371-387, 2023, DOI:10.32604/csse.2023.034739
    Abstract The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones. Mosques are the type of buildings that have a unique energy usage pattern. Nevertheless, these types of buildings have minimal consideration in the ongoing energy efficiency applications. This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks. Therefore, this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh, Saudi Arabia. In this study, and by harvesting… More >

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    ARTICLE

    An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

    Qiang Zhi1, Xiang Jiang1, Hangying Zhang2, Zhengshu Zhou3, Jianguo Ren1, Tong Huang4,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 389-402, 2023, DOI:10.32604/csse.2023.035576
    Abstract The lightweight encryption algorithm based on Add-Rotation-XOR (ARX) operation has attracted much attention due to its high software affinity and fast operation speed. However, lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm. The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost. This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes. Firstly, we specify a More >

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    ARTICLE

    Hybrid Watermarking and Encryption Techniques for Securing Medical Images

    Amel Ali Alhussan1,*, Hanaa A. Abdallah2, Sara Alsodairi2, Abdelhamied A. Ateya3
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 403-416, 2023, DOI:10.32604/csse.2023.035048
    Abstract Securing medical data while transmission on the network is required because it is sensitive and life-dependent data. Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking. This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms. The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process. Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands. The resulting sub-band low-high (LH1) is decomposed using 2D-DWT… More >

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    ARTICLE

    Improved Beam Steering Method Using OAM Waves

    Nidal Qasem*, Ahmad Alamayreh
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 417-431, 2023, DOI:10.32604/csse.2023.035603
    Abstract Orbital Angular Momentum (OAM) is an intrinsic feature of electromagnetic waves which has recently found many applications in several areas in radio and optics. In this paper, we use OAM wave characteristics to present a simple method for beam steering over both elevation and azimuth planes. The design overcomes some limitations of traditional steering methods, such as limited dynamic range of steering, the design complexity, bulky size of the steering structure, the limited bandwidth of operation, and low gain. Based on OAM wave characteristics, the proposed steering method avoids design complexities by adopting a simple… More >

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    ARTICLE

    Exploring High-Performance Architecture for Data Center Networks

    Deshun Li1, Shaorong Sun2, Qisen Wu2, Shuhua Weng1, Yuyin Tan2, Jiangyuan Yao1,*, Xiangdang Huang1, Xingcan Cao3
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 433-443, 2023, DOI:10.32604/csse.2023.034368
    Abstract As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed… More >

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    ARTICLE

    Price Prediction of Seasonal Items Using Time Series Analysis

    Ahmed Salah1,2, Mahmoud Bekhit3, Esraa Eldesouky4,5, Ahmed Ali4,6,*, Ahmed Fathalla7
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 445-460, 2023, DOI:10.32604/csse.2023.035254
    Abstract The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices… More >

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    ARTICLE

    Alpha Fusion Adversarial Attack Analysis Using Deep Learning

    Mohibullah Khan1, Ata Ullah1, Isra Naz2, Sajjad Haider1, Nz Jhanji3,*, Mohammad Shorfuzzaman4, Mehedi Masud4
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 461-473, 2023, DOI:10.32604/csse.2023.029642
    Abstract The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN More >

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    ARTICLE

    IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

    Mesfer Alduhayyem1,*, Mrim M. Alnfiai2,3, Nabil Almalki4, Fahd N. Al-Wesabi5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 475-489, 2023, DOI:10.32604/csse.2023.034067
    Abstract Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a… More >

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    ARTICLE

    Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

    Nga Nguyen Thi Thanh, Quang H. Nguyen*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107
    Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

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    ARTICLE

    Image Recognition Based on Deep Learning with Thermal Camera Sensing

    Wen-Tsai Sung1, Chin-Hsuan Lin1, Sung-Jung Hsiao2,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 505-520, 2023, DOI:10.32604/csse.2023.034781
    Abstract As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The More >

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    ARTICLE

    Visualization Techniques via MLBS for Personnel Management in Major Events

    Yu Su1,2,3, Lingjuan Hou2,3,*, Sinan Li1, Zhaochang Jiang1, Haoran Peng4
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 521-536, 2023, DOI:10.32604/csse.2022.028606
    Abstract Mobile location-based services (MLBS) refer to services around geographic location data. Mobile terminals use wireless communication networks (or satellite positioning systems) to obtain users’ geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services. The development of systems based on MLBS has significance and practical value. In this paper a visualization management information system for personnel in major events based on microservices, namely MEPMIS, is designed and implemented by using MLBS. The system consists of a server and a client app, and it has some… More >

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    ARTICLE

    3D Object Detection with Attention: Shell-Based Modeling

    Xiaorui Zhang1,2,3,4,*, Ziquan Zhao1, Wei Sun4,5, Qi Cui6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230
    Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate… More >

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    ARTICLE

    A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

    Ali Alqazzaz1, Mohammad Tabrez Quasim1,*, Mohammed Mujib Alshahrani1, Ibrahim Alrashdi2, Mohammad Ayoub Khan1
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 551-562, 2023, DOI:10.32604/csse.2023.031048
    Abstract Facebook, Twitter, Instagram, and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts, posts, comments, images, and videos that express moods, sentiments, and feelings. With this, it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes. However, the analysis of depression based on social media has gained widespread acceptance worldwide, other verticals still have yet to be discovered. The depression analysis uses Twitter data from a publicly available web source in this work. To assess the accuracy of More >

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    ARTICLE

    A Novel Mixed Precision Distributed TPU GAN for Accelerated Learning Curve

    Aswathy Ravikumar, Harini Sriraman*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.034710
    Abstract Deep neural networks are gaining importance and popularity in applications and services. Due to the enormous number of learnable parameters and datasets, the training of neural networks is computationally costly. Parallel and distributed computation-based strategies are used to accelerate this training process. Generative Adversarial Networks (GAN) are a recent technological achievement in deep learning. These generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous datasets. Typically, a GAN is trained on a single server. Conventional deep learning accelerator designs are challenged by the unique properties of GAN,… More >

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    ARTICLE

    The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

    N. Rajagopal Reddy, S. Sharief Basha*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 579-596, 2023, DOI:10.32604/csse.2023.034527
    Abstract The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a… More >

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    ARTICLE

    LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

    Sesikala Bapatla1, J. Harikiran2,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998
    Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global… More >

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    ARTICLE

    Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection

    Eslam Hamouda1, Mohamed Ezz1,*, Ayman Mohamed Mostafa1, Murtada K. Elbashir1, Meshrif Alruily1, Mayada Tarek2
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 619-636, 2023, DOI:10.32604/csse.2023.035830
    Abstract Many types of research focus on utilizing Palmprint recognition in user identification and authentication. The Palmprint is one of biometric authentication (something you are) invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems. Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification. This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint templates and predicts whether these… More >

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    ARTICLE

    A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

    Neenavath Veeraiah1,*, Youseef Alotaibi2, Saleh Alghamdi3, Satish Thatavarti4
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 637-657, 2023, DOI:10.32604/csse.2023.034224
    Abstract Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting… More >

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    ARTICLE

    Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

    Sanghyuck Nam1, Suhwan Kwak1, Jaehwan Lee2, Sangoh Park1,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 659-670, 2023, DOI:10.32604/csse.2023.034994
    Abstract Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process… More >

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    ARTICLE

    Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

    Xiaorui Zhang1,2,3,*, Xianglong Zeng1, Wei Sun3,4, Yongjun Ren1,2,3, Tong Xu5
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 671-686, 2023, DOI:10.32604/csse.2023.035119
    Abstract Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation. Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features. In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures,… More >

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    ARTICLE

    Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

    V. Dinesh1, S. Srinivasan2, Gyanendra Prasad Joshi3, Woong Cho4,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 687-699, 2023, DOI:10.32604/csse.2023.035459
    Abstract In a vehicular ad hoc network (VANET), a massive quantity of data needs to be transmitted on a large scale in shorter time durations. At the same time, vehicles exhibit high velocity, leading to more vehicle disconnections. Both of these characteristics result in unreliable data communication in VANET. A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability. Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs. But one such difficulty was reducing the cluster number under increasing transmitting nodes. This… More >

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    ARTICLE

    An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

    Ahmed Ben Atitallah1,*, Mohamed Amin Ben Atitallah2,3, Yahia Said4,5, Mohammed Albekairi1, Anis Boudabous6, Turki M. Alanazi1, Khaled Kaaniche1, Mohamed Atri7
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 701-717, 2023, DOI:10.32604/csse.2023.035871
    Abstract The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction… More >

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    ARTICLE

    Byte-Level Function-Associated Method for Malware Detection

    Jingwei Hao*, Senlin Luo, Limin Pan
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 719-734, 2023, DOI:10.32604/csse.2023.033923
    Abstract The byte stream is widely used in malware detection due to its independence of reverse engineering. However, existing methods based on the byte stream implement an indiscriminate feature extraction strategy, which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes, resulting in byte semantic confusion. To address this issue, an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure, code, and data. The Minhash algorithm, grayscale mapping, More >

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    ARTICLE

    Deep Learning Framework for the Prediction of Childhood Medulloblastoma

    M. Muthalakshmi1,*, T. Merlin Inbamalar2, C. Chandravathi3, K. Saravanan4
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449
    Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement More >

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    ARTICLE

    Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

    Naqqash Dilshad1, Taimoor Khan2, JaeSeung Song1,*
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 749-764, 2023, DOI:10.32604/csse.2023.034475
    Abstract To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance… More >

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    ARTICLE

    Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

    Ahmed M. Elshewey1, Mahmoud Y. Shams2,*, Zahraa Tarek3, Mohamed Megahed4, El-Sayed M. El-kenawy5, Mohamed A. El-dosuky3,6
    Computer Systems Science and Engineering, Vol.46, No.1, pp. 765-781, 2023, DOI:10.32604/csse.2023.034324
    Abstract Food choice motives (i.e., mood, health, natural content, convenience, sensory appeal, price, familiarities, ethical concerns, and weight control) have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the… More >

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