مطالب

The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest.

Generating dynamic videos from static images and accurately modeling object motion within scenes are fundamental challenges in computer vision, with broad applications in video enhancement, photo animation, and visual scene understanding. This paper proposes a novel hybrid framework that combines convolutional neural networks (CNNs), recurrent neural networks (RNNs) with long short-term memory (LSTM) units, and generative adversarial networks (GANs) to synthesize temporally consistent and spatially realistic video sequences from still images. The architecture incorporates splicing techniques, the Lucas-Kanade motion estimation algorithm, and a loop feedback mechanism to address key limitations of existing approaches, including motion instability, temporal noise, and degraded video quality over time. CNNs extract spatial features, LSTMs model temporal dynamics, and GANs enhance visual realism through adversarial training. Experimental results on the KTH dataset, comprising 600 videos of fundamental human actions, demonstrate that the proposed method achieves substantial improvements over baseline models, reaching a peak PSNR of 35.8 and SSIM of 0.96—representing a 20% performance gain. The model successfully generates high-quality, 10-second videos at a resolution of 720×1280 pixels with significantly reduced noise, confirming the effectiveness of the integrated splicing and feedback strategy for stable and coherent video generation.

Online reviews are crucial in influencing consumer decisions and business practices. However, some individuals exploit this system by posting fake reviews, known as spam opinions, to manipulate perceptions. Spam detection systems face significant challenges in robustness due to their primary focus on identifying spam attacks without accounting for adversaries that target the detection mechanisms. This oversight enables spammers to exploit vulnerabilities in traditional algorithms with complex deceptive strategies, ultimately undermining their effectiveness. This paper proposes a novel multi-layer graph-based method that represents reviews, reviewers, and products as interconnected nodes. This approach captures the complex relationships among them and addresses adversarial attempts to manipulate the detection process. Our approach utilizes three key nodes—opinion, reviewer, and product—to assess the honesty, trust, and reliability of reviews, reviewers, and products in the context of potential deception. Furthermore, we develop a simulation tool capable of generating diverse attack scenarios, including those targeting the detection system itself, enabling a comprehensive evaluation of robustness. We compared the performance of our method with other graph-based techniques through simulations and case studies, demonstrating that our method is a competitive solution among existing alternatives.

Background: The rapid growth of computer technology in recent decades suggests that people today, in addition to print media, have many electronic information items that need to be managed appropriately and have increased the use of personal computers.Aims: This Researches descriptive and analytical based on the purpose of applied Research, and in terms of data collection and nature and comparison between the activities of personal information management and two measures of knowledge and skills has been presented.Methodology: The statistical population includes the students of Jundishapur University of Medical Sciences in Ahvaz in the academic year 2017-2018. To determine the statistical population, the sampling method was used by determining the total number of students 1300 and using the Karjesi-Morgan table, the approximate number of the sample was determined to be 297. The data collection was done using the questionnaire of 2012 and in order to analyze Pearson and Spearman correlation tests and AMOS and SPSS software were used.Results: The results of the survey show that knowledge and skill variables have a significant and positive effect on personal information management, and age, level of education, and school of study have a significant and positive effect on personal information management.Conclusions: The results of the research show that the status of personal information management of medical students in Jundishapur Ahvaz has a positive and meaningful effect based on the knowledge and skills of the students. According to the changes made in the management of personal information activities, it can be said that the difference in the amount and amount created in the variables of personal information management can be seen. The main reasons for this can include not having enough time, work pressures and lack of familiarity with the activities required for personal information management.

Background: Understanding color preference in virtual environments is crucial for applications in digital design, human-computer interaction, and virtual reality (VR).Aims: This study examines how luminance, hue, and saturation influence color preference in VR settings, considering both environmental and perceptual factors.Methodology: A controlled VR experiment was used, where participants interacted with two distinct virtual zones designed to simulate different lighting conditions.Finding: The findings suggest that chromatic lightness and perceived hue play distinct roles in color preference, with evidence supporting Weber's Law of illumination adaptation. It was also shown that regions with elevated chroma exhibit more pronounced colors. Additionally, the participants' average color preferences were determined, and the appropriate modification rate was extracted by comparing the preferred colors to the average colors of the virtual spaces. One significant finding was that, cooler colors were favored to warmer ones, which is consistent with previous research on color preferences. Furthermore, a correlation between lighting circumstances and color preferences was established.Conclusion: The findings indicated that adjusting the hue, saturation, and brightness can improve the design of virtual environments by matching the tastes of users. These insights contribute to a deeper understanding of color perception in digital spaces and have implications for design, architecture, and cognitive science.