Asian Journal of Research in Computer Science
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Cost-effective Digital Prescription using Pharmaceutical Knowledge Graph
No full text In the current era of the advancing medical domain and the ever-evolving use of technology in fields of pharmaceutical research, remote monitoring, and decision support systems in healthcare, prescription management has transformed from handwritten prescriptions to digital ones. This transition however does not imply that these prescriptions are comprehensive and provide optimized treatment outcomes. These digital prescriptions still reflect the formerly used handwritten prescriptions. Thus, recipients face the daunting challenge of making cost-effective, holistic, informed, and personalized decisions without compromising the legitimacy and authenticity of the original prescription, all due to a lack of readily available alternative medicine options. This problem can be addressed by utilizing the knowledge graph that we built, which contains carefully curated medical information collected from reliable and diverse sources, ensuring the authenticity and relevance of the information. Delving into the intricate interconnections among diverse medical entities and their properties, the medical knowledge graph presents an invaluable solution, empowering the generation of smart digital prescriptions in a fast and precise manner. Specifically, this study focuses on the transformative potential of digital prescriptions, elucidating their role in streamlining healthcare processes and enhancing communication between healthcare providers and patients. By leveraging the insights derived from our medical knowledge graph, we aim to contribute to the advancement of digital prescription systems, fostering more effective, personalized, and technology-driven healthcare solutions
Facial Recognition-Based Entry System for Student Residence Halls: Enhancing Security and Accessibility
No full textProtecting an organization from numerous threats both inside and outside is the primary function of the security system. Automated embedded systems have come a long way in the contemporary era and have shown to be highly beneficial in applications related to security and surveillance. Face recognition is one of the study fields in computer vision, which is commonly used in security systems for video surveillance. Even though facial recognition technology has advanced significantly and is employed in several significant applications, numerous challenges need to be solved. These challenges include changes in posture, occlusions, expression, aging, lighting, and other elements. Deep learning can be useful in these situations. By using several processing layers to develop data representations with multiple feature extraction layers, deep learning may achieve higher accuracy. With the purpose of providing better security for student residence halls, in this work, we present a real-time deep learning-based facial recognition system that can be used to identify an individual\u27s identity and give a warning when the individual\u27s face is not recognized in front of the system. Here, faces from the face database are matched in order to identify students based on a video of their arrival into the residence halls. This process begins with face detection and ends with face recognition. We used a Convolutional Neural Network (CNN) based model Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection and recognizing faces using the Google FaceNet model. The model was trained on around 3000 photos, taken by 30 distinct people
IoT Security in the Era of Ubiquitous Computing: A Multidisciplinary Approach to Addressing Vulnerabilities and Promoting Resilience
No full textThe Internet of Things (IoT) has rapidly become a pivotal, transformative force, seamlessly integrating billions of physical devices through sophisticated networks of embedded sensors, software, and internet connectivity. This expansive and interconnected ecosystem offers a broad spectrum of applications, significantly benefiting urban infrastructure with innovative solutions, enhancing industrial operations through optimization, and enriching consumer experiences with smart devices for safety and convenience. Despite the numerous benefits, the widespread adoption of IoT technologies has challenges, particularly in security and privacy. The proliferation of IoT devices has opened up new avenues for potential cyber threats, posing risks of data breaches and privacy violations. An in-depth analysis of notable IoT security incidents, such as the 2015 Jeep Hack, the Owlet WiFi Baby Heart Monitor Hack, and the TRENDnet Webcam Hack, highlights the critical vulnerabilities inherent in many IoT systems.
Organizations must adopt comprehensive and robust security measures to address these security concerns, including implementing advanced encryption protocols, deploying effective firewalls stringent access control mechanisms, and conducting regular security audits. A multi-layered security architecture becomes essential in mitigating such threats and ensuring the integrity of IoT networks. Furthermore, integrating blockchain technology presents a promising enhancement to IoT security and privacy protocols. Blockchain\u27s inherent features of decentralization, transparency, and immutability offer an additional layer of security, making it more difficult for unauthorized entities to compromise IoT systems.
Equally crucial is the need to elevate IoT security awareness among organizations; this can be achieved through persistent research, fostering collaborations with security experts, and promoting best practices in IoT security. By actively addressing these security challenges, organizations can not only harness the full potential of IoT but also protect their reputations, build trust with stakeholders, and ensure the privacy and safety of their data. Therefore, while IoT presents an array of opportunities for innovation and efficiency, the importance of vigilance in security cannot be overstated. Balancing the benefits of IoT with robust security measures will be vital to realizing its full potential safely and reliably
Unsupervised Fuzzy-Multi-Core Aspect Sentiment Analysis for Big Data of Online News Users\u27 Persian Opinions
No full textAn online news article can cover various topics or contain different aspects of a subject, encouraging readers to express their opinions on specific topics or aspects. Sentiment analysis evaluates the overall sentiment of the audience towards the entire news article, whether it is positive, negative, or neutral. However, in aspect-based sentiment analysis, the focus is on determining which aspect of the news article the opinion is referring to. Extracting the relevant aspect in sentiment analysis involves identifying the part of the article that the reader has expressed an opinion about. This task can lead to a more precise analysis of audience reactions to future news and events. To accomplish this, the news text is segmented into constituent sentences and transformed into a vector space. Then, an unsupervised clustering method is applied to extract various aspects of the news. Fuzzy multi-core clustering is employed as the clustering technique, which has lower computational overhead and can handle uncertain, noisy, and outlier data easily. The implemented approach is based on the concept of feasibility and utilizes multi-core learning to detect clusters in complex data structures. This method remains robust against issues such as ineffective cores or unrelated features by automatically adjusting the core weights within an optimized framework. Furthermore, support vector machines are employed to establish the relationship between opinions and relevant aspects. The transition to the vector space, the mapping process, clustering operations, and aspect extraction are performed in the reducer
Cloud Computing Forensics; Challenges and Future Perspectives: A Review
No full textCloud computing has become increasingly popular in recent years, evolving into a computing paradigm that is both cost-effective and efficient. It has the potential to be one of the technologies that has had the most significant impact on computing throughout its history. Regrettably, cloud service providers and their customers have not yet developed major forensic tools that can assist with the investigation of criminal conduct that occurs in the cloud. Because it is difficult to prevent cloud vulnerabilities and criminal targeting, it is necessary to be aware of how digital forensic investigations of the cloud may be carried out. This is because cloud vulnerabilities and criminal targeting are difficult to avoid. In this context, the current study examines current and future trends in cloud forensics, methodology for cloud forensics, and cloud forensic tools. In addition, the study also looks at cloud forensic approaches
Gamified Cyber-Crime Monitoring and Control Framework in a Computer Network Environment
No full textRecent advancements in cybercrime are continually emerging, with the estimated damages to the global economy reaching the billion dollar mark. In the past, people acting alone or in small groups were the main perpetrators of cybercrime. Complex cybercriminal networks are now bringing people from all over the world together in real time to commit crimes on a never-before-seen scale. Game theory gives a formal vocabulary for the description and study of interacting situations in which a number of "entities," known as players, take actions that have an effect on one another. The field of cyber security could benefit from problem-solving techniques based on games theory to protect assets. In this article, we suggest a conceptual framework for a system for monitoring and controlling cybercrime
A Review on Distributed Denial of Service Attack
No full textToday’s world, technology has become an inevitable part of human life. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutions has shifted from offline to online. It leads to exponential increase in intrusions and attacks over the internet-based technologies. Distributed denial of service (DDOS) attack is one of the most dangerous attack that could cause devastating effects on the internet. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. In network security this attack is very dangerous. The main aim of DDOS attack is to collapse the network or server with abnormal traffic to make server unavailable for the legitimate users. In this paper reviews various type of DDOS attacks, Symptoms of DDOS attack, role of botnet on DDOS attack and give some mitigation and prevention technique for DDOS attack
Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices
No full textAims: This research offers an in-depth exploration of the hurdles organizations face during legacy application modernization. The investigation delves into the primary motivations behind modernization, delineates the associated challenges, and proposes viable strategies and best practices to mitigate these issues.
Study Design: This is a Review Article which synthesizes and critically assesses a broad array of sources relevant to legacy application modernization. It amalgamates insights from various studies, offering a comprehensive overview and analysis of existing literature to derive meaningful conclusions and recommendations. Through this approach, the article provides a holistic understanding of the challenges and strategies associated with modernizing legacy systems.
Place and Duration of Study: This global study was conducted over eight years, from January 2016 to August 2023.
Methodology: This research uses a literature review to collect data. In the literature review process, a comprehensive array of data collection methods is strategically employed to ensure the acquisition of a diverse and pertinent body of knowledge concerning the challenges associated with modernizing legacy applications and the effective strategies and best practices to address them. It starts with searching in extensive Online Databases and Repositories, using Keyword Searches and Citation Tracking to find the relevant literature. Systematic reviews and meta-analyses give structured synthesis, while manual searches collect real-world case studies. Grey Literature supplements insights, and Evidence-Based Practices ensure rigor. Thematic Analysis sorts findings, whereas Data Management arranges data, and the Critical Appraisal Skills programme evaluates the credibility of sources. This approach is an important starting point for modernizing legacy systems and developing effective policies and guidelines.
Results: The research identifies business necessities and technological advancements as the predominant catalysts for modernization. It further elucidates the obstacles encountered by organizations during this transition, such as the intricacies of data migration, the complexity inherent in legacy systems, and issues related to user acceptance and integration. The investigation also delves into potential strategies and best practices to navigate these challenges, emphasizing the significance of selecting the right modernization approach.
Conclusion: The existing research underscores that although the path to modernizing legacy applications has obstacles, they can be navigated successfully through astute planning, strategic decision-making, and adept execution. In doing so, organizations have the potential to metamorphose their dated systems into valuable tools that resonate with current business demands and the latest technological advancements
Health Insurance Cost Prediction Using Deep Neural Network
No full textArtificial intelligence (AI) and Deep Learning (DL) are strategies for making human being’s lives simpler in the healthcare enterprise through predicting and identifying ailments more fast than the general public of scientific specialists. There may be an immediate connection between the insurance organization and the policyholder while technology reduces the distance between them to zero in particular with digital medical insurance. In preference to commonplace protection, simulated intelligence and profound mastering have meaningfully impacted the way in which guarantors build health care coverage designs and empowered customers to hastily get benefits greater. With a view to provide clients with accurate, spark off, and effective medical health insurance, insurance companies use DL. Medical health insurance quotes have been expected the use of an artificial neural network (ANN) and a deep neural network (DNN) algorithm on this take a look at. Based on the traits of the individuals, the author envisioned how a good deal medical insurance might price. Age, gender, body mass index, the range of kids, smoking behavior, and place had been all used to train and examine an artificial neural network model
Predicting Students’ Performance Using Machine Learning Algorithms: A Review
No full textEducational Data Mining is a discipline focused on developing ways for studying the unique and increasingly large-scale data generated by educational settings and applying those methods to better understand students and the environments in which they learn. Predicting student performance is one of the most critical concerns in educational data mining, which is gaining popularity. Student performance prediction attempts to forecast a student\u27s grade before enrolling in a course or completing an exam. The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the most important attribute(s) in a student\u27s data. The study showed that neural networks is the most used classifier for predicting students’ academic results and also provided the best results in terms of accuracy. Also, 87% of the algorithms used were supervised learning as compared to 13% for unsupervised learning and 59% of the studies employed various feature selection methods to improve the performance of the machine learning models