Asian Journal of Research in Computer Science
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Student Academic Record Systems and their Security Issues
No full textStudent academic records are an invaluable resource for universities and colleges worldwide, yet, their safekeeping is still a major concern for many of these institutions. The security measures currently applied to protect academic record systems are deemed unsatisfactory. This paper surveys the security issues relating to students’ academic records delving into existing students’ academic records systems, cybersecurity challenges and measures to deal with the security concerns. The paper proposes a practical security model, the PIEM model, to address these security issues aimed at empowering academic institutions to build and maintain strongly protected academic record systems for capturing, storing and retrieving student academic data. The work underscores the urgent need of educational institutions to exert extra efforts to securely maintain students’ academic records and facilitate easy access and use by the various educational actors
Exam Assessor Tool: An Automated System for Efficient Answer Sheet Evaluation
No full textWith Education 4.0 and four quadrant approach number of innovations have gone into academics for efficient, experiential, and outcome-based education however assessment schemes are still very much dependent on manual assessment methods which are time-consuming and cumbersome. The grading system can sometimes be irrational, with diversified schemes for the same course and can also be biased. Covid 19 pandemic caused a global economic avalanche like we’ve never experienced in our lifetime. Many countries have implemented control measures such as blockades and curfews. The education system in this chaos saw a silver lining with academics shifting to online mode, with paradigm shift in teaching, assessment techniques too need to evolve. Work done is an effort to ease the process of assessment, a machine learning assisted model is developed that automates subjective answer evaluation in the education sector. Our project involved several crucial steps, including grayscale conversion, Natural Language Processing (NLP) for data cleansing, data splitting, and training an artificial neural network (ANN) to predict scores based on extracted features. ANN-based system grades subjective responses without human intervention, reducing the workload of teachers and professors. Model constructed an ANN architecture with three layers using Rectified Linear Activation Unit (ReLU) and Sigmoid activation functions. Trained model was incorporated into a user-friendly web application using the Streamlit library. Model design gives a major boost in grading efficiency and accuracy while providing valuable feedback to students. Research surveys were conducted, and a dataset was constructed for training and testing the model. study yielded an accuracy of 83.14% after employing techniques such as text cleaning, preprocessing, and feature extraction
Fire Prediction Analysis Based on Ensemble Machine Learning Algorithms
No full textA fire accident is the most tragic incident in human life. Particularly environmental hazards such as forest fires lead loss of wildlife, economy, wealth, human lives and pollution. our research purpose of predict the occurrence of fire incidents using ensemble machine learning models. The goal is to develop an accurate and reliable model that can forecast the occurrence of forest fires based on various environmental factors. The best performance is obtained by the ensemble machine learning model for this work. Comparative study of individual model and ensemble model. If you check all models Decision tree predicts 75.4%, the Random Forest tree predicts 83.2%, the Support Vector Machine predicts 71.8%, and the K nearest neighbour predicts 82.1%. Ensemble models with two combinations of decision tree and random forest tree predicts accuracy is 80.8%. Support vector machine and KNN predicts the accuracy rate is 73.4%. The individual model predicts more accuracy compared to ensemble learning model
AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection
No full textThis study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework
Employee Attrition Prediction Based on Gradient Boosting Approach
No full textIn today\u27s organizational landscape, predicting employee attrition has emerged as a critical concern. The departure of trained, technical, and pivotal staff members poses significant challenges, including financial setbacks incurred in their replacement. To address this, organizations harness current and historical employee data to discern prevalent attrition triggers. Employing established classification methodologies such as Decision Tree, Logistic Regression, Random Forest, Support vector machine, and Gradient boosting Algorithms are constructed using human resource data. Leveraging feature selection techniques, these models facilitate proactive measures to mitigate attrition risks. By accurately forecasting attrition, companies not only fortify their workforce stability but also enhance economic resilience through diminished human resource expenditures. This proactive approach not only aids in retaining valuable talent but also fosters sustainable growth by fostering an environment conducive to employee retention and organizational stability
Improve Threshold Range of Canopy Clustering Using Optimization Algorithms
No full textCanopy clustering is an effective method for determining the number of clusters dynamically without requiring a predefined cluster count, making it particularly suitable for large and complex datasets. However, its performance is highly dependent on the manual tuning of threshold parameters T1 and T2, which can be time-consuming and inefficient. This study aims to enhance the Canopy clustering algorithm by automating the optimization of threshold ranges using intelligent optimization algorithms. We propose a novel framework that integrates Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Snake Optimization (SO) to automatically determine the optimal values of T1 and T2. Additionally, to address high-dimensional data complexity, we employ dimensionality reduction techniques such as t-SNE, SNE, and Kernel Principal Component Analysis (KPCA). The silhouette coefficient is utilized as the fitness function to evaluate clustering performance. Comprehensive experiments conducted on the Wine, Iris, and MNIST Subset datasets demonstrate that the proposed optimization-based Canopy clustering framework significantly improves clustering accuracy by up to 21% on the Wine dataset and 19% on the Iris dataset compared to traditional methods. Specifically, on the Wine dataset, the optimized Canopy clustering achieved a silhouette coefficient of 0.63, a 21% improvement over the original 0.52. On the Iris dataset, the optimized method outperformed k-means and manual Canopy clustering with silhouette coefficients of 0.62 versus 0.52 and 0.55, respectively. These results highlight the effectiveness of intelligent optimization algorithms in enhancing clustering adaptability and efficiency
Magnetic Field Sensor Network for Pipeline Monitoring Systems
No full textThe oil pipeline industry has seen a rise in criminal activities recently. More than $133b is estimated to be lost globally as a result of the activities of cartels, organized crime members and small-time vandals to oil pipelines. To steal petroleum products from a pipeline, criminals must first drill into the pipeline, (cold or hot tapping) and subsequently weld a new weld piece or screw in an orifice to the pipeline, through which the petroleum product is diverted from the pipeline to another storage. These have crippled the ecosystem, human lives lost and a wide range of poverty in the economy which has motivated this paper to find a lasting solution to the problem at hand. This work employed a magnetic field sensor detector for early detection and petroleum monitoring which is caused by vandals and corrosion that leads to spillage, it will significantly improve productivity, safe lives, improve agricultural products and the ecosystem. The sensor network detects acts of vandalism or corrosion and triggers real-time alert message to the operators for actions to be taken, to prevent wide spread of the substance by specifying the spot location of the incidence where the corrosion or spillage took place. 
Securing the Future: Cybersecurity Challenges and Solutions in Digital Oilfields
No full textDigital oilfield is a concept that applies advanced technology to automate workflows in the oil and gas industry for the sole purpose of maximizing production, reducing costs, and minimizing the overall risks associated with operations. However, despite the numerous advantages, careful planning and mitigation strategies put in place, there has been a spike in the rate of cyber-attacks carried out on critical infrastructure across various industries. Cyber threats like malware, ransomware, code injection attacks and phishing attacks pose significant risks to the smooth functioning of these digital infrastructures. Its sensitivities, including outdated legacy systems, Supervisory Control and Data Acquisition (SCADA) systems, and numerous IoT devices, including human error also play considerable roles in cybersecurity breaches, highlighting the need for robust security protocols and strategies. However, with the advent of safer AI technologies, enhanced employee training on cybersecurity, cyber incidents as reported in the case studies, could be mitigated. This research examines critical cyber-attacks in the oil and gas industry to identify vulnerabilities and develop robust preventive strategies. The findings emphasize the importance of a multi-layered security approach, including network segmentation, end-to-end data encryption, and systematic software patch management. Organizations must implement comprehensive incident response plans and conduct regular security audits to maintain operational resilience. The study highlights that effective cybersecurity in the oil and gas sector requires both regulatory compliance and strategic asset prioritization. By identifying and classifying critical infrastructure, companies can allocate resources more effectively and strengthen their security posture where it matters most. The research demonstrates that successful incident management hinges on well-defined response and recovery protocols. Emerging technologies play a pivotal role in advancing cybersecurity defenses. Artificial intelligence enables predictive threat detection, while blockchain technology enhances data integrity and traceability. Cloud security solutions and machine learning algorithms provide scalable, adaptive protection against evolving threats. Through evaluation of case study of cyber incidents across two distinct sectors, it becomes imperative that cybersecurity is a topic that cuts across various sectors and demands industry collaboration and stringent regulatory compliance to avoid future breaches
Cybercrime: Psychological Tricks and Computer Securities Challenges
No full textSeveral studies agree that traditional ways of preventing cybercrime, by applying common computer security techniques such as passwords, firewalls and anti-virus, are no longer effective tools/methods for preventing cybercrime. Moreover, the empirical studies evidence that the most effective cybercriminals’ technique is the phishing. Phishing is the cybercriminals means of entry or technique that devote both psychological and technical tricks to deceive the service users (individual or organizations) to become a victim of cybercrime. In that sense, the individual or organization (victim) became the enabler or catalyst of their own risk (cyberattack). The study established Psycho-Cybercrime Solution (PCS) algorithmic Model that detects psychological tricks in the phishing attack. The PCS model is the awareness and preventive model that provides the early warnings to the service users. The PCS model was tested in 40 e-mail messages, 20 from the author’s Gmail and 20 from Yahoo accounts and its results show the best fits. The study finds that the phishing attacks are psychologically and technically tricked. The common psychological tricks are fear, urgency, Authority, familiarity, curiosity, social proof, emotional appeal and trust, and the technical tricks are E-mail, Domain and DNS spoofing, URL manipulation and link shortening. Consequently, the study concluded that the phishing is the initiator or predecessor of other cybercrimes; it is a cybercriminal entry mean technique, which most cybercrimes start with phishing attacks. Hence the avoidance or prevention of phishing will consequently reduce the incidence of other cybercrimes. Therefore, we recommend the adaption of the PCS algorithmic model in cybercrime investigation and in community awareness campaign on cybersecurity issues. More specifics, cybersecurity stakeholders such as financial institutions, learning institutions, revenues authorities, communication service provider companies, healthcare centers, security organs, e.g., law enforcement organs and others to accommodate the PCS model their security strategy plans at their organizational levels. This will reduce the risks cyberattack and hence improve their organizational performance and customer trust
Exploring Generative AI: Models, Applications, and Challenges in Data Synthesis
No full textGenerative AI has emerged as a transformative field within artificial intelligence, enabling the creation of new data that mimics real-world information and expands the boundaries of what machines can autonomously generate. This study discuss the various models of generative AI, focusing on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Auto-Regressive models, each offering distinct approaches and strengths in data generation. VAEs excel in learning latent representations, making them ideal for applications like anomaly detection and data imputation. GANs, renowned for their high-quality image synthesis, have found extensive use in tasks ranging from text-to-image conversion to super-resolution. Auto-Regressive models, on the other hand, are particularly effective in sequential data generation, such as text generation, music composition, and time series prediction.
The paper highlights key applications of these models across diverse domains, including image synthesis, text generation, drug discovery, and simulation tasks in fields like healthcare, finance, and entertainment. Additionally, the study emphasizes the evaluation metrics are also called the comparitive parameters crucial for assessing the performance of generative models, such as perceptual quality metrics, Inception Score (IS), and Fréchet Inception Distance (FID), which provide quantitative insights into the quality and diversity of generated data.
This study employs a systematic methodology comprising a comprehensive literature review, strategic search queries, and thematic data synthesis to explore generative AI. Key areas of focus include models (VAE, GAN, auto-regressive, flow-based), applications, evaluation techniques, challenges, and recent advances. The analysis identifies emerging trends, novel methods, and critical gaps in the field.
This study also compares the performance of three Gen –AI models along with the comparative parameters like data type, Data Type, Applications, Training Complexity, Output Quality, Interpretability, Limitations, Advantages, Computational Cost and Scalability.
Generative AI raises ethical concerns, including biases in training data that perpetuate stereotypes and marginalization. It can be misused for harmful purposes like creating deepfakes or spreading misinformation, impacting trust and privacy. Questions of accountability and ownership arise when AI-generated content infringes on intellectual property or causes harm. Addressing these issues is essential for responsible AI deployment