Physical Sciences and Engineering

Applications of Artificial Intelligence (AI) across different fields have revolutionized industries, and law is no exception. In India, the judiciary has traditionally acted as the interpretation of the Constitution based on conventional legal axioms and human judgment. But with the advent of AI in the guise of Natural Language Processing (NLP), machine learning (ML), and big data analysis, the trend of interpretation of law will shift. The application of Artificial Intelligence (AI) in different areas has revolutionized the domain of governance, law, and policy-making.

Knowledge acquisition relies on the capacity of the students to read and grasp the meaning of the text. However, diverse factors are impeding students’ ability to comprehend the breadth and depth of the English subjects—reading material—consequently compelling misinterpretation and incompetence in understanding the projected lessons. This study aimed to identify the effectiveness of vocabulary enrichment in improving students’ reading comprehension among the students in Sto. Tomas College Danao City, Inc.

This work consisted of identifying the residues generated in the technological process of wood machining and the techniques of their recovery within the company SICOFOR in Pointe Noire, Republic of Congo. The ridges from the trunks of the trees were used as plant material. Data were collected through direct observation, interview and discussion, and then measurement of parameters. The results indicated that SICOFOR has a good production system because it generates little waste; i.e.

The purpose of this study was to investigate the influence of imagery, concentration, level of experience and playing position on penalty kick performance success among the players of the University of Cape Coast (UCC) Youngsters Football Club (FC). Thirty registered players of UCC Youngsters FC for the 2022/2023 season participated in the study. The players were subjected to the taking of kicks from the penalty spot after imagery and concentration intervention programs.

Ransomware attacks have emerged as a major cybersecurity threat in terms of the massive financial and data losses it has inflicted across the globe. Such attacks cannot easily be detected by traditional detection techniques, including signature-based and rule-based detection, because these are issues that rely heavily on predefined characteristics and static rules for their identification purposes.

Dermatological disorders, and especially skin cancer, are a worldwide health issue. Precise and early diagnosis is critical in order to pursue efficient treatment, and machine learning, in this case Convolutional Neural Networks (CNNs), has tremendous potential in computerizing the diagnostic process. This research suggests a Visual Geometry Group Network (VGGNet) model for dermatological disorder diagnosis, specifically for skin disorders like melanoma, psoriasis, and eczema.

Financial fraud activities are a serious threat to the security and integrity of online banking systems. Traditional fraud detection approaches, such as rule-based and simple machine learning models, are not effective in detecting changing patterns of fraud and suffer from high false positive rates and scalability. To overcome these drawbacks, this research introduces BankSafeNet, a Dual-Autoencoder and Transformer-Based Anomaly Detection System for detecting financial fraud.

Maintenance of performance, reliability, and efficiency of any system within distributed computing environments is central to QoS. Conventional QoS optimization methods confront challenges regarding dynamic resource allocation, failure, and adaptation of systems. To overcome these limitations, a hybrid artificial neural network and Ant Colony Optimization model are proposed to provide an efficient QoS optimization strategy.

Internet of Things networks are proliferating rapidly, and securing document sharing over the cloud presents a significant challenge. Traditional encryption techniques cannot create a compromise between security, efficiency, and scalability. The known encryption techniques such as AES, RSA, and ABE have high computational overheads and their inefficient key management render them unsuitable for larger-scale IoT environments.

The Apology Strategies of Malaysian and Iraqi undergraduate students represent an investigation of cultural norms which affect spoken Apology Speech Acts. The study evaluated apology methods used between Malaysian and Iraqi students while studying the effects of collectivism and hierarchy on these techniques. The research included 120 participants sorted into equal groups of 60 students from Malaysia and Iraq ranging in age from 18 to 23 who studied different academic subjects.