CSSEOpen Access

Computer Systems Science and Engineering

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

  • Online
    Articles

    914

  • on board
    editors

    38



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

    A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463
    Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL)… More >

  • Open Access

    ARTICLE

    Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003
    Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >

  • Open Access

    ARTICLE

    Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1033-1045, 2023, DOI:10.32604/csse.2023.028225
    Abstract Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land… More >

  • Open Access

    ARTICLE

    Effective and Efficient Video Compression by the Deep Learning Techniques

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1047-1061, 2023, DOI:10.32604/csse.2023.030513
    Abstract Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using… More >

  • Open Access

    ARTICLE

    Intelligent Home Using Fuzzy Control Based on AIoT

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1063-1081, 2023, DOI:10.32604/csse.2023.028438
    Abstract The Internet of Things has grown rapidly in recent years, and the technologies related to it have been widely used in various fields. The idea of this paper is to build a set of Internet of Things systems in a smart home wireless network environment, with the purpose of providing people with a more comfortable, convenient, and safe life. In the sensing layer of the Internet of Things, we discuss the uses of common sensing technologies on the Internet and combine these with Arduino microprocessors to integrate temperature sensing modules, humidity sensing modules, gas sensing modules, and particulate matter 2.5… More >

  • Open Access

    ARTICLE

    Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1083-1094, 2023, DOI:10.32604/csse.2023.032786
    Abstract The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion, which leads to a false matching, with an inaccurate pose estimation or failed tracking. To address the challenge above, a new method of feature point matching is proposed in this paper, which combines the variable velocity model with the reverse optical flow method. First, the constant velocity model is extended to a new variable velocity model, and the expanded variable velocity model is used to provide the initial pixel shifting for… More >

  • Open Access

    ARTICLE

    A Secure Framework for Blockchain Transactions Protection

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1095-1111, 2023, DOI:10.32604/csse.2023.032862
    Abstract One of the most extensively used technologies for improving the security of IoT devices is blockchain technology. It is a new technology that can be utilized to boost the security. It is a decentralized peer-to-peer network with no central authority. Multiple nodes on the network mine or verify the data recorded on the Blockchain. It is a distributed ledger that may be used to keep track of transactions between several parties. No one can tamper with the data on the blockchain since it is unchangeable. Because the blocks are connected by hashes, the transaction data is safe. It is managed… More >

  • Open Access

    ARTICLE

    Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1113-1128, 2023, DOI:10.32604/csse.2023.030842
    Abstract Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres… More >

  • Open Access

    ARTICLE

    Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1129-1143, 2023, DOI:10.32604/csse.2023.030080
    Abstract Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image… More >

  • Open Access

    ARTICLE

    An Enhanced Group Key-Based Security Protocol to Protect 5G SON Against FBS

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1145-1165, 2023, DOI:10.32604/csse.2023.032044
    Abstract Network operators are attempting many innovations and changes in 5G using self-organizing networks (SON). The SON operates on the measurement reports (MR), which are obtained from user equipment (UE) and secured against malware and userspace programs. However, the synchronization signal block that the UE relies on to measure the wireless environment configured by a base station is not authenticated. As a result, the UE will likely gauge the wrong wireless environment configured by a false base station (FBS) and transmit the corresponding MR to the serving base station, which poisons the data used for 5G SONs. Therefore, the serving base… More >

  • Open Access

    ARTICLE

    De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1167-1186, 2023, DOI:10.32604/csse.2023.032508
    Abstract Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper… More >

  • Open Access

    ARTICLE

    Cooperative NOMA Based on OAM Transmission for Beyond 5G Applications

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1187-1197, 2023, DOI:10.32604/csse.2023.030699
    Abstract Cooperative non-orthogonal multiple access (NOMA) is heavily studied in the literature as a solution for 5G and beyond 5G applications. Cooperative NOMA transmits a superimposed version of all users’ messages simultaneously with the aid of a relay, after that, each user decodes its own message. Accordingly, NOMA is deemed as a spectral efficient technique. Another emerging technique exploits orbital angular momentum (OAM), where OAM is an attractive character of electromagnetic waves. OAM gathered a great deal of attention in recent years (similar to the case with NOMA) due to its ability to enhance electromagnetic spectrum exploitation, hence increasing the achieved… More >

  • Open Access

    ARTICLE

    Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1199-1210, 2023, DOI:10.32604/csse.2023.030909
    Abstract Multicarrier Waveform (MCW) has several advantages and plays a very important role in cellular systems. Fifth generation (5G) MCW such as Non-Orthogonal Multiple Access (NOMA) and Filter Bank Multicarrier (FBMC) are thought to be important in 5G implementation. High Peak to Average Power Ratio (PAPR) is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user devices. The paper presents a novel Divergence Selective Mapping (DSLM) and Divergence Partial Transmission Sequence (D-PTS) for 5G waveforms. It is seen that the proposed D-SLM and PTS lower PAPR with low computational complexity. The work… More >

  • Open Access

    ARTICLE

    Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1211-1231, 2023, DOI:10.32604/csse.2023.029184
    Abstract The amount of needed control messages in wireless sensor networks (WSN) is affected by the storage strategy of detected events. Because broadcasting superfluous control messages consumes excess energy, the network lifespan can be extended if the quantity of control messages is decreased. In this study, an optimized storage technique having low control overhead for tracking the objects in WSN is introduced. The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List (OBLL). Whenever an object passes over the… More >

  • Open Access

    ARTICLE

    Adaptive Deep Learning Model for Software Bug Detection and Classification

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1233-1248, 2023, DOI:10.32604/csse.2023.025991
    Abstract Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with… More >

  • Open Access

    ARTICLE

    Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1249-1263, 2023, DOI:10.32604/csse.2023.030603
    Abstract Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts’ knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals.… More >

  • Open Access

    ARTICLE

    Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1265-1278, 2023, DOI:10.32604/csse.2023.026776
    Abstract The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1279-1294, 2023, DOI:10.32604/csse.2023.031001
    Abstract The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short Term Memory (LSTM), Convolutional Neural… More >

  • Open Access

    ARTICLE

    Intrusion Detection Using Federated Learning for Computing

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1295-1308, 2023, DOI:10.32604/csse.2023.027216
    Abstract The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall behavior of an intruder while… More >

  • Open Access

    ARTICLE

    Profiling of Urban Noise Using Artificial Intelligence

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1309-1321, 2023, DOI:10.32604/csse.2023.031010
    Abstract Noise pollution tends to receive less awareness compared to other types of pollution, however, it greatly impacts the quality of life for humans such as causing sleep disruption, stress or hearing impairment. Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control, planning of the soundscape environment, or selection of safe living space. In this paper, we proposed a self-attention long short-term memory (LSTM) method that can improve sound classification compared to previous baselines. An attention mechanism will be designed solely to… More >

  • Open Access

    ARTICLE

    Optimized Resource Allocation and Queue Management for Traffic Control in MANET

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1323-1342, 2023, DOI:10.32604/csse.2023.030786
    Abstract A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently… More >

  • Open Access

    ARTICLE

    Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1343-1356, 2023, DOI:10.32604/csse.2023.031142
    Abstract Web services are provided as reusable software components in the services-oriented architecture. More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations. The workflow consists of tasks where many services can be considered for each task. Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem. This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey. The proposed algorithm determines the… More >

  • Open Access

    ARTICLE

    Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772
    Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the… More >

  • Open Access

    ARTICLE

    DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1375-1392, 2023, DOI:10.32604/csse.2023.030944
    Abstract A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network… More >

  • Open Access

    ARTICLE

    Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1393-1407, 2023, DOI:10.32604/csse.2023.030328
    Abstract Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN… More >

  • Open Access

    ARTICLE

    Image Enhancement Using Adaptive Fractional Order Filter

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1409-1422, 2023, DOI:10.32604/csse.2023.029611
    Abstract Image enhancement is an important preprocessing task as the contrast is low in most of the medical images, Therefore, enhancement becomes the mandatory process before actual image processing should start. This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images, the proposed operators are based on Grunwald-Letnikov (G-L), Riemann-Liouville (R-L) and Caputo (Li & Xie), which are the definitions of fractional order calculus. In this fractional-order, differentiation is well focused on the enhancement of echocardiographic images. This provoked for developing a non-linear filter mask for image enhancement. The designed filter is… More >

  • Open Access

    ARTICLE

    Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1423-1438, 2023, DOI:10.32604/csse.2023.026057
    Abstract An image can be degraded due to many environmental factors like foggy or hazy weather, low light conditions, extra light conditions etc. Image captured under the poor light conditions is generally known as non-uniform illumination image. Non-uniform illumination hides some important information present in an image during the image capture Also, it degrades the visual quality of image which generates the need for enhancement of such images. Various techniques have been present in literature for the enhancement of such type of images. In this paper, a novel architecture has been proposed for enhancement of poor illumination images which uses radial… More >

  • Open Access

    ARTICLE

    Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1439-1454, 2023, DOI:10.32604/csse.2023.029953
    Abstract Online reviews significantly influence decision-making in many aspects of society. The integrity of internet evaluations is crucial for both consumers and vendors. This concern necessitates the development of effective fake review detection techniques. The goal of this study is to identify fraudulent text reviews. A comparison is made on shill reviews vs. genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset. We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability. Overall, the research improves fake review screening by using various transformer models such… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Artifact Removal from Brain Signal

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1455-1467, 2023, DOI:10.32604/csse.2023.029649
    Abstract Electroencephalography (EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. To extract clean clinical information from EEG signals, it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with Electrocardiogram (ECG) artifacts that occurs mostly in cardiac patients. The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in… More >

  • Open Access

    ARTICLE

    Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1469-1482, 2023, DOI:10.32604/csse.2023.031943
    Abstract Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a… More >

  • Open Access

    ARTICLE

    PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1483-1493, 2023, DOI:10.32604/csse.2023.021540
    Abstract Data transmission through a wireless network has faced various signal problems in the past decades. The orthogonal frequency division multiplexing (OFDM) technique is widely accepted in multiple data transfer patterns at various frequency bands. A recent wireless communication network uses OFDM in long-term evolution (LTE) and 5G, among others. The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network. This transmission loss is called peak-to-average power ratio (PAPR). This wireless signal distortion can be reduced using various techniques. This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.… More >

  • Open Access

    ARTICLE

    Real Time Automation and Ratio Control Using PLC & SCADA in Industry 4.0

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1495-1516, 2023, DOI:10.32604/csse.2023.030635
    Abstract Industrial Control Systems (ICS) and SCADA (Supervisory Control and Data Acquisition) systems play a critical role in the management and regulation of critical infrastructure. SCADA systems brings us closer to the real-time application world. All process and equipment control capability is typically provided by a Distributed Control System (DCS) in industries such as power stations, agricultural systems, chemical and water treatment plants. Instead of control through DCS, this paper proposes a SCADA and PLC (Programmable Logic Controller) system to control the ratio control division and the assembly line division inside the chemical plant. A specific design and implementation method for… More >

  • Open Access

    ARTICLE

    Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1517-1530, 2023, DOI:10.32604/csse.2023.030069
    Abstract Multicast device-to-device (D2D) communication technology is considered as one of the new technologies in the fifth generation (5G) networks that directly addresses the need for content sharing among internet users. In fact, when direct communication is available between devices, the spectral efficiency is improved by reusing the licensed cellular spectrum. The current studies show that D2D communication increases network capacity and reduces latency. In order to achieve the alternate capabilities, coordination is required to implement interference management. We considered subcarrier allocation for the uplink, in addition to the power control that takes place on the underlay network. The completed data… More >

  • Open Access

    ARTICLE

    Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745
    Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous… More >

  • Open Access

    ARTICLE

    Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1545-1559, 2023, DOI:10.32604/csse.2023.027580
    Abstract Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this… More >

  • Open Access

    ARTICLE

    Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1561-1576, 2023, DOI:10.32604/csse.2023.029463
    Abstract Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration… More >

  • Open Access

    ARTICLE

    Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1577-1593, 2023, DOI:10.32604/csse.2023.031319
    Abstract A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put forward a quick and effective… More >

  • Open Access

    ARTICLE

    An Optimized Method for Accounting Information in Logistic Systems

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1595-1609, 2023, DOI:10.32604/csse.2023.027971
    Abstract In the era of rapid information development, with the popularity of computers, the advancement of science and technology, and the ongoing expansion of IT technology and business, the enterprise resource planning (ERP) system has evolved into a platform and a guarantee for the fulfilment of company management procedures after long-term operations. Because of developments in information technology, most manual accounting procedures are being replaced by computerized Accounting Information Systems (AIS), which are quicker and more accurate. The primary factors influencing the decisions of logistics firm trading parties are investigated in order to enhance the design of decision-supporting modules and to… More >

  • Open Access

    ARTICLE

    Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1611-1623, 2023, DOI:10.32604/csse.2023.026561
    Abstract Retransmission avoidance is an essential need for any type of wireless communication. As retransmissions induce the unnecessary presence of redundant data in every accessible node. As storage capacity is symmetrical to the size of the memory, less storage capacity is experienced due to the restricted size of the respective node. In this proposed work, we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization (PSO). PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space. PSO is integrated with a Reduced Coverage Set, to… More >

  • Open Access

    ARTICLE

    Wrapper Based Linear Discriminant Analysis (LDA) for Intrusion Detection in IIoT

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1625-1640, 2023, DOI:10.32604/csse.2023.025669
    Abstract The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive… More >

  • Open Access

    ARTICLE

    Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1641-1654, 2023, DOI:10.32604/csse.2023.031582
    Abstract Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine… More >

  • Open Access

    ARTICLE

    Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1655-1677, 2023, DOI:10.32604/csse.2023.030697
    Abstract An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1679-1694, 2023, DOI:10.32604/csse.2023.031063
    Abstract Due to exponential increase in smart resource limited devices and high speed communication technologies, Internet of Things (IoT) have received significant attention in different application areas. However, IoT environment is highly susceptible to cyber-attacks because of memory, processing, and communication restrictions. Since traditional models are not adequate for accomplishing security in the IoT environment, the recent developments of deep learning (DL) models find beneficial. This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection (HMFS-SDLCAD) model. The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment. At… More >

  • Open Access

    ARTICLE

    Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1695-1709, 2023, DOI:10.32604/csse.2023.031506
    Abstract 3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached… More >

  • Open Access

    ARTICLE

    Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1711-1726, 2023, DOI:10.32604/csse.2023.031467
    Abstract Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads… More >

  • Open Access

    ARTICLE

    DoS Attack Detection Based on Deep Factorization Machine in SDN

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1727-1742, 2023, DOI:10.32604/csse.2023.030183
    Abstract Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets… More >

  • Open Access

    ARTICLE

    Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1743-1758, 2023, DOI:10.32604/csse.2023.032127
    Abstract Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve… More >

  • Open Access

    ARTICLE

    Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1759-1773, 2023, DOI:10.32604/csse.2023.030581
    Abstract Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm… More >

  • Open Access

    ARTICLE

    Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031841
    Abstract Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration… More >

  • Open Access

    ARTICLE

    Optimized Resource Allocation for IoT-D2D Communication Using WSN

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1789-1804, 2023, DOI:10.32604/csse.2023.031341
    Abstract The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in… More >

  • Open Access

    ARTICLE

    Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1805-1819, 2023, DOI:10.32604/csse.2023.029622
    Abstract Since transactions in blockchain are based on public ledger verification, this raises security concerns about privacy protection. And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification, when the whole transaction on the chain is verified. In order to improve the efficiency and privacy protection of block data verification, this paper proposes an efficient block verification mechanism with privacy protection based on zero-knowledge proof (ZKP), which not only protects the privacy of users but also improves the speed of data block verification. There is no need to put the whole… More >

  • Open Access

    ARTICLE

    Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1821-1836, 2023, DOI:10.32604/csse.2023.029561
    Abstract Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks. Recently,… More >

  • Open Access

    ARTICLE

    A Hybridized Artificial Neural Network for Automated Software Test Oracle

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1837-1850, 2023, DOI:10.32604/csse.2023.029703
    Abstract Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality. These two characteristics are very critical in the software applications of present times. When testers want to perform scenario evaluations, test oracles are generally employed in the third phase. Upon test case execution and test outcome generation, it is essential to validate the results so as to establish the software behavior’s correctness. By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application, leads… More >

  • Open Access

    ARTICLE

    Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136
    Abstract Biometrics, which has become integrated with our daily lives, could fall prey to falsification attacks, leading to security concerns. In our paper, we use Transient Evoked Otoacoustic Emissions (TEOAE) that are generated by the human cochlea in response to an external sound stimulus, as a biometric modality. TEOAE are robust to falsification attacks, as the uniqueness of an individual’s inner ear cannot be impersonated. In this study, we use both the raw 1D TEOAE signals, as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform (CWT). We use 1D and 2D Convolutional Neural Networks (CNN) for… More >

  • Open Access

    ARTICLE

    Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1869-1886, 2023, DOI:10.32604/csse.2023.029762
    Abstract Signal processing based research was adopted with Electroencephalogram (EEG) for predicting the abnormality and cerebral activities. The proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or not. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase and hence we have proposed this idea. In this work, the EEG signal series are divided… More >

  • Open Access

    ARTICLE

    Micro Calcification Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808
    Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary to focus on the four… More >

  • Open Access

    ARTICLE

    Avoid Suspicious Route of Blackhole Nodes in MANET’s: Using A Cooperative Trapping

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1901-1915, 2023, DOI:10.32604/csse.2023.027819
    Abstract Mobile Ad hoc Network (MANET) is decentralized wireless network and can communicate without existing infrastructure in many areas. MANET is vulnerable to various attacks that affect its performance such as blackhole attack. Blackhole attacker, inject fault routing information to persuade the source node to select the path with malicious node as the shortest path. To eliminate malicious nodes from launching any collaborative attack. A cooperative Trapping Approach (CTA) was proposed based on modifying Ad-hoc On-demand Distance Vector (AODV) routing protocol and trapping the malicious nodes by responding to the trap request message. The approach aims to eliminate and rule out… More >

  • Open Access

    ARTICLE

    Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1917-1934, 2023, DOI:10.32604/csse.2023.032497
    Abstract Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by… More >

  • Open Access

    ARTICLE

    Trusted Cluster-Based Communication for Wireless Sensor Network Using Meta-Heuristic Algorithms

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1935-1951, 2023, DOI:10.32604/csse.2023.031509
    Abstract The mobile transient and sensor network’s routing algorithm detects available multi-hop paths between source and destination nodes. However, some methods are not as reliable or trustworthy as expected. Therefore, finding a reliable method is an important factor in improving communication security. For further enhancement of protected communication, we suggest a trust cluster based secure routing (TCSR) framework for wireless sensor network (WSN) using optimization algorithms. First, we introduce an efficient cluster formation using a modified tug of war optimization (MTWO) algorithm, which provides load-balanced clusters for energy-efficient data transmission. Second, we illustrate the optimal head selection using multiple design constraints… More >

  • Open Access

    ARTICLE

    ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1953-1970, 2023, DOI:10.32604/csse.2023.028713
    Abstract Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc. These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques. Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region. This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images. Underwater color image restoration techniques based… More >

  • Open Access

    ARTICLE

    Energy Efficient Unequal Fault Tolerance Clustering Approach

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1971-1983, 2023, DOI:10.32604/csse.2022.021924
    Abstract For achieving Energy-Efficiency in wireless sensor networks (WSNs), different schemes have been proposed which focuses only on reducing the energy consumption. A shortest path determines for the Base Station (BS), but fault tolerance and energy balancing gives equal importance for improving the network lifetime. For saving energy in WSNs, clustering is considered as one of the effective methods for Wireless Sensor Networks. Because of the excessive overload, more energy consumed by cluster heads (CHs) in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure. For increasing the WSNs’ lifetime, the… More >

  • Open Access

    ARTICLE

    Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1985-2000, 2023, DOI:10.32604/csse.2023.030960
    Abstract The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn’t exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have… More >

  • Open Access

    ARTICLE

    IoT-Deep Learning Based Activity Recommendation System

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2001-2016, 2023, DOI:10.32604/csse.2023.031965
    Abstract The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community. Health professionals are increasingly making use of the benefits of these technologies, resulting in a major improvement in health care both in and out of clinical settings. The Internet of Things (IoT) is a new internet revolution that is a rising research area, particularly in health care. Healthcare Monitoring Systems (HMS) have progressed rapidly as the usage of Wearable Sensors (WS) and smartphones have increased. The existing framework of conventional telemedicine’s store-and-forward method has some issues, including… More >

  • Open Access

    ARTICLE

    Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2017-2032, 2023, DOI:10.32604/csse.2023.029169
    Abstract Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called… More >

  • Open Access

    ARTICLE

    Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.031181
    Abstract Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech. Online provocation, abuses, and attacks are widely termed cyberbullying (CB). The massive quantity of user generated content makes it difficult to recognize CB. Current advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) tools enable to detect and classify CB in social networks. In this view, this study introduces a spotted hyena optimizer with deep learning driven cybersecurity (SHODLCS) model for OSN. The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.… More >

  • Open Access

    ARTICLE

    Coherence Based Sufficient Condition for Support Recovery Using Generalized Orthogonal Matching Pursuit

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2049-2058, 2023, DOI:10.32604/csse.2023.031566
    Abstract In an underdetermined system, compressive sensing can be used to recover the support vector. Greedy algorithms will recover the support vector indices in an iterative manner. Generalized Orthogonal Matching Pursuit (GOMP) is the generalized form of the Orthogonal Matching Pursuit (OMP) algorithm where a number of indices selected per iteration will be greater than or equal to 1. To recover the support vector of unknown signal ‘x’ from the compressed measurements, the restricted isometric property should be satisfied as a sufficient condition. Finding the restricted isometric constant is a non-deterministic polynomial-time hardness problem due to that the coherence of the… More >

  • Open Access

    ARTICLE

    NOMA with Adaptive Transmit Power Using Intelligent Reflecting Surfaces

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2059-2070, 2023, DOI:10.32604/csse.2023.032610
    Abstract In this article, we use Intelligent Reflecting Surfaces (IRS) to improve the throughput of Non Orthogonal Multiple Access (NOMA) with Adaptive Transmit Power (ATP). The results are valid for Cognitive Radio Networks (CRN) where secondary source adapts its power to generate low interference at primary receiver. In all previous studies, IRS were implemented with fixed transmit power and previous results are not valid when the power of the secondary source is adaptive. In CRN, secondary nodes are allowed to transmit over the same band as primary users since they adapt their power to minimize the generated interference. Each NOMA user… More >

  • Open Access

    ARTICLE

    Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2071-2092, 2023, DOI:10.32604/csse.2023.024110
    Abstract Privacy preservation (PP) in Digital forensics (DF) is a conflicted and non-trivial issue. Existing solutions use the searchable encryption concept and, as a result, are not efficient and support only a keyword search. Moreover, the collected forensic data cannot be analyzed using existing well-known digital tools. This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB) privacy guidelines. To have an efficient investigation process and meet the increased volume of data, the presented framework is designed based on the selective imaging concept and advanced encryption standard (AES). The… More >

  • Open Access

    ARTICLE

    Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2093-2109, 2023, DOI:10.32604/csse.2023.032488
    Abstract Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a… More >

  • Open Access

    ARTICLE

    A Machine Learning Based Funding Project Evaluation Decision Prediction

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2111-2124, 2023, DOI:10.32604/csse.2023.030516
    Abstract Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human mind. In this paper, we apply machine learning to the field of funding allocation decision making, and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision? and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding? Since funding data is characterized by imbalanced data distribution, we propose a slacked weighted entropy decision tree (SWE-DT). We assign weight to each class with the help of slacked factor. The experimental results show… More >

  • Open Access

    ARTICLE

    Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008
    Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

  • Open Access

    ARTICLE

    Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2141-2154, 2023, DOI:10.32604/csse.2023.030984
    Abstract Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to… More >

  • Open Access

    ARTICLE

    Formal Verification Platform as a Service: WebAssembly Vulnerability Detection Application

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2155-2170, 2023, DOI:10.32604/csse.2023.027680
    Abstract In order to realize a general-purpose automatic formal verification platform based on WebAssembly technology as a web service (FVPS), which aims to provide an automated report of vulnerability detections, this work builds a Hyperledger Fabric blockchain runtime model. It proposes an optimized methodology of the functional equivalent translation from source program languages to formal languages. This methodology utilizes an external application programming interface (API) table to replace the source codes in compilation, thereby pruning the part of housekeeping codes to ease code inflation. Code inflation is a significant metric in formal language translation. Namely, minor code inflation enhances verification scale… More >

  • Open Access

    ARTICLE

    Efficiency Performances of LVDC Supplies for Residential Building

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2171-2186, 2023, DOI:10.32604/csse.2023.029389
    Abstract The Low Voltage Direct Current (LVDC) architecture gives higher benefits over the classic low-voltage alternating current (LVAC) supply concept. LVDC has fewer energy conversion stages, is compatible with renewable energy sources, and is easier to integrate with accumulators. In this paper, an LVDC supply concept is proposed and compared with currently used conventional photovoltaic (PV) systems in terms of efficiency. The new LVDC photovoltaic system behavior is validated using LTspice modeling tool. The findings of this work prove that the concept of LVDC supply is highly attractive when the electricity produced by the photovoltaic is used onsite in the daytime.… More >

  • Open Access

    ARTICLE

    Heterogeneous Ensemble Feature Selection Model (HEFSM) for Big Data Analytics

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2187-2205, 2023, DOI:10.32604/csse.2023.031115
    Abstract Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel multiple expert’s judgments on a… More >

  • Open Access

    ARTICLE

    Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2207-2222, 2023, DOI:10.32604/csse.2023.032297
    Abstract A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective weighting method, the commercial housing… More >

  • Open Access

    ARTICLE

    Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2223-2234, 2023, DOI:10.32604/csse.2023.032316
    Abstract Fog computing became a traditional OffLad Destination (OLD) to compute the offloaded tasks of the Internet of Vehicles (IoV). Nevertheless, the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud. Thus, the IoV will incur additional offloading costs. In this paper, we propose a new offloading scheme by utilizing RoadSide Parked Vehicles (RSPV) as an alternative OLD for IoV. The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud. Finally, a performance evaluation of the proposed scheme… More >

  • Open Access

    ARTICLE

    Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2235-2247, 2023, DOI:10.32604/csse.2023.032156
    Abstract Traditional security systems are exposed to many various attacks, which represents a major challenge for the spread of the Internet in the future. Innovative techniques have been suggested for detecting attacks using machine learning and deep learning. The significant advantage of deep learning is that it is highly efficient, but it needs a large training time with a lot of data. Therefore, in this paper, we present a new feature reduction strategy based on Distributed Cumulative Histograms (DCH) to distinguish between dataset features to locate the most effective features. Cumulative histograms assess the dataset instance patterns of the applied features… More >

  • Open Access

    ARTICLE

    EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2249-2263, 2023, DOI:10.32604/csse.2023.032231
    Abstract Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The… More >

  • Open Access

    ARTICLE

    Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2265-2280, 2023, DOI:10.32604/csse.2023.032571
    Abstract Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing… More >

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