Zuriati Ahmad Zukarnain^1,*, Amgad Muneer^2,3, Nur Atirah Mohamad Nassir¹, Akram A. Almohammedi^4,5

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2725-2740, 2023, DOI:10.32604/csse.2023.034448

Abstract With the advancements in the era of artificial intelligence, blockchain, cloud computing, and big data, there is a need for secure, decentralized medical record storage and retrieval systems. While cloud storage solves storage issues, it is challenging to realize secure sharing of records over the network. Medi-block record in the healthcare system has brought a new digitalization method for patients’ medical records. This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital. It enables electronic medical records to be available with the correct authentication and restricts… More >

Mesfer Al Duhayyim^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Abdullah Mohamed⁵, Ishfaq Yaseen⁶, Gouse Pasha Mohammed⁶, Mohammed Rizwanullah⁶, Abu Sarwar Zamani⁶

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3049-3065, 2023, DOI:10.32604/csse.2023.033836

Abstract Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions… More >

R. Ani^1,*, O. S. Deepa², B. R. Manju¹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3033-3048, 2023, DOI:10.32604/csse.2023.033807

Abstract The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein. The use of virtual screening in pharmaceutical research is growing in popularity. During the early phases of medication research and development, it is crucial. Chemical compound searches are now more narrowly targeted. Because the databases contain more and more ligands, this method needs to be quick and exact. Neural network fingerprints were created more effectively than the well-known… More >

Safdar Khan¹, Muhammad Attique Khan², Jamal Hussain Shah^1,*, Faheem Shehzad², Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410

Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The… More >

Yousif Sufyan Jghef¹, Mohammed Jasim Mohammed Jasim², Subhi R. M. Zeebaree^3,*, Rizgar R. Zebari⁴

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1651-1664, 2023, DOI:10.32604/csse.2023.037449

Abstract Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To do so, the presented BIOMDL-FD… More >

Ishtiaque Mahmood¹, Tahir Alyas², Sagheer Abbas³, Tariq Shahzad⁴, Qaiser Abbas^5,6, Khmaies Ouahada^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2439-2453, 2023, DOI:10.32604/csse.2023.033842

Abstract Attacks on fully integrated servers, apps, and communication networks via the Internet of Things (IoT) are growing exponentially. Sensitive devices’ effectiveness harms end users, increases cyber threats and identity theft, raises costs, and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods. Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security. Following the 1, 2, 3, and 4G cellular networks, the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular… More >

Okba Taouali^1,*, Sawcen Bacha², Khaoula Ben Abdellafou¹, Ahamed Aljuhani¹, Kamel Zidi³, Rehab Alanazi¹, Mohamed Faouzi Harkat⁴

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1593-1609, 2023, DOI:10.32604/csse.2023.039984

Abstract Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to effectively combat a diverse range… More >

Popuri Srinivasarao, Aravapalli Rama Satish^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1527-1547, 2023, DOI:10.32604/csse.2023.038951

Abstract Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs. The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique (ECHT). The constraints such as self-adaptive penalty, epsilon (ε)-parameter and stochastic… More >

Saleh Ateeq Almutairi^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

Liming Ye^1,2, Gang Liu^1,2,3,4,*, Genshen Chen^1,2, Kang Li^1,2, Qiyu Chen^1,2,3, Wenyao Fan^1,2, Junjie Zhang^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2529-2545, 2023, DOI:10.32604/csse.2023.041488

Abstract Nowadays, Web browsers have become an important carrier of 3D model visualization because of their convenience and portability. During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS (Frames Per Second), occlusion culling, as an important method for rendering optimization, can remove most of the occluded objects and improve rendering efficiency. The traditional occlusion culling algorithm (TOCA) is calculated by traversing all objects in the scene, which involves a large amount of repeated calculation and time consumption. To advance the rendering process and enhance rendering efficiency, this paper… More >