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All Journal JOIV : International Journal on Informatics Visualization
Ishak, Khairul Khalil
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Computer Science & IT

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Adoption of Industry 4.0 with Cloud Computing as a Mediator: Evaluation using TOE Framework for SMEs Abu Bakar, Muhammad Ramzul; Mat Razali, Noor Afiza; Ishak, Khairul Khalil; Ismail, Mohd Nazri; Tengku Sembok, Tengku Mohd
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2205

Abstract

Industry 4.0 represents a significant shift in production processes, necessitating the integration of humans, products, information, and robots into digitalized workflows. While this transformation offers numerous benefits, its adoption, particularly among small and medium enterprises (SMEs), is hindered by various challenges such as financial constraints, maintenance costs, and a lack of digital culture and awareness. This study examines the adoption of Industry 4.0, specifically through cloud computing technologies, within the manufacturing and service sectors of SMEs in Malaysia. Cloud computing is economical, straightforward, and easily implemented for SMEs. We propose a conceptual model based on an extended Technology-Organisation-Environment (TOE) model, integrating refined constructs and considering digital organizational culture as a moderator, with cloud computing acting as a mediator to enhance firm performance. The study investigates the relationship between these constructs and addresses overlooked factors influencing adoption. Utilizing a structured questionnaire with 54 items derived from previous research, we employ partial least squares structural equation modeling (PLS-SEM) to analyze data collected from a pilot study. Our findings confirm the reliability and validity of the proposed conceptual model, meeting established criteria for composite reliability, average variance extracted (AVE), Cronbach's alpha, and discriminant validity (HTMT Criterion). Furthermore, this study presents empirical findings on technological, organizational, and environmental influences on adopting cloud computing. The insights gained from this research offer valuable guidance to enhance the performance of SMEs in the Industry 4.0 landscape.
A Review on Deep Learning Approaches and Optimization Techniques For Political Security Threat Prediction Zaabar, Liyana Safra; Mat Razali, Noor Afiza; Ishak, Khairul Khalil; Abdullah, Nor Asiakin; Wook, Muslihah
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2204

Abstract

In an era of complex geopolitical dynamics and evolving security threats, the accurate prediction and proactive management of political security risks are imperative. This article provides a comprehensive review of the application of deep learning methodologies and optimization techniques to enhance political security threat prediction. Beginning with analyzing the dynamic landscape of political security threats, the paper emphasizes the necessity for adaptive, data-driven predictive tools. It then delves into the fundamentals of deep learning, elucidating core principles, notable architectural frameworks, and their diverse applications across domains. Expanding upon this foundation, the study evaluates the suitability of deep learning models for addressing the multifaceted challenges associated with political security threat prediction. To maximize the utility of these models, the article explores optimization techniques encompassing hyperparameter tuning, transfer learning, and ensemble strategies, assessing their effectiveness in fine-tuning predictions and bolstering the resilience of threat prediction systems. This review involved the utilization of four journal databases: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. We analyzed and examined 39 articles, paying close attention to the different patterns and techniques found within the chosen research framework. Through a critical synthesis of existing research, this review offers insights into the strengths, limitations, and future directions of deep learning-based political security threat prediction, contributing to the ongoing discourse on leveraging artificial intelligence for safeguarding global stability and security.
IoT Attack Detection using Machine Learning and Deep Learning in Smart Home S Azli Sham, Sharifah Nabila; Ishak, Khairul Khalil; Mat Razali, Noor Afiza; Mohd Noor, Normaizeerah; Hasbullah, Nor Asiakin
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2174

Abstract

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation.  However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.
Co-Authors Abdullah, Nor Asiakin Abu Bakar, Muhammad Ramzul Mat Razali, Noor Afiza Mohd Nazri Ismail, Mohd Nazri Mohd Noor, Normaizeerah Nor Asiakin Hasbullah S Azli Sham, Sharifah Nabila Tengku Sembok, Tengku Mohd Wook, Muslihah Zaabar, Liyana Safra