Asian Journal of Research in Computer Science
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Forensic Evidence Collection in IoT Environments: A Systematic Review of Current Techniques, Gaps and Strategic Recommendations for Data Integrity
No full textDue to the advancement in the Internet of Things (IoT) devices, the different sectors have greatly expanded through connectivity and flexibility. However, these various devices and networks present certain difficulties for the digital forensic investigations, especially, in the aspects of the devices type variety and data integrity. In later years IoT has given additional concerns to the field of digital investigation and traditional techniques have many times been found incompetent to effectively deal with these issues which demands the establishment of sound evidence acquisition processes suitable for IoT environment. Thus, this research employed mixed methods approach, a comprehensive review as well as the systematic literature review (SLR) method to conduct a comprehensive analysis of the existing forensics techniques and tools in the context of IoT. This research aims to identify gaps in current methodologies and propose potential solutions to enhance the reliability and effectiveness of forensic evidence collection in IoT environments through the systematic analysis of peer-reviewed articles, case studies, and industry reports. The study proposed strategic recommendations for developing additional robust forensic methods that ensure data integrity and accommodate the vast diversity of IoT devices, thereby supporting more accurate and reliable digital investigations in this fast developing technological landscape
Development of OTPD4ALS: A Specialized Database for Efficient Screening of Anti-amyotrophic Lateral Sclerosis Drug Candidates
No full textAmyotrophic lateral sclerosis (ALS) is a complex, multi-system disorder characterized by glial cell involvement, immune system dysfunction, axonal transport disturbances, and impaired mitochondrial and neurotrophic support. Developing a specialised database for the efficient screening and identification of potential anti-ALS compounds is essential for advancing therapeutic discovery. This study presents the design and development of OTPD4ALS (Open Targets Potential Drugs for ALS), a comprehensive database specifically tailored to accelerate the identification of promising drug candidates. The methodology involved curating potential compounds, predicting pharmacokinetics in batches, and structuring the database for optimal accessibility. OTPD4ALS provides a streamlined platform for quickly identifying potential therapeutic compounds and is publicly available at https://otpd4als.vercel.app
Performance Benchmarking of YOLOv11 Variants for Real-Time Delivery Vehicle Detection: A Study on Accuracy, Speed, and Computational Trade-offs
No full textThe YOLOv series represents state-of-the-art technology for single-stage object detection, excelling in speed and accuracy. In many scenarios, it outperforms traditional two-stage detection frameworks, making it ideal for real-time applications. This study evaluates YOLOv11 model variants (n, s, m, i, x) on a custom dataset of 2,285 labelled images representing four delivery vehicle classes: FedEx, Other-Vehicles, UPS, and USPS-Truck. The dataset is meticulously curated to capture diverse delivery vehicle scenarios and split into training, validation, and test sets. Each variant was fine-tuned using uniform settings: 20 epochs, an input resolution of 640×640 pixels, and a batch size of 16.
Performance was assessed using metrics such as mean Average Precision (mAP, a standard metric measuring detection accuracy) across Intersection over Union (IoU) thresholds from 50% to 95% (a range defining the overlap between predicted and ground-truth bounding boxes), precision, recall, and inference speed on GPU and CPU. The results highlight trade-offs between model complexity and performance: smaller variants like YOLOv11-n achieved faster inference speeds (170.74 FPS on GPU and 5.86 ms on GPU), while larger models like YOLOv11-x excelled in detection accuracy and recall but at the cost of slower speeds (240.03 FPS on GPU and 4.17 ms on GPU). YOLOv11-s, for example, offered a balance with the highest FPS (1120.46 GPU FPS) but with moderate accuracy and recall. These findings demonstrate the adaptability of YOLOv11 variants to varying application requirements, from high-speed real-time systems to scenarios prioritizing detection accuracy.
This research advances object detection by providing a detailed performance benchmark for YOLOv11 variants. It offers practical insights for deploying YOLOv11 in diverse fields, including logistics, delivery tracking, and other domains requiring efficient and accurate object detection
Use of Big Data Analytics to Understand Consumer Behavior
No full textThe purpose of this study is to investigate the meaning and characteristics of big data and to examine aspects of consumer behavior within the framework of big data research. The results show that although external factors and internal perception are the primary determinants of consumer decision making, big data also affects customer perception through external factors. Data is too large to be handled and analyzed by standard database management system techniques (Latvia_ ESRD 43_2016, n.d.). In order to give their businesses a competitive edge, marketers can use analytics to better understand consumer behavior. This article explores the characteristics of the big data phenomenon (Ramanathan, U., Subramanian, N., Yu, W., & Vijaygopal, R., 2017). Data is gathered from clients who receive business and technical training under controlled conditions. Apart from going over technical aspects like architecture, infrastructure, logic, theory, and environment building, this study will also cover consumer behavior modeling (Erevelles, S., Fukawa, N., & Swayne, L, 2016) The first part of the literature review examines concepts related to trust in consumer behavior. It investigates the psychological foundations of consumer trust as well as the ways in which perceptions of risk, confidence, and trust influence the decision-making process (Sousa, R., & Voss, C., 2012) Furthermore, it runs counter to the idea of empowered firms by highlighting how legitimacy affects consumer trust, brand integrity, and trust. However, it looks at why customers are suspicious of unlicensed services and discusses the problems and reasons behind that mistrust. In (Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A., 2019). The recommendation engine essentially suggests numerous products based on a variety of factors, such as the user\u27s age and previous purchases. This kind of data filtering technology uses machine learning algorithms to recommend the best products to a particular customer. The purpose of this study is to segment related product reviews and analyze user sentiment in order to create a product recommendation system (Ertemel, A. V., 2015)
Empirical Study of Agile Software Development Methodologies: A Comparative Analysis
No full textThe comparative analysis of software development models, also called the Software Development Life Cycle (SDLC), is an everyday discourse among software engineers, reflecting the dynamic nature of the field. Within this realm, various software development methodologies, such as prototyping, spiral development, and Rapid Action Development, have been established and recognised for their unique approaches to software creation. In recent years, Agile methodologies have emerged as prominent contenders in software development, offering flexibility, adaptability, and efficiency in delivering high-quality software within designated timeframes. Among the array of Agile methodologies, including Dynamic System Development Method (DSDM), Scrum, Feature-Driven Development (FDD), Extreme Programming (XP), Kanban, Adaptive Software Development (ASD), Mendix, Lean, and Crystal, several have garnered significant attention in the software development community. Specifically, ASD, DSDM, XP, FDD, Kanban, and Scrum have emerged as prominent choices among Agile methods utilised by software developers. This study conducts a comprehensive examination and comparison of these six Agile software models, aiming to elucidate their functionalities, strengths, and weaknesses. The findings of this comparative analysis seek to provide valuable insights for software industries, enabling informed decision-making when selecting software development models for upcoming projects. By understanding each Agile methodology\u27s nuanced differences and capabilities, software developers and industry stakeholders can align their project requirements with the most suitable software development approach, ultimately optimising project outcomes and software quality
The Role of ChatGPT in Exploring Science and Its Applications: A Case Study at the “Citizen” Program
No full textFrom the citizen science perspective, this study investigates the multidimensional function that ChatGPT, an innovative language model developed by OpenAI, plays in the field. Using its capacity to generate text that is incredibly similar to that produced by humans, ChatGPT shows a great deal of promise in enabling and improving a variety of facets of collaborative scientific activities. While the paper does an excellent job of showing the potential uses, it also discusses the limitations and difficulties of implementing ChatGPT into citizen science programs
Strategic Assessment of Intricacies in Healthcare Cyber Security: Analyzing Distinctive Challenges, Evaluating Their Ramifications on Healthcare Delivery, and Proposing Advanced Mitigation Strategies
No full textHealthcare firms have access to highly sensitive and valuable data, such as patient health records and payment card information. They are also becoming more reliant on Internet of Medical Things (IoMT) devices to provide treatment, and assaults on this networked equipment can result in data breaches or disruptions to crucial care of patients. Hence there is need to critically assess the intricacies in healthcare cyber security by analyzing the unique challenges facing healthcare sector, their impact on healthcare delivery and suggest some effective cyber security measures to mitigate the identified Cyber security challenges facing health care sector. The research employs a quantitative method by using the descriptive and survey approach through the use of questionnaires to elicit and gather relevant information regarding the Unique Cyber security Challenges in the Healthcare Industry. The descriptive method was used to achieve the objectives of the study while the survey technique was used to get qualitative information from respondents about effect of those cyber security challenges in the health care industry. The target sample size used for this study was 1300. However, only 980 responses were recovered and used for the analysis. Future research work suggested that strong encryption procedures can be developed and implemented for healthcare security, sophisticated anomaly detection tools can be incorporated, and cooperative frameworks for information exchange across the healthcare ecosystem could be established. Also the suggested effective measures to mitigate cyber security challenges in healthcare sector can be implemented in order to secure the patient sensitive and valuable data
Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree
No full textThis study compares Support Vector Machine (SVM), Logistic Regression, and Decision Tree algorithms for liver disease prediction using a dataset sourced from Kaggle, comprising 20,000 training records and approximately 1,000 test records. The research evaluates the algorithms based on performance metrics, including accuracy, precision, recall, and F1-score. SVM emerged as the most effective model with an accuracy of 85%, followed by Logistic Regression with 82% and Decision Tree with 79%. The findings underscore the significance of algorithm selection in healthcare applications and highlight SVM\u27s potential for early detection and intervention in liver disease cases, paving the way for improved patient outcomes and healthcare management. Future work will focus on refining the algorithms and validating the results with larger and more diverse datasets to enhance predictive accuracy and robustness further
Heuristics for the Intelligent Prediction of Population Growth
No full textPopulation growth is a phenomenon that is inevitable in a life course and the components of population growth are fertility, mortality and migration. Because of the impact population growth exact upon the socio- economic nature of a country, it will be wise to know the future size and distribution of it so that adequate measures can be made to forestall possible problems. There are different population models and algorithms implemented to project population growth, ranging from statistical to machine learning models; these model techniques were very suitable in their time, such as the Malthusian theory, which, first and foremost, gives deeper consideration to the exponential growth model, while many implemented classification and linear regression algorithm. Linear regression machine learning algorithms were considered the most effective algorithm for population growth projection. This study tends to develop a machine learning model that is data-driven mathematically, capable of implementing the data-independent prediction model equation that was used to predict the impact of the Non-pharmaceuticals approach and pharmaceuticals approach (NPA and PA) in mitigating the spread of the Covid-19 virus, and both models were re-modified to project Nigeria population growth. The data-independent prediction model (DIPM) utilized onset data from the NPA and PA interventions to predict the probability of mitigating the spread of the virus for a specific period. The DIPM has the properties of arithmetic and exponential methods. The fusion of these two properties with the aid of machine learning model has further reveal the data-independent prediction model as a conceptualization technique to reflect how data are processed in an algorithm setting in a concrete world, with the support of the Java array list algorithm, what all the statistical models and supervised machines learning model used in the past studies could not achieve, have been accomplished succinctly. The data independent prediction model is a robust technique for both projection and forecasting future population growth as well as proffer answers to historical issues in mathematical modeling of population expansion
Simulation and Construction of Wireless Heartbeat and Body Temperature Monitoring Device Using Microcontroller
No full textHeart rate and body temperature are important health parameter that need to be monitored especially for the old aged that needs constant medical care. In this study, simulation and construction of wireless body temperature and heartbeat monitoring device was carried out using PIC16F886 microcontroller and RF module. The circuit was simulated using Proteus8.4, while a prototype was constructed on a Vero board. The circuit was tested by measuring temperature and heartbeat of 20 volunteers during different levels of activities. Result shows that, the transmitter section, reads body temperature and heartbeat using sensors. The signal is then processed by the microcontroller and wirelessly transmitted to the receiver section through RF module at 433MHz, while detail is displayed on the LCD. At the receiver section, the signals are received through RF antenna operating at the same frequency with the transmitting RF module (433MHz). The data processed by the receiver microcontroller is also displayed on LCD. The readings of body temperature and heartbeat can be taken simultaneously by the same device and transmitted wirelessly to the care giver or patient relation. The device recorded a heartbeat rate of 76bpm at normal body temperature of 36.2°C and for every degree increases in body temperature, the heart beats about 10bpm faster. The device is reliable and cost effective to enable it accessible and affordable to the care giver and patient’s relative who can monitor the patient remotely