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All Journal World Psychology Journal of Moeslim Research Technik Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal of Humanities Research Sustainability
Vann Sok
Pannasastra University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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 1 
Development of Machine Learning Algorithms for Anomaly Detection in Internet of Things (IoT) Networks Vicheka Rith; Vann Sok; Arnes Yuli Vandika
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1560

Abstract

The proliferation of Internet of Things (IoT) devices has increased the vulnerability of networks to security threats, making anomaly detection essential for maintaining system integrity. Traditional security measures often fall short in identifying and mitigating complex attack patterns that can jeopardize IoT networks. This research aims to develop a machine learning algorithm specifically designed for anomaly detection in IoT environments. The goal is to enhance the ability to identify unusual behavior indicative of potential security breaches while minimizing false positives. A dataset comprising network traffic from various IoT devices was collected and preprocessed to extract relevant features. Several machine learning algorithms, including decision trees, support vector machines, and neural networks, were implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were used to assess the effectiveness of each model. The results indicated that the proposed machine learning algorithm outperformed traditional methods, achieving an accuracy of 95% in detecting anomalies. The model demonstrated a significant reduction in false positives compared to existing techniques, thereby enhancing the reliability of anomaly detection in IoT networks. The research concludes that the developed machine learning algorithm is a robust solution for detecting anomalies in IoT environments. This advancement contributes to the field by providing an effective tool for improving security measures in the rapidly evolving landscape of IoT. Future work should focus on real-time implementation and further optimization of the algorithm to adapt to dynamic network conditions.
Analysis of factors that influence student creativity in solving mathematical problems Vicheka Rith; Vann Sok; Ravi Dara
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i4.1707

Abstract

Creativity in solving mathematical problems is a critical skill for students, enabling them to think innovatively and apply knowledge in diverse contexts. However, the development of mathematical creativity is influenced by various factors, including cognitive, environmental, and instructional aspects. Understanding these factors is essential to designing effective strategies to foster creativity in mathematics education. Despite its importance, there is limited research exploring the interplay of these factors in influencing student creativity. This study aims to analyze the factors that influence student creativity in solving mathematical problems and determine which factors have the most significant impact. A mixed-method approach was employed, involving 150 high school students from three schools. Data were collected using a creativity assessment test, a questionnaire on cognitive and environmental factors, and semi-structured interviews. Quantitative data were analyzed using regression analysis, while qualitative data were subjected to thematic analysis. The findings revealed that cognitive factors, such as critical thinking and prior knowledge, were the strongest predictors of mathematical creativity. Environmental factors, including classroom climate and teacher support, also played a significant role. Instructional methods, particularly problem-based learning, were found to enhance creativity by encouraging exploration and independent thinking. The study highlights the multifaceted nature of mathematical creativity and the need for comprehensive strategies that address cognitive, environmental, and instructional factors to foster creativity in mathematics education.
THE EFFECTS OF SLEEP DEPRIVATION ON COGNITIVE FUNCTION AND EMOTIONAL REGULATION: AN EXPERIMENTAL APPROACH Zainol Hasan; Vann Sok; Sokha Dara
World Psychology Vol. 5 No. 3 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/wp.v5i3.1298

Abstract

Sleep is a fundamental biological process that supports cognitive efficiency, emotional stability, and overall psychological well-being. Increasing academic demands, technological engagement, occupational pressures, and lifestyle changes have contributed to a growing prevalence of sleep deprivation across various populations. Insufficient sleep has been associated with impairments in cognitive performance and emotional functioning, yet the interconnected effects of sleep loss on these domains remain insufficiently understood. This study aims to examine the effects of sleep deprivation on cognitive function and emotional regulation through a controlled experimental approach. A randomized controlled pretest-posttest design was employed involving 120 healthy young adults aged 18–25 years. Participants were randomly assigned to either a sleep deprivation group or a control group maintaining a normal sleep schedule. Cognitive performance was assessed using standardized neuropsychological tests measuring attention, working memory, and executive functioning, while emotional regulation was evaluated through validated psychological scales. Statistical analyses included repeated-measures analysis of variance, independent-samples t-tests, and correlation analyses. Findings revealed that sleep deprivation significantly reduced attention, working memory, executive functioning, emotional stability, and positive affect. Significant associations were also identified between declines in executive functioning and impairments in emotional regulation. The study concludes that sleep deprivation exerts a substantial negative influence on both cognitive and emotional processes, highlighting the critical role of adequate sleep in maintaining adaptive psychological functioning and overall well-being.
Applications of Artificial Intelligence in Weather Prediction and Agricultural Risk Management in India Aldi Bastiatul Fawait; Puteri Aprilani; Sugiarto Sugiarto; Vann Sok
Techno Agriculturae Studium of Research Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v1i3.1591

Abstract

Agriculture in India is particularly vulnerable to climate change and extreme weather conditions, which can negatively impact productivity and food security. This research was conducted against the background of the importance of developing technology to help farmers in dealing with weather uncertainty and managing agricultural risks. The purpose of this study is to explore the application of artificial intelligence (AI) in accurately predicting weather as well as managing the risks associated with extreme weather in India's agricultural sector. This study uses a descriptive method with a quantitative and qualitative approach, where data is collected through interviews with agricultural experts, analysis of historical weather data, and AI modeling. The results show that the AI application is able to predict weather patterns with an accuracy rate of up to 90%, which helps farmers make more informed decisions regarding planting timing, irrigation, and pesticide use. In addition, AI-based risk management systems allow for early detection of extreme weather, thereby reducing crop losses. The conclusion of the study is that artificial intelligence applications have great potential to improve food security and agricultural productivity in India by helping farmers anticipate weather changes and manage risks more efficiently. However, the adoption of this technology requires adequate training and infrastructure to ensure its optimal use in the field.
Nanostructured Catalysts for Efficient Energy Conversion: Recent Advances Rithy Vann; Ravi Dara; Vann Sok
Research of Scientia Naturalis Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i4.1573

Abstract

The global transition towards sustainable energy sources has driven significant research into developing advanced catalytic materials that can enable efficient energy conversion processes. Nanostructured catalysts, with their unique physiochemical properties, have emerged as promising candidates to address the challenges associated with energy conversion technologies, such as low conversion efficiencies and high production costs. Understanding the recent advancements in the field of nanostructured catalysts is crucial for accelerating the development of next-generation energy conversion systems. This review article aims to provide a comprehensive overview of the recent progress in the design, synthesis, and application of nanostructured catalysts for efficient energy conversion. The study investigates the underlying principles governing the enhanced catalytic performance of nanomaterials and examines their potential impact on diverse energy conversion processes, including fuel cells, water splitting, and photocatalytic systems. The research methodology involves an extensive literature review of peer-reviewed journal articles, conference proceedings, and patent documents published within the last five years. The analysis focuses on the latest developments in the synthesis and characterization of nanostructured catalysts, as well as their performance evaluation under realistic operating conditions. The review highlights the successful implementation of various nanostructured catalyst architectures, such as nanoparticles, nanotubes, nanosheets, and core-shell structures, in enhancing the catalytic activity, selectivity, and stability for energy conversion applications. Significant advancements in the rational design of catalysts through the control of composition, morphology, and surface properties are discussed, along with their impact on improving energy conversion efficiencies and reducing production costs. The study concludes that the continued development of nanostructured catalysts holds great promise for addressing the current challenges in energy conversion technologies. The insights gained from this review can guide future research directions and facilitate the translation of nanostructured catalyst innovations into practical, large-scale energy conversion systems.
Development of Composite Biomaterial Based Dental Implants to Improve Osseointegration Vicheka Rith; Vann Sok; Ravi Dara
Journal of Biomedical and Techno Nanomaterials Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v1i3.1760

Abstract

Dental implant technologies face significant challenges in achieving optimal osseointegration, critical for successful long-term patient outcomes. Traditional implant materials demonstrate limitations in biological performance, cellular interactions, and healing processes. Developing advanced biomaterial strategies capable of dynamically interacting with physiological environments represents crucial scientific innovation. Research objectives aimed to develop sophisticated composite biomaterial dental implants with enhanced osseointegration capabilities through innovative surface modifications and strategic ceramic reinforcement approaches. Experimental methodology employed comprehensive research design integrating materials science, cellular biology, and advanced computational modeling. Experimental protocols included precision biomaterial synthesis, nanoscale surface engineering, in vitro cellular response assessments, and sophisticated characterization techniques. Experimental results demonstrated statistically significant improvements in osseointegration rates, cellular attachment, and mechanical strength across developed composite biomaterial variants. Hierarchical surface modifications incorporating zirconia and hydroxyapatite reinforcements exhibited superior performance compared to traditional implant technologies. Conclusive findings validate innovative composite biomaterial approaches as transformative strategies for dental implant development, offering potential for accelerated healing, improved cellular interactions, and personalized medical interventions.
PEPTIDE-FUNCTIONALIZED MAGNETIC NANOPARTICLES FOR EARLY STAGE PATHOGEN DETECTION Loso Judijanto; Vann Sok; Chenda Dara
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i5.2380

Abstract

The rapid and sensitive detection of pathogenic bacteria is paramount for preventing infectious disease outbreaks, ensuring food safety, and guiding clinical treatment. This study aimed to develop and validate a novel biosensing platform based on peptide-functionalized magnetic nanoparticles for the rapid, selective, and sensitive detection of a model pathogen, Escherichia coli O157:H7, in its early stages. Superparamagnetic iron oxide nanoparticles were synthesized and subsequently functionalized with a specifically designed, high-affinity peptide that targets an outer membrane protein of E. coli O157:H7. The detection was performed using a simple colorimetric assay based on the peroxidase-like activity of the MNPs, where the signal intensity was proportional to the concentration of captured bacteria. The peptide-functionalized MNPs demonstrated a high capture efficiency of over 95% within 20 minutes. The platform exhibited excellent sensitivity with a low limit of detection of approximately 15 colony-forming units per milliliter (CFU/mL) in buffer and 30 CFU/mL in spiked milk samples. The developed peptide-functionalized magnetic nanoparticle platform is a highly effective and robust system for the early-stage detection of pathogens. Its combination of speed, high sensitivity, and excellent specificity makes it a promising candidate for the development of portable, point-of-care diagnostic tools for applications in food safety, environmental monitoring, and clinical diagnostics, addressing a critical need for rapid and reliable pathogen screening.
Decoding Lost Languages: A Philological Study of Ancient Texts Sri Nur Rahmi; Vann Sok; Sokha Dara
Journal of Humanities Research Sustainability Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v1i4.1798

Abstract

Background. This research focuses on the decoding of ancient languages and the complexity of the symbols used by the Egyptian, Mesopotamian, and Indus Valley civilizations. The background of the research is based on the importance of understanding language as a key tool for uncovering the social, spiritual, and administrative lives of past civilizations. Purpose. The purpose of the study was to explain how these symbols can be interpreted using a combination of traditional philological methods and artificial intelligence technology.   Method. The methods used include manual linguistic analysis supported by modern algorithms to speed up the decoding process. Results. The results showed that the symbols of Ancient Egypt were easier to decrypt due to additional documentation, while the symbols from the Indus Valley remained difficult to understand. Symbols from Mesopotamia show complex dual meanings, especially in religious and astronomical contexts. Case studies show that ancient languages are multifunctional tools that reflect advanced social and spiritual structures.   Conclusion. The conclusion of the study confirms that an interdisciplinary approach is essential to uncover more secrets from past civilizations. This research enriches the understanding of ancient languages and shows that technology can speed up the decoding process, although it is not yet fully adequate. This contribution paves the way for further research involving global collaboration and the development of new technologies.
Co-Authors Aldi Bastiatul Fawait Fawait Arnes Yuli Vandika Chenda Dara Loso Judijanto Puteri Aprilani Ravi Dara Rithy Vann Sokha Dara Sri Nur Rahmi Sugiarto Vicheka Rith Zainol Hasan