CSSEOpen Access

Computer Systems Science and Engineering

ISSN:0267-6192(print)
ISSN:(online)
Publication Frequency:Monthly

  • Online
    Articles

    834

  • on board
    editors

    39



About the Journal

The Computer Systems Science and Engineering journal is devoted to the publication of high quality papers on theoretical developments in computer systems science, and their applications in computer systems engineering. Original research papers, state-of-the-art reviews and technical notes are invited for publication. Computer Systems Science and Engineering is published monthly by Tech Science Press.

Indexing and Abstracting

Science Citation Index (Web of Science): 2021 Impact Factor 4.397; Scopus Cite Score (Impact per Publication 2021): 1.9; SNIP (Source Normalized Impact per Paper 2021): 0.827; ACM Digital Library.

  • Open Access

    ARTICLE

    Learning-Based Metaheuristic Approach for Home Healthcare Optimization Problem

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 1-19, 2023, DOI:10.32604/csse.2023.029058
    Abstract This research focuses on the home health care optimization problem that involves staff routing and scheduling problems. The considered problem is an extension of multiple travelling salesman problem. It consists of finding the shortest path for a set of caregivers visiting a set of patients at their homes in order to perform various tasks during a given horizon. Thus, a mixed-integer linear programming model is proposed to minimize the overall service time performed by all caregivers while respecting the workload balancing constraint. Nevertheless, when the time horizon become large, practical-sized instances become very difficult to solve in a reasonable computational… More >

  • Open Access

    ARTICLE

    WACPN: A Neural Network for Pneumonia Diagnosis

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 21-34, 2023, DOI:10.32604/csse.2023.031330
    Abstract Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%,… More >

  • Open Access

    ARTICLE

    Developing Reliable Digital Healthcare Service Using Semi-Quantitative Functional Resonance Analysis

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 35-50, 2023, DOI:10.32604/csse.2023.030848
    Abstract Since entering the era of Industry 4.0, the concept of Healthcare 4.0 has also been put forward and explored by researchers. How to use Information Technology (IT) to better serve people’s healthcare is one of the most featured emerging directions in the academic circle. An important field of Healthcare 4.0 research is the reliability engineering of healthcare service. Because healthcare systems often affect the health and even life of their users, developers must be very cautious in the design, development, and operation of these healthcare systems and services. The problems to be solved include the reliability of business process, system… More >

  • Open Access

    ARTICLE

    ASL Recognition by the Layered Learning Model Using Clustered Groups

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 51-68, 2023, DOI:10.32604/csse.2023.030647
    Abstract American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the… More >

  • Open Access

    ARTICLE

    Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 69-84, 2023, DOI:10.32604/csse.2023.030598
    Abstract It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since soft computing approaches are more effective than traditional approaches, studies on MPPT have shifted in this direction. This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT. The meta-heuristic training algorithms used are particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA). The antecedent and conclusion parameters of… More >

  • Open Access

    ARTICLE

    Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 85-97, 2023, DOI:10.32604/csse.2023.030849
    Abstract With the rapid development of Internet technology, the type of information in the Internet is extremely complex, and a large number of riot contents containing bloody, violent and riotous components have appeared. These contents pose a great threat to the network ecology and national security. As a result, the importance of monitoring riotous Internet activity cannot be overstated. Convolutional Neural Network (CNN-based) target detection algorithm has great potential in identifying rioters, so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5 (YOLOv5), and further optimization of hyperparameters using genetic algorithm to… More >

  • Open Access

    ARTICLE

    Coupled CUBIC Congestion Control for MPTCP in Broadband Networks

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 99-115, 2023, DOI:10.32604/csse.2023.030801
    Abstract Recently, multipath transmission control protocol (MPTCP) was standardized so that data can be transmitted through multiple paths to utilize all available path bandwidths. However, when high-speed long-distance networks are included in MPTCP paths, the traffic transmission performance of MPTCP is severely deteriorated, especially in case the multiple paths’ characteristics are heavily asymmetric. In order to alleviate this problem, we propose a “Coupled CUBIC congestion control” that adopts TCP CUBIC on a large bandwidth-delay product (BDP) path in a linked increase manner for maintaining fairness with an ordinary TCP traversing the same bottleneck path. To verify the performance excellence of the… More >

  • Open Access

    ARTICLE

    An Adaptive Wireless Power Sharing Control for Multiterminal HVDC

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 117-129, 2023, DOI:10.32604/csse.2023.022464
    Abstract Power sharing among multiterminal high voltage direct current terminals (MT-HVDC) is mainly developed based on a priority or sequential manners, which uses to prevent the problem of overloading due to a predefined controller coefficient. Furthermore, fixed power sharing control also suffers from an inability to identify power availability at a rectification station. There is a need for a controller that ensures an efficient power sharing among the MT-HVDC terminals, prevents the possibility of overloading, and utilizes the available power sharing. A new adaptive wireless control for active power sharing among multiterminal (MT-HVDC) systems, including power availability and power management policy,… More >

  • Open Access

    ARTICLE

    Design of Semipersistent Resource Allocation in LTE-V Network

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 131-147, 2023, DOI:10.32604/csse.2023.027833
    Abstract Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced (LTE-A), which was developed by 3rd Generation Partnership Project (3GPP). Device-to-device (D2D) communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B (eNB). Moreover, D2D transmission can support vehicles as a vehicle-to-vehicle (V2V) environment. It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety (and efficiency). However, radio resources are limited in radio networks. A vehicle transmits through D2D in Long Term Evolution-Vehicle (LTE-V) mode-3 standard, and an eNB can allocate the same spectrum… More >

  • Open Access

    ARTICLE

    An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 149-166, 2023, DOI:10.32604/csse.2023.024424
    Abstract The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of… More >

  • Open Access

    ARTICLE

    Super-Resolution Based on Curvelet Transform and Sparse Representation

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 167-181, 2023, DOI:10.32604/csse.2023.028906
    Abstract Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary learning in the sparse representation… More >

  • Open Access

    ARTICLE

    Blockchain-based Privacy-Preserving Group Data Auditing with Secure User Revocation

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 183-199, 2023, DOI:10.32604/csse.2023.031030
    Abstract Progress in cloud computing makes group data sharing in outsourced storage a reality. People join in group and share data with each other, making team work more convenient. This new application scenario also faces data security threats, even more complex. When a user quit its group, remaining data block signatures must be re-signed to ensure security. Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side. However, considering the privacy and security need of group auditing, there still lacks a comprehensive solution to implement secure group user revocation, supporting identity privacy preserving and… More >

  • Open Access

    ARTICLE

    New Spam Filtering Method with Hadoop Tuning-Based MapReduce Naïve Bayes

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 201-214, 2023, DOI:10.32604/csse.2023.031270
    Abstract As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in… More >

  • Open Access

    ARTICLE

    Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 215-230, 2023, DOI:10.32604/csse.2023.031116
    Abstract Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their… More >

  • Open Access

    ARTICLE

    Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508
    Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.… More >

  • Open Access

    ARTICLE

    Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 247-261, 2023, DOI:10.32604/csse.2023.025972
    Abstract During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse,… More >

  • Open Access

    ARTICLE

    A Novel Approach for Network Vulnerability Analysis in IIoT

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 263-277, 2023, DOI:10.32604/csse.2023.029680
    Abstract Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the network traffic features are collected… More >

  • Open Access

    ARTICLE

    Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 279-292, 2023, DOI:10.32604/csse.2023.028703
    Abstract A prevalent diabetic complication is Diabetic Retinopathy (DR), which can damage the retina’s veins, leading to a severe loss of vision. If treated in the early stage, it can help to prevent vision loss. But since its diagnosis takes time and there is a shortage of ophthalmologists, patients suffer vision loss even before diagnosis. Hence, early detection of DR is the necessity of the time. The primary purpose of the work is to apply the data fusion/feature fusion technique, which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy. Mechanized procedures for… More >

  • Open Access

    ARTICLE

    Masked Face Recognition Using MobileNet V2 with Transfer Learning

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 293-309, 2023, DOI:10.32604/csse.2023.027986
    Abstract Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of… More >

  • Open Access

    ARTICLE

    Machine Learning-based Inverse Model for Few-Mode Fiber Designs

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 311-328, 2023, DOI:10.32604/csse.2023.029325
    Abstract The medium for next-generation communication is considered as fiber for fast, secure communication and switching capability. Mode division and space division multiplexing provide an excellent switching capability with high data transmission rate. In this work, the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division multiplexing. The technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of modes. It is for a three-ring-core few-mode fiber for guiding five, ten, fifteen, and twenty modes. Three types of regression… More >

  • Open Access

    ARTICLE

    A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 329-342, 2023, DOI:10.32604/csse.2023.028528
    Abstract

    In cognitive radio networks (CoR), the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability. Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection. However, these methods do not take into account the effect of sample size and its effect on improving CoR performance. In general, a large sample size results in more reliable detection, but takes longer sensing time and increases complexity. Thus, the locally sensed sample size is an optimization problem. Therefore, optimizing the local sample size for each cognitive… More >

  • Open Access

    ARTICLE

    Selection of Wind Turbine Systems for the Sultanate of Oman

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 343-359, 2023, DOI:10.32604/csse.2023.029510
    Abstract The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades, and the government has struggled to find a solution. In addition, Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040, including solar and wind turbines. Furthermore, the use of small-scale energy from wind devices has been on the rise in recent years. This upward trend is attributed to advancements in wind turbine technology, which have lowered the cost of energy from wind. To calculate… More >

  • Open Access

    ARTICLE

    Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597
    Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks… More >

  • Open Access

    ARTICLE

    Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 377-393, 2023, DOI:10.32604/csse.2023.027481
    Abstract With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty… More >

  • Open Access

    ARTICLE

    TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 395-408, 2023, DOI:10.32604/csse.2023.029032
    Abstract Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights.… More >

  • Open Access

    ARTICLE

    Genetics Based Compact Fuzzy System for Visual Sensor Network

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 409-426, 2023, DOI:10.32604/csse.2023.026846
    Abstract As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called… More >

  • Open Access

    ARTICLE

    A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 427-443, 2023, DOI:10.32604/csse.2023.026688
    Abstract In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster Head (CH). Then, the data… More >

  • Open Access

    ARTICLE

    Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 445-458, 2023, DOI:10.32604/csse.2023.027329
    Abstract In Wireless Sensor Network (WSN), scheduling is one of the important issues that impacts the lifetime of entire WSN. Various scheduling schemes have been proposed earlier to increase the lifetime of the network. Still, the results from such methods are compromised in terms of achieving high lifetime. With this objective to increase the lifetime of network, an Efficient Topology driven Cooperative Self-Scheduling (TDCSS) model is recommended in this study. Instead of scheduling the network nodes in a centralized manner, a combined approach is proposed. Based on the situation, the proposed TDCSS approach performs scheduling in both the ways. By sharing… More >

  • Open Access

    ARTICLE

    A Multi-Stage Secure IoT Authentication Protocol

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 459-481, 2023, DOI:10.32604/csse.2023.028536
    Abstract The Internet of Things (IoT) is a network of heterogeneous and smart devices that can make decisions without human intervention. It can connect millions of devices across the universe. Their ability to collect information, perform analysis, and even come to meaningful conclusions without human capital intervention matters. Such circumstances require stringent security measures and, in particular, the extent of authentication. Systems applied in the IoT paradigm point out high-interest levels since enormous damage will occur if a malicious, wrongly authenticated device finds its way into the IoT system. This research provides a clear and updated view of the trends in… More >

  • Open Access

    ARTICLE

    Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 483-495, 2023, DOI:10.32604/csse.2023.029854
    Abstract Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing… More >

  • Open Access

    ARTICLE

    Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 497-512, 2023, DOI:10.32604/csse.2023.029133
    Abstract Recently, energy harvesting wireless sensor networks (EHWSN) have increased significant attention among research communities. By harvesting energy from the neighboring environment, the sensors in EHWSN resolve the energy constraint problem and offers lengthened network lifetime. Clustering is one of the proficient ways for accomplishing even improved lifetime in EHWSN. The clustering process intends to appropriately elect the cluster heads (CHs) and construct clusters. Though several models are available in the literature, it is still needed to accomplish energy efficiency and security in EHWSN. In this view, this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy… More >

  • Open Access

    ARTICLE

    Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 513-530, 2023, DOI:10.32604/csse.2023.029608
    Abstract Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are… More >

  • Open Access

    ARTICLE

    Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 531-544, 2023, DOI:10.32604/csse.2023.021548
    Abstract The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving… More >

  • Open Access

    ARTICLE

    Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 545-562, 2023, DOI:10.32604/csse.2023.029975
    Abstract Heartbeat detection stays central to cardiovascular an electrocardiogram (ECG) is used to help with disease diagnosis and management. Existing Convolutional Neural Network (CNN)-based methods suffer from the less generalization problem thus; the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed. In contrast, this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders (KDNN-SAE) that computes the disease before the exact heart rate by combining features from multiple ECG Signals. Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.… More >

  • Open Access

    ARTICLE

    Optical Ciphering Scheme for Cancellable Speaker Identification System

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.024375
    Abstract Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the employment of non-invertible transforms. This… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 579-594, 2023, DOI:10.32604/csse.2023.027592
    Abstract Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time consuming, inconsistent prediction etc. To… More >

  • Open Access

    ARTICLE

    Optimal Dynamic Voltage Restorer Using Water Cycle Optimization Algorithm

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 595-623, 2023, DOI:10.32604/csse.2023.027966
    Abstract This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR) in a three-phase system. The control scheme employs the fractional order, proportional-integral-derivative (FOPID) controller to improve on the DVR performance in order to enhance the power quality in terms of the response time, steady-state error and total harmonic distortion (THD). The result obtained was compared with fractional order, proportional-integral (FOPI), proportional-integral-derivative (PID) and proportional-integral (PI) controllers in order to show the effectiveness of the proposed DVR control scheme. A water cycle optimization algorithm (WCA) was utilized to find the optimal set for all… More >

  • Open Access

    ARTICLE

    Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 625-640, 2023, DOI:10.32604/csse.2023.030444
    Abstract An ad-hoc sensor network (ASN) is a group of sensing nodes that transmit data over a wireless link to a target node, direct or indirect, through a series of nodes. ASN becomes a high-risk group for several security exploits due to the sensor node’s limited resources. Internal threats are more challenging to protect against than external attacks. The nodes are grouped, and calculate each node’s trust level. The trust level is the result of combining internal and external trust degrees. Cluster heads (CH) are chosen based on the anticipated trust levels. The communications are then digitally signed by the source,… More >

  • Open Access

    ARTICLE

    Modified Elliptic Curve Cryptography Multi-Signature Scheme to Enhance Security in Cryptocurrency

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 641-658, 2023, DOI:10.32604/csse.2023.028341
    Abstract Internet of Things (IoT) is an emerging technology that moves the world in the direction of smart things. But, IoT security is the complex problem due to its centralized architecture, and limited capacity. So, blockchain technology has great attention due to its features of decentralized architecture, transparency, immutable records and cryptography hash functions when combining with IoT. Cryptography hash algorithms are very important in blockchain technology for secure transmission. It converts the variable size inputs to a fixed size hash output which is unchangeable. Existing cryptography hash algorithms with digital signature have issues of single node accessibility and accessed up… More >

  • Open Access

    ARTICLE

    Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 659-669, 2023, DOI:10.32604/csse.2023.028400
    Abstract The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The… More >

  • Open Access

    ARTICLE

    Classification Model for IDS Using Auto Cryptographic Denoising Technique

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 671-685, 2023, DOI:10.32604/csse.2023.029984
    Abstract Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats.… More >

  • Open Access

    ARTICLE

    Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 687-700, 2023, DOI:10.32604/csse.2023.030188
    Abstract Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity… More >

  • Open Access

    ARTICLE

    Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 701-714, 2023, DOI:10.32604/csse.2023.030611
    Abstract The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass… More >

  • Open Access

    ARTICLE

    Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 715-736, 2023, DOI:10.32604/csse.2023.029017
    Abstract Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in… More >

  • Open Access

    ARTICLE

    Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 737-751, 2023, DOI:10.32604/csse.2023.030170
    Abstract Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long… More >

  • Open Access

    ARTICLE

    Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 753-767, 2023, DOI:10.32604/csse.2023.030556
    Abstract Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with… More >

  • Open Access

    ARTICLE

    Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 769-784, 2023, DOI:10.32604/csse.2023.029457
    Abstract In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PIλDµ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for… More >

  • Open Access

    ARTICLE

    BE-RPL: Balanced-load and Energy-efficient RPL

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 785-801, 2023, DOI:10.32604/csse.2023.030393
    Abstract Internet of Things (IoT) empowers imaginative applications and permits new services when mobile nodes are included. For IoT-enabled low-power and lossy networks (LLN), the Routing Protocol for Low-power and Lossy Networks (RPL) has become an established standard routing protocol. Mobility under standard RPL remains a difficult issue as it causes continuous path disturbance, energy loss, and increases the end-to-end delay in the network. In this unique circumstance, a Balanced-load and Energy-efficient RPL (BE-RPL) is proposed. It is a routing technique that is both energy-efficient and mobility-aware. It responds quicker to link breakage through received signal strength-based mobility monitoring and selecting… More >

  • Open Access

    ARTICLE

    Web Page Recommendation Using Distributional Recurrent Neural Network

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 803-817, 2023, DOI:10.32604/csse.2023.028770
    Abstract In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the… More >

  • Open Access

    ARTICLE

    Genetic Algorithm Based Smart Grid System for Distributed Renewable Energy Sources

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 819-837, 2023, DOI:10.32604/csse.2023.028525
    Abstract This work presents the smart grid system for distributed Renewable Energy Sources (RES) with control methods. The hybrid MicroGrids (MG) are trending in small-scale power systems that involve distributed generations, power storage, and various loads. RES of solar are implemented with boost converter using Maximum Power Point Tracking (MPPT) with perturb and observe technique to track the maximum power. Also, the wind system is designed by permanent magnet synchronous generator that includes boost converter with MPPT technique. The battery is also employed with a Direct Current (DC)-DC bidirectional converter, and has a state of charge. The MATLAB/Simulink Simscape software is… More >

  • Open Access

    ARTICLE

    Voice Guidance System for Color Recognition Based on IoT

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 839-855, 2023, DOI:10.32604/csse.2023.030408
    Abstract We often need to identify the colors of such objects as buildings, clothing, photos, and traffic signs in everyday life. Some people cannot distinguish among such colors. This study proposes a method to help people, including colorblind people, identify colors. We establish a platform for color identification that is connected by the Internet of Things (IoT). The Espressif Systems 32 (ESP32) single chip is connected to a Wi-Fi communication network to transmit data to the color identification platform. The system interface is displayed in the form of color code and color name display. The speech synthesis module Speech Synthesizer Node… More >

  • Open Access

    ARTICLE

    A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 857-868, 2023, DOI:10.32604/csse.2023.031321
    Abstract The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine… More >

  • Open Access

    ARTICLE

    Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 869-886, 2023, DOI:10.32604/csse.2023.029822
    Abstract Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the… More >

  • Open Access

    ARTICLE

    Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 887-901, 2023, DOI:10.32604/csse.2023.030627
    Abstract Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and… More >

  • Open Access

    ARTICLE

    Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 903-924, 2023, DOI:10.32604/csse.2023.030749
    Abstract The purpose of sensing the environment and geographical positions, device monitoring, and information gathering are accomplished using Wireless Sensor Network (WSN), which is a non-dependent device consisting of a distinct collection of Sensor Node (SN). Thus, a clustering based on Energy Efficient (EE), one of the most crucial processes performed in WSN with distinct environments, is utilized. In order to efficiently manage energy allocation during sensing and communication, the present research on managing energy efficiency is performed on the basis of distributed algorithm. Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of… More >

  • Open Access

    ARTICLE

    Multivariate Broadcast Encryption with Group Key Algorithm for Secured IoT

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 925-938, 2023, DOI:10.32604/csse.2023.027315
    Abstract The expanding and ubiquitous availability of the Internet of Things (IoT) have changed everyone’s life easier and more convenient. Same time it also offers a number of issues, such as effectiveness, security, and excessive power consumption, which constitute a danger to intelligent IoT-based apps. Group managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member. A centralized trustworthy system, which is in charge of key distribution and upgrades, is used to maintain group keys. To provide longitudinal access controls, Software Defined Network… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 939-953, 2023, DOI:10.32604/csse.2023.030491
    Abstract The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also… More >

  • Open Access

    ARTICLE

    Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 955-969, 2023, DOI:10.32604/csse.2023.030132
    Abstract Recently, unmanned aerial vehicles (UAV) or drones are widely employed for several application areas such as surveillance, disaster management, etc. Since UAVs are limited to energy, efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station (BS). Therefore, clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs. In this aspect, this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system (GTOADL-SCS) technique for UAV networks. The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification. At… More >

  • Open Access

    ARTICLE

    Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 971-983, 2023, DOI:10.32604/csse.2023.030362
    Abstract Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years. Both Machine Learning (ML) and Deep Learning (DL) classification models are useful in effective identification and classification of cyberattacks. In addition, the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification models. In this background, the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning (ICC-CGODL) Model. The goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon data. Besides, ICC-CGODL model primarily… More >

  • Open Access

    ARTICLE

    Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 985-1000, 2023, DOI:10.32604/csse.2023.029189
    Abstract Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis. The recent challenges faced in Mobile Ad Hoc Networks (MANETs) include scalability, localization, heterogeneous network, self-organization, and self-sufficient operation. In this background, the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks, especially in disaster area network. Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol (IP) architecture. However, IP-based communications have a few limitations such as inefficient bandwidth utilization, high processing, less transfer speeds, and excessive memory… More >

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