Home / Journals / CSSE / Vol.44, No.3, 2023
  • Open Access

    ARTICLE

    Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

    S. Siamala Devi1, K. Venkatachalam2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1869-1880, 2023, DOI:10.32604/csse.2023.025505
    Abstract The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message… More >

  • Open Access

    ARTICLE

    Towards Developing Privacy-Preserved Data Security Approach (PP-DSA) in Cloud Computing Environment

    S. Stewart Kirubakaran1,*, V. P. Arunachalam1, S. Karthik1, S. Kannan2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1881-1895, 2023, DOI:10.32604/csse.2023.026690
    Abstract In the present scenario of rapid growth in cloud computing models, several companies and users started to share their data on cloud servers. However, when the model is not completely trusted, the data owners face several security-related problems, such as user privacy breaches, data disclosure, data corruption, and so on, during the process of data outsourcing. For addressing and handling the security-related issues on Cloud, several models were proposed. With that concern, this paper develops a Privacy-Preserved Data Security Approach (PP-DSA) to provide the data security and data integrity for the outsourcing data in Cloud Environment. Privacy preservation is ensured… More >

  • Open Access

    ARTICLE

    Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

    R. Vijay Anandh1,*, S. Rukmani Devi2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1897-1910, 2023, DOI:10.32604/csse.2023.027187
    Abstract The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected to two main processes namely,… More >

  • Open Access

    ARTICLE

    Cooperative Relay Networks Based on the OAM Technique for 5G Applications

    Mohammad Alkhawatrah, Ahmad Alamayreh, Nidal Qasem*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1911-1919, 2023, DOI:10.32604/csse.2023.028614
    Abstract Orbital Angular Momentum (OAM) is an intrinsic property of electromagnetic waves. Great research has been witnessed in the last decades aiming at exploiting the OAM wave property in different areas in radio and optics. One promising area of particular interest is to enhance the efficiency of the available communications spectrum. However, adopting OAM-based solutions is not priceless as these suffer from wave divergence especially when the OAM order is high. This shall limit the practical communications distance, especially in the radio regime. In this paper, we propose a cooperative OAM relaying system consisting of a source, relay, and destination. Relays… More >

  • Open Access

    ARTICLE

    A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

    Anas Basalamah1, Mahedi Hasan2, Shovan Bhowmik2, Shaikh Akib Shahriyar2,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1921-1938, 2023, DOI:10.32604/csse.2023.027399
    Abstract The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict… More >

  • Open Access

    ARTICLE

    Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

    Dhanalakshmi R1, Sovan Samanta2, Arun Kumar Sivaraman3, Jeong Gon Lee4,*, Balasundaram A5, Sanamdikar Sanjay Tanaji6, Priya Ravindran7
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1939-1950, 2023, DOI:10.32604/csse.2023.026254
    Abstract Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov’s intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy… More >

  • Open Access

    ARTICLE

    Availability Capacity Evaluation and Reliability Assessment of Integrated Systems Using Metaheuristic Algorithm

    A. Durgadevi*, N. Shanmugavadivoo
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1951-1971, 2023, DOI:10.32604/csse.2023.026810
    Abstract

    Contemporarily, the development of distributed generations (DGs) technologies is fetching more, and their deployment in power systems is becoming broad and diverse. Consequently, several glitches are found in the recent studies due to the inappropriate/inadequate penetrations. This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system. The assessment procedure is carried out using MATLAB software and Modified Salp Swarm Algorithm (MSSA) that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations. This algorithm modifies two control… More >

  • Open Access

    ARTICLE

    Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

    Aishah Alrashidi, Ali Mustafa Qamar*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1973-1988, 2023, DOI:10.32604/csse.2023.024633
    Abstract Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for Atomic and Renewable Energy and… More >

  • Open Access

    ARTICLE

    Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

    Kandasamy Kittusamy*, Latha Shanmuga Vadivu Sampath Kumar
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1989-2005, 2023, DOI:10.32604/csse.2023.026501
    Abstract Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and Joint Sparse Representation (JSR). This… More >

  • Open Access

    ARTICLE

    Proof-of-Improved-Participation: A New Consensus Protocol for Blockchain Technology

    N. Anita*, M. Vijayalakshmi, S. Mercy Shalinie
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2007-2018, 2023, DOI:10.32604/csse.2023.025516
    Abstract The Internet of Things (IoT) is converting today’s physical world into a complex and sophisticated network of connected devices on an enormous scale. The existing malicious node detection mechanism in traditional approaches lacks in transparency, availability, or traceability of the detection phase. To overcome these concerns, we provide a decentralized technique using blockchain technology. Despite the fact that blockchain technology is applicable to create that type of models, existing harmony set of instructions are susceptible to do violence to such as DoS and Sybil, making blockchain systems unfeasible. Here, a new Proof-of-Improved-Participation (PoIP) harmony instruction was suggested that benefits the… More >

  • Open Access

    ARTICLE

    Process Discovery and Refinement of an Enterprise Management System

    Faizan Ahmed Khan1, Farooq Ahmad1, Arfat Ahmad Khan2, Chitapong Wechtaisong2,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2019-2032, 2023, DOI:10.32604/csse.2023.023490
    Abstract The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By… More >

  • Open Access

    ARTICLE

    Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

    P. Nalayini1,*, R. Arun Prakash2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.028269
    Abstract Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user… More >

  • Open Access

    ARTICLE

    Face Templates Encryption Technique Based on Random Projection and Deep Learning

    Mayada Tarek1,2,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2049-2063, 2023, DOI:10.32604/csse.2023.027139
    Abstract Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The… More >

  • Open Access

    ARTICLE

    An Ontology Based Multilayer Perceptron for Object Detection

    P. D. Sheena Smart1,*, K. K. Thanammal2, S. S. Sujatha2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2065-2080, 2023, DOI:10.32604/csse.2023.028053
    Abstract In object detection, spatial knowledge assisted systems are effective. Object detection is a main and challenging issue to analyze object-related information. Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification. But these techniques still face, less computational efficiency and high time consumption. This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time. The proposed method uses a multilayer for finding the similarity score. A fuzzy membership function is used to validate… More >

  • Open Access

    ARTICLE

    Prediction Model for a Good Learning Environment Using an Ensemble Approach

    S. Subha1,*, S. Baghavathi Priya2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2081-2093, 2023, DOI:10.32604/csse.2023.028451
    Abstract This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is used to validate the… More >

  • Open Access

    ARTICLE

    An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

    Khalil Hamdi Ateyeh Al-Shqeerat*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2095-2107, 2023, DOI:10.32604/csse.2023.028975
    Abstract Most remote systems require user authentication to access resources. Text-based passwords are still widely used as a standard method of user authentication. Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security. One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users. Graphical user authentication methods have recently been proposed to verify the user identity. However, the fundamental limitation of a graphical password is that it must have a colorful… More >

  • Open Access

    ARTICLE

    Tracking Pedestrians Under Occlusion in Parking Space

    Zhengshu Zhou1,*, Shunya Yamada2, Yousuke Watanabe2, Hiroaki Takada1,2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2109-2127, 2023, DOI:10.32604/csse.2023.029005
    Abstract Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians’ sudden appearance in the vehicle’s path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision… More >

  • Open Access

    ARTICLE

    Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

    V. Thanikachalam1,*, M. G. Kavitha2, V. Sivamurugan1
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2129-2145, 2023, DOI:10.32604/csse.2023.026527
    Abstract In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique… More >

  • Open Access

    ARTICLE

    Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

    V. Joseph Raymond1,2,*, R. Jeberson Retna Raj1
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2147-2163, 2023, DOI:10.32604/csse.2023.028227
    Abstract Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of… More >

  • Open Access

    ARTICLE

    Algorithms for Pre-Compiling Programs by Parallel Compilers

    Fayez AlFayez*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2165-2176, 2023, DOI:10.32604/csse.2023.026238
    Abstract The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the… More >

  • Open Access

    ARTICLE

    A Secure Hardware Implementation for Elliptic Curve Digital Signature Algorithm

    Mouna Bedoui1,*, Belgacem Bouallegue1,2, Abdelmoty M. Ahmed2, Belgacem Hamdi1,3, Mohsen Machhout1, Mahmoud1, M. Khattab2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2177-2193, 2023, DOI:10.32604/csse.2023.026516
    Abstract Since the end of the 1990s, cryptosystems implemented on smart cards have had to deal with two main categories of attacks: side-channel attacks and fault injection attacks. Countermeasures have been developed and validated against these two types of attacks, taking into account a well-defined attacker model. This work focuses on small vulnerabilities and countermeasures related to the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm. The work done in this paper focuses on protecting the ECDSA algorithm against fault-injection attacks. More precisely, we are interested in the countermeasures of scalar multiplication in the body of the elliptic curves to protect against… More >

  • Open Access

    ARTICLE

    Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

    Ashit Kumar Dutta1,*, Nazik M. A. Zakari2, Yasser Albagory3, Abdul Rahaman Wahab Sait4
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2195-2207, 2023, DOI:10.32604/csse.2023.026461
    Abstract Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify… More >

  • Open Access

    ARTICLE

    Paillier Cryptography Based Message Authentication Code for IoMT Security

    S. Siamala Devi1, Chandrakala Kuruba2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2209-2223, 2023, DOI:10.32604/csse.2023.025514
    Abstract Health care visualization through Internet of Things (IoT) over wireless sensor network (WSN) becomes a current research attention due to medical sensor evolution of devices. The digital technology-based communication system is widely used in all application. Internet of medical thing (IoMT) assisted healthcare application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation. These smart medical devices may consume with limited resources and also the data generated by these devices are large in size. These IoMT based applications suffer from the issues such as security, anonymity, privacy,… More >

  • Open Access

    ARTICLE

    FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

    Prasant Ku. Dash1, Lopamudra Hota2, Madhumita Panda3, N. Z. Jhanjhi4,*, Kshira Sagar Sahoo5, Mehedi Masud6
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2225-2242, 2023, DOI:10.32604/csse.2023.024836
    Abstract The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the… More >

  • Open Access

    ARTICLE

    Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

    B. Kalpana1, S. Dhanasekaran2, T. Abirami3, Ashit Kumar Dutta4, Marwa Obayya5, Jaber S. Alzahrani6, Manar Ahmed Hamza7,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2243-2257, 2023, DOI:10.32604/csse.2023.027129
    Abstract Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the… More >

  • Open Access

    ARTICLE

    Early Skin Disease Identification Using eep Neural Network

    Vinay Gautam1, Naresh Kumar Trivedi1, Abhineet Anand1, Rajeev Tiwari2,*, Atef Zaguia3, Deepika Koundal4, Sachin Jain5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2259-2275, 2023, DOI:10.32604/csse.2023.026358
    Abstract Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have… More >

  • Open Access

    ARTICLE

    Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

    Ashit Kumar Dutta1,*, Mazen Mushabab Alqahtani2, Yasser Albagory3, Abdul Rahaman Wahab Sait4, Majed Alsanea5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2277-2292, 2023, DOI:10.32604/csse.2023.028107
    Abstract Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The… More >

  • Open Access

    ARTICLE

    Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

    B. Murugeshwari1,*, S. Rajalakshmi1, K. Sudharson2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074
    Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while… More >

  • Open Access

    ARTICLE

    Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

    Mahmoud Ragab1,2,3,*, Jaber Alyami4,5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877
    Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent… More >

  • Open Access

    ARTICLE

    An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

    N. Vanitha1,*, C. R. Rene Robin1, D. Doreen Hephzibah Miriam2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2323-2336, 2023, DOI:10.32604/csse.2023.028309
    Abstract Abstract: Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the… More >

  • Open Access

    ARTICLE

    Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET

    S. A. Arunmozhi*, S. Rajeswari, Y. Venkataramani
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2337-2347, 2023, DOI:10.32604/csse.2023.024340
    Abstract Mobile Ad hoc Network (MANET) possesses unique characteristics which makes it vulnerable to security threats. In MANET, it is highly challenging to protect the nodes from cyberattacks. Power conservation improves both life time of nodes as well as the network. Computational capabilities and memory constraints are critical issues in the implementation of cryptographic techniques. Energy and security are two important factors that need to be considered for improving the performance of MANET. So, the incorporation of an energy efficient secure routing protocol becomes inevitable to ensure appropriate action upon the network. The nodes present in a network are limited due… More >

  • Open Access

    ARTICLE

    Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

    I. Vallirathi1,*, S. Ebenezer Juliet2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2349-2360, 2023, DOI:10.32604/csse.2023.024864
    Abstract Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node… More >

  • Open Access

    ARTICLE

    Anomaly Detection for Industrial Internet of Things Cyberattacks

    Rehab Alanazi*, Ahamed Aljuhani
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2361-2378, 2023, DOI:10.32604/csse.2023.026712
    Abstract The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively… More >

  • Open Access

    ARTICLE

    Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

    S. Rameshkumar1,*, R. Ganesan2, A. Merline1
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2379-2394, 2023, DOI:10.32604/csse.2023.027910
    Abstract In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power… More >

  • Open Access

    ARTICLE

    Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

    Ashit Kumar Dutta1,*, Basit Qureshi2, Yasser Albagory3, Majed Alsanea4, Manal Al Faraj1, Abdul Rahaman Wahab Sait5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2395-2409, 2023, DOI:10.32604/csse.2023.027502
    Abstract Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is… More >

  • Open Access

    ARTICLE

    Future Event Prediction Based on Temporal Knowledge Graph Embedding

    Zhipeng Li1,2, Shanshan Feng3,*, Jun Shi2, Yang Zhou2, Yong Liao1,2, Yangzhao Yang2, Yangyang Li4, Nenghai Yu1, Xun Shao5
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2411-2423, 2023, DOI:10.32604/csse.2023.026823
    Abstract Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs… More >

  • Open Access

    ARTICLE

    CNTFET Based Fully Differential First Order All Pass Filter

    Muhammad I. Masud*, Iqbal A. Khan
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2425-2438, 2023, DOI:10.32604/csse.2023.027570
    Abstract A novel, carbon nanotube field effect transistor (CNTFET) based fully differential first order all pass filter (FDFAPF) circuit configuration is presented. The FDFAPF uses CNTFET based negative transconductors (NTs) and positive transconductors (PTs) in its realization. The proposed circuit topology employs two PTs, two NTs, two resistors and one capacitor. All the passive components of the realized topology are grounded. Active only fully differential first order all pass filter (AO-FDFAPF) topology is also derived from the proposed FDFAPF. The electronic tunability of the AO-FDFAPF is obtained by controlling the employed CNTFET based varactor. A tunabilty of pole frequency in the… More >

  • Open Access

    ARTICLE

    SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

    C. P. Thamil Selvi1,*, R. PushpaLakshmi2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2439-2456, 2023, DOI:10.32604/csse.2023.029254
    Abstract One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data… More >

  • Open Access

    ARTICLE

    An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

    Mahesh B. Shelke1, Jeong Gon Lee2,*, Sovan Samanta3, Sachin N. Deshmukh1, G. Bhalke Daulappa4, Rahul B. Mannade5, Arun Kumar Sivaraman6
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2457-2468, 2023, DOI:10.32604/csse.2023.027979
    Abstract In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative,… More >

  • Open Access

    ARTICLE

    Design of Online Vitals Monitor by Integrating Big Data and IoT

    E. Afreen Banu1,*, V. Rajamani2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2469-2487, 2023, DOI:10.32604/csse.2023.021332
    Abstract In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min… More >

  • Open Access

    ARTICLE

    An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

    Saranya Veerapaulraj1,*, M. Karthikeyan1, S. Sasipriya2, A. S. Shanthi1
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2489-2500, 2023, DOI:10.32604/csse.2023.026463
    Abstract Routing strategies and security issues are the greatest challenges in Wireless Sensor Network (WSN). Cluster-based routing Low Energy adaptive Clustering Hierarchy (LEACH) decreases power consumption and increases network lifetime considerably. Securing WSN is a challenging issue faced by researchers. Trust systems are very helpful in detecting interfering nodes in WSN. Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem. The metaheuristic Elephant Herding Optimizations (EHO) algorithm is formulated based on elephant herding in their clans. EHO considers two herding behaviors to solve and enhance optimization… More >

  • Open Access

    ARTICLE

    Secure e-Prescription Management System: Mitigating Blended Threat in IoBE

    Deukhun Kim1, Heejin Kim2, Jin Kwak3,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2501-2519, 2023, DOI:10.32604/csse.2023.029356
    Abstract New information and communication technologies (ICT) are being applied in various industries to upgrade the value of the major service items. Moreover, data collection, storage, processing, and security applications have led to the creation of an interrelated ICT environment in which one industry can directly influence the other. This is called the “internet of blended environments” (IoBE), as it is an interrelated data environment based on internet-of-things collection activities. In this environment, security incidents may increase as size and interconnectivity of attackable operations grow. Consequently, preemptive responses to combined security threats are needed to securely utilize IoBE across industries. For… More >

  • Open Access

    ARTICLE

    Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning

    Abu Saleh Musa Miah1, Jungpil Shin1,*, Md. Al Mehedi Hasan1, Md Abdur Rahim2, Yuichi Okuyama1
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2521-2536, 2023, DOI:10.32604/csse.2023.029336
    Abstract Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition, we proposed a Rotation, Translation… More >

  • Open Access

    ARTICLE

    Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

    K. Selvasheela1,*, A. M. Abirami2, Abdul Khader Askarunisa3
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148
    Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review.… More >

  • Open Access

    ARTICLE

    Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

    Abdul Rahaman Wahab Sait1,*, Mohamad Khairi Ishak2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.029603
    Abstract Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA)… More >

  • Open Access

    ARTICLE

    Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

    T. Jagadesh1,2, B. Sheela Rani3,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2569-2583, 2023, DOI:10.32604/csse.2023.027239
    Abstract RSs (Radar Systems) identify and trace targets and are commonly employed in applications like air traffic control and remote sensing. They are necessary for monitoring precise target trajectories. Estimations of RSs are non-linear as the parameters TDEs (time delay Estimations) and Doppler shifts are computed on receipt of echoes where EKFs (Extended Kalman Filters) and UKFs (Unscented Kalman Filters) have not been examined for computations. RSs, certain times result in poor accuracies and SNRs (low signal to noise ratios) especially, while encountering complicated environments. This work proposes IUKFs (Iterated UKFs) to track online filter performances while using optimization techniques to… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

    V. Nirmala1,*, B. Gomathy2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512
    Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

    Vani A. Hiremani*, Kishore Kumar Senapati
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2603-2618, 2023, DOI:10.32604/csse.2023.027612
    Abstract The inter-class face classification problem is more reasonable than the intra-class classification problem. To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions. This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision. We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities. Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features. We have developed a modified convolutional neural network to characterize the… More >

  • Open Access

    ARTICLE

    Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

    J. Shanthini1, P. Punitha2,*, S. Karthik2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2619-2636, 2023, DOI:10.32604/csse.2023.027330
    Abstract In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating… More >

  • Open Access

    ARTICLE

    The Laplacian Energy of Hesitancy Fuzzy Graphs in Decision-Making Problems

    N. Rajagopal Reddy1, Mohammad Zubair Khan2, S. Sharief Basha3, Abdulrahman Alahmadi2, Ahmed H. Alahmadi2, Chiranji Lal Chowdhary4,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2637-2653, 2023, DOI:10.32604/csse.2023.029255
    Abstract Decision-making (DM) is a process in which several persons concurrently engage, examine the problems, evaluate potential alternatives, and select an appropriate option to the problem. Technique for determining order preference by similarity to the ideal solution (TOPSIS) is an established DM process. The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data, in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices, each of which is defined by Hesitancy fuzzy numbers. Findings: we represent analytical results, such as designing a… More >

  • Open Access

    ARTICLE

    Triplet Label Based Image Retrieval Using Deep Learning in Large Database

    K. Nithya1,*, V. Rajamani2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2655-2666, 2023, DOI:10.32604/csse.2023.027275
    Abstract Recent days, Image retrieval has become a tedious process as the image database has grown very larger. The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable. In these, the pair-wise label similarity is used to find the matching images from the database. But this method lacks of limited propose code and weak execution of misclassified images. In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed. A Point Attention Based Triplet Network (PABTN) is introduced to study propose code that gives maximum discriminative ability.… More >

  • Open Access

    ARTICLE

    Effective Denoising Architecture for Handling Multiple Noises

    Na Hyoun Kim, Namgyu Kim*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2667-2682, 2023, DOI:10.32604/csse.2023.029732
    Abstract Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we propose a convolutional denoising autoencoder… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

    S. Prabhakaran1,*, R. Annie Uthra1, J. Preetharoselyn2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2683-2700, 2023, DOI:10.32604/csse.2023.028898
    Abstract The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

    Ashit Kumar Dutta1,*, T. Meyyappan2, Basit Qureshi3, Majed Alsanea4, Anas Waleed Abulfaraj5, Manal M. Al Faraj1, Abdul Rahaman Wahab Sait6
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2701-2713, 2023, DOI:10.32604/csse.2023.028984
    Abstract Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODL-PEDC model is to distinguish… More >

  • Open Access

    ARTICLE

    A Novel Segment White Matter Hyperintensities Approach for Detecting Alzheimer

    Antonitta Eileen Pious1,*, U. K. Sridevi2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2715-2726, 2023, DOI:10.32604/csse.2023.026582
    Abstract Segmentation has been an effective step that needs to be done before the classification or detection of an anomaly like Alzheimer’s on a brain scan. Segmentation helps detect pixels of the same intensity or volume and group them together as one class or region, where in that particular region of interest (ROI) can be concentrated on, rather than focusing on the entire image. In this paper White Matter Hyperintensities (WMH) is taken as a strong biomarker that supports and determines the presence of Alzheimer’s. As the first step a proper segmentation of the lesions has to be carried out. As… More >

  • Open Access

    ARTICLE

    Regularised Layerwise Weight Norm Based Skin Lesion Features Extraction and Classification

    S. Gopikha*, M. Balamurugan
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2727-2742, 2023, DOI:10.32604/csse.2023.028609
    Abstract Melanoma is the most lethal malignant tumour, and its prevalence is increasing. Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people. Recently, deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction. A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data. As a result, overfitting and poor performance occur. To deal with this issue, we proposed a novel Consecutive Layerwise weight Constraint MaxNorm model (CLCM-net)… More >

  • Open Access

    ARTICLE

    Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

    Saleh Albahli1,*, Ghulam Nabi Ahmad Hassan Yar2,3
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2743-2757, 2023, DOI:10.32604/csse.2023.024463
    Abstract Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined; COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For… More >

  • Open Access

    ARTICLE

    Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

    R. Punithavathi1, A. Delphin Carolina Rani2, K. R. Sughashini3, Chinnarao Kurangi4, M. Nirmala5, Hasmath Farhana Thariq Ahmed6, S. P. Balamurugan7,*
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2759-2774, 2023, DOI:10.32604/csse.2023.027647
    Abstract Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the… More >

  • Open Access

    ARTICLE

    Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

    S. Neelavathy Pari1,*, K. Sudharson2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2775-2789, 2023, DOI:10.32604/csse.2023.029345
    Abstract A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network (MANET). A MANET’s nodes could engage actively and dynamically with one another. However, MANETs, from the other side, are exposed to severe potential threats that are difficult to counter with present security methods. As a result, several safe communication protocols designed to enhance the secure interaction among MANET nodes. In this research, we offer a reputed optimal routing value among network nodes, secure computations, and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism (HTRM). In addition,… More >

  • Open Access

    ARTICLE

    Gamma Correction for Brightness Preservation in Natural Images

    Navleen S Rekhi1,2,*, Jagroop S Sidhu2, Amit Arora2
    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2791-2807, 2023, DOI:10.32604/csse.2023.026976
    Abstract Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale… More >

Share Link

WeChat scan