Asian Journal of Research in Computer Science
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792 research outputs found
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Blockchain-Enabled Secure Credentialing and Access Management for Remote Healthcare Providers and Patients Across Fragmented Digital Health Platforms
No full textThis research developed a blockchain-enabled framework to enhance secure credentialing and access management for remote healthcare providers and patients across fragmented digital health platforms. Addressing inefficiencies in traditional systems such as lengthy verification delays and data silos, the study employed a design science approach, integrating Hyperledger Fabric and Ethereum smart contracts. Simulations using synthetic healthcare datasets demonstrated a significant improvement, including a 99.99% reduction in credential verification time (to 14 seconds), a 650% throughput increase (to 1,876 TPS), and a 94.7% reduction in security breaches, with 97.8% interoperability success across 234 systems. The framework achieved 99.93% authentication accuracy and 41% administrative cost savings. While results show strong potential, the reliance on simulations may not capture full real-world complexities, and high initial deployment costs remain a constraint. Regulatory compliance, particularly with evolving standards such as HIPAA, was considered essential for implementation. Future work will focus on real-world pilot deployments, AI-driven fraud detection, and the establishment of standardized protocols to support scalability and interoperability. Overall, this study advances secure and efficient healthcare delivery by enabling real-time credentialing and interoperable access, fostering patient-centric care in telemedicine
Simulation and Construction of Radio Frequency Identification (RFID) Based Door Locking System Using Smart Card
No full textDoors are meant for security and safety to prevent intrusions from unwanted persons. However, traditional locking mechanisms, reliant on physical keys, present significant challenges including loss, theft, and unauthorized duplication, which compromise the safety of individuals and properties. This research focuses on the simulation and construction of a Radio Frequency Identification (RFID) based door locking system utilizing smart card authentication. Simulation was performed using Proteus 8.0 to design and verify the RFID-based circuit before implementation. Embedded C was used to program the microcontroller, ensuring logical control of the door locking mechanism based on RFID authentication. The physical construction was done on a Vero board using LGT8F328P microcontroller, RC522 RFID reader, 12V solenoid lock, and relay. The system was powered using a 12V DC adapter. Testing involved scanning 4 RFID cards and 1 RFID tag—only 1 card and 1 tag were registered in memory. Simulation result showed reliable activation of lock/unlock functions, with the system responding in under 1 second per card authentication. LED indicators displayed clear lock and unlock status throughout the process. The findings indicate that the RFID system provides a robust alternative to traditional locking mechanisms, mitigating issues related to lost or stolen keys while enhancing user convenience. This work contributes to the field of access control systems by demonstrating the feasibility of RFID technology in improving security measures for individuals and organizations
Applications of Data Mining Techniques in Medicinal Plant Bioinformatics: Frameworks, Tools, and Challenges
No full textGlobally, lot quantities of data exist in medical, industry and other related fields. An interested area of research in bioinformatics a case study in medicinal plants is the application and improvement of data mining techniques to solve biological problems involved. The quantity and variety of data in natural sciences enhanced quickly. Therefore, data abstraction, data manipulation and pattern discovery techniques are require in dealing with such large varieties of medicinal plants. Analyzing huge biological medicinal plants’ data sets requires making sense of the data by inferring structure or generalizations from the data. Integration among different sources of data is likewise of most important interest, as complex relations may happen. In this research paper, some basic concepts of bioinformatics, data mining and WEKA explorer tool are discussed. This research supports the development and validation of herbal treatments by comparing their efficacy with orthodox medicines that enhances decision making for herbal practitioners through structured databases and software tools that provide accurate treatment recommendations. Also it strengthens the integration of bioinformatics with herbal medicine by facilitating gene expression analysis, sequence interpretation, and knowledge discovery, ultimately promoting more efficient herbal drug development and application which is the main goal. Therefore, the area of application of data mining in the field of bioinformatics was clearly stated in this research and the current opportunities and challenges of data mining in medicinal plants bioinformatics are discussed as well
Decoherence Impediments in Quantum Computing and Fundamental Challenges of Quantum Error Correction
No full textThis review synthesizes current understanding of decoherence pathways across leading hardware platforms and explains why several experimentally observed noise features—drift, burst events, coherent components, correlated and non-Markovian structure, and leakage—can be disproportionately damaging for QEC. Decoherence remains the dominant impediment to scalable quantum computing because it is not a single error mechanism but a system-level phenomenon arising from materials defects, electromagnetic loss, control electronics, measurement backaction, and the broader environment in which a processor operates. Quantum error correction (QEC) is designed to algorithmically suppress physical noise, yet its practical success depends on how closely real devices satisfy the assumptions under which fault tolerance is proved: approximate locality, weak temporal correlations, sufficiently stochastic error statistics, low leakage, and reliable syndrome extraction. We then evaluate fundamental and engineering challenges that shape the viability of QEC at scale: syndrome measurement fidelity, correlated error suppression, decoding latency and classical co-processing, architectural constraints (connectivity, crosstalk, calibration overhead), and the resource cost of implementing a universal fault-tolerant gate set. Recent demonstrations of below-threshold behaviour and “break-even” regimes show that QEC is transitioning from theory to practice, but also clarify what remains unresolved: maintaining stable noise below fault-tolerance targets over long times, scaling to many logical qubits with low correlated-error rates, and integrating hardware-aware codes and decoders. We conclude with research priorities that treat decoherence and QEC as part of a co-designed stack spanning device physics, control, architecture, and algorithms
Leveraging Artificial Intelligence for Customer Segmentation and Demand Forecasting in the Car Rental Industry
No full textThe dynamic car rental industry faces significant challenges in demand forecasting, with about 50% of companies reporting inaccuracies that result in fleet utilization rates of only 70-75% instead of the optimal 85-90%. The study integrates customer segmentation and demand forecasting into a framework using various ML models. This study utilized historical rental data from Secured Wheels Car Rental reports in Lagos and Ibadan, Nigeria. The data underwent thorough preprocessing, including cleaning, selecting relevant features, and splitting it for analysis. The study employs decision trees, random forests, and clustering algorithms such as DBSCAN, Agglomerative clustering, Fuzzy-C-Means, and Affinity Propagation for segmentation. To enhance demand forecasting in the car rental industry, key customer segmentation features such as inactivity period, number of reservations, and cluster groups were incorporated into the model. This integration allowed for more precise demand predictions by capturing segment-specific patterns. For demand forecasting, the study uses ARIMA, regression model, and Holt-Winters. Performance metrics like accuracy, precision, silhouette coefficient, and Mean Absolute Error (MAE) evaluated the models, and the framework\u27s results were benchmarked against existing methods. Results indicate that the Agglomerative clustering achieved a silhouette coefficient of 0.9238 and a Davies-Bouldin index of 0.0031. At the same time, the HW model recorded a lower Mean Absolute Error (MAE) of 29.3641 and a Mean Squared Error (MSE) of 1183. The HW model was trained with customer segmentation features and the five cluster groups. These enhanced blended models enable more tailored marketing strategies and personalized customer experiences, increasing customer satisfaction and loyalty
Modeling the Integration of IGP and BGP by Keeping MPLS Architecture
No full textThis article explores the key characteristics of integrating IGP and BGP protocols to enhance the scalability, resilience, and manageability of modern network infrastructures. The research addresses the limitations of operating IGP and BGP independently by proposing their integration into a unified MPLS-based architecture. This integration aims to improve scalability, reduce convergence time, and optimize resource management in increasingly complex network environments. The study includes an analysis of the theoretical foundations of IGP and BGP technologies and reviews current approaches to segmented and integrated routing. Special attention is given to the mathematical model involving RFC 3107 and RFC 4655 extensions, the Next-Hop Self function, and the role of border routers in bridging routing information exchange between local and global levels. Using analytical methods and computer simulations, the study demonstrates that the integrated model reduces convergence time (down to 50 ms) and enhances routing efficiency. The scientific novelty lies in the proposed new approach to implementing IGP and BGP within MPLS technology. The findings are particularly relevant for telecommunications professionals engaged in the detailed analysis and optimization of routing protocols, as well as researchers focused on the integration of IGP and BGP to improve network performance and scalability
Diabetes Prediction Using Machine Learning
No full textDiabetes mellitus is a persistent metabolic condition impacting millions globally. Preventing problems requires early detection. This study uses a clinical cohort from Medical Centre Chittagong, Bangladesh, and the Pima Indian Diabetes dataset to create machine learning-based classification models for diabetes prediction. Five supervised algorithms, including k-nearest neighbours, naïve Bayes, support vector machines, decision trees, and multilayer perceptron’s, were trained and validated using ten-fold cross-validation following thorough data pre-processing and the selection of nine essential features. Performance measurements encompassed accuracy, precision, recall, F-measure, and area under the ROC curve. Model accuracies varied between 81.1% and 97.6%, whereas ensemble techniques had a dependability of up to 98.7% and an AUC of 0.95. These results show how integrated machine learning pipelines can help clinicians make clinical decisions when it comes to diabetes risk screening
Development of Petroleum Applications and their Benefits Using Artificial Intelligence
No full textThe application of AI and ML across various industries, such as manufacturing and petroleum, has led to innovative solutions that enhance productivity, accuracy, and decision-making capabilities. PCA was used to identify key GAI and MLA variables that influence the performance of the oil and gas value chain. SEM was employed to assess the regression equations related to their application. Recently, significant advancements in artificial intelligence (AI) technology have rapidly expanded within the petroleum industry, presenting enormous potential for growth and innovation. Generative Artificial Intelligence (GAI) and Machine Learning Algorithms (MLA) are becoming increasingly important in the oil and gas industry due to rapid advancements in society and technology. Principal Component Analysis (PCA) was utilized to identify critical variables influencing performance in the oil and gas value chain, and their effects were evaluated using Structural Equation Modeling (SEM). To effectively make decisions in the oil and gas value chain, it is essential to utilize advanced processing and analysis tools because of the vast amo unt of data generated by real-time monitoring of reservoirs and well operations. Machine learning (ML) is a powerful subset of artificial intelligence (AI) that utilizes models and algorithms to analyze historical data and extract insights. AI is a groundbreaking technology that enables machines to mimic human behavior
The Application of Transfer Learning in Medical Image Classification: A Review of Literature
No full textTimely identification about health disorders has been made possible by photographic technologies, therefore photographic evidence provides a helpful technique towards illness identification. In addition to being labor-intensive, mechanical imagery processing techniques are prone to inter- and intra-observer inconsistency. Those restrictions may be addressed by computerized diagnostic imagery investigation methods. Transfer Learning (TL) frameworks using computerized health-related imagery processing have been looked at within the paper. It is has found how transfer learning can be used for many different healthcare scanning responsibilities, including recognizing objects, diseases classification, separation, and sensitivity scoring, etc. In contrast to conventional deep learning techniques, it is demonstrated that transfer learning offers superior selection assistance and uses reduced experimental input. A total of 100 peer-approved English language publications using the archives, IEEE Xplore and PubMed, where obtained till April 2025. A total of 53 research papers are considered suitable to the subject matter of this study after the PRISMA procedures for article screening was applied. Transfer learning techniques, such as pattern extraction, pattern extraction mix, fine tuning, and fine tuning from the start, are examined by works that concentrated on choosing core algorithms. Most research conducted experimental evaluations about several algorithms, then examined shallower depth approaches. Depth framework that has been used frequently within the academic research is Inception. In order to determine the best arrangement for the Transfer Learning, most research quantitatively compared several methods. just one methodology was used throughout the remaining experiments, then the two highly popular methods included pattern extraction and fine-tuning from start. Some research used pretrained algorithms for fine tuning and extraction of features hybrids. According to this study, the best popular Transfer Learning algorithms in analyzing medical images are AlexNet, ResNet, VGGNet, and GoogleNet. Such TL algorithms have been shown to be capable of comprehending healthcare photos, plus their capability to do so is improved by customisation, rendering them valuable instruments in studying photo scans
Recent Advances in RL for Self-adaptive Software Systems: A Systematic Review
No full textIn dynamic environments like cloud computing, the internet of things (IoT), and cyber-physical systems, where conventional rule-based adaptation mechanisms frequently fall short of maintaining optimal performance in the face of uncertainty and change, self-adaptive software systems (SASS) are becoming more and more important. A promising remedy that allows systems to learn and adapt on their own through trial-and-error interactions is Reinforcement Learning (RL). After a thorough screening of 1,248 papers, 68 quantitative studies were chosen for analysis in this systematic review, which examines developments in RL for dynamic optimisation of SASS from 2021 to early 2025. Value-based, policy gradient, multi-agent, and hybrid/meta-learning approaches are the main RL methodologies identified in the review, which also looks at how they are applied in fields like cybersecurity, cloud resource management, and autonomous systems. The findings indicate that Cloud systems reduced average cost by 34.7% using PPO-based solutions, cybersecurity systems improved attack detection speed by 22.1% and false positive rates by 18.3%, and autonomous systems reduced energy consumption by 40% and adaptation latency by 27.5% in IoT and swarm robotics. Policy gradient methods (41%) dominate continuous control tasks, with PPO used in 27% of studies. Value-based approaches (32%) dominate discrete action domains, with deep Q-networks (DQN) variants used in 78% of cloud resource allocation studies. Multi-agent RL accounts for 18% of studies, with Multi-agent deep deterministic policy gradient (MADDPG) - 62% and QMIX (38%) being the most used. Serverless computing cut cold-start times by 35%, data centre optimisation lowered power usage effectiveness (PUE) by 15%, and RL-driven intrusion detection systems identified zero-day threats with 92% accuracy. Reward design difficulties were found in 63% of experiments, sample inefficiency required 1.2M episodes to converge, and real-world multi-agent reinforcement learning (MARL) deployments performed 23% worse than models. Metal analysis effect resulted in 95% in cost reduction, latency improvement and adaptation speed respectively. Practical adoption is limited because so few studies use standardised benchmarks or address safety and interpretability. In order to close the gap between research and practical implementation, the article ends by outlining open research questions and promoting formal verification, transfer learning, and hybrid learning