Research has shown that, after extensive use, digital devices like computers often suffer performance declines, and some even experience sudden, complete breakdowns without warning. This phenomenon is particularly disturbing for individuals who heavily rely on these devices to carry out critical tasks. Although researchers have extensively probed the causes of computer breakdowns, detailed parameters influencing the lifespan of computers remain underexplored. This paper, therefore, aims to estimate the probability associated with the continuous functioning or failure of a computer system over a specified duration, and to examine risk factors associated with failure. Delving into the mysteries of computer longevity, data on 100 computers in a designated lab at an academic environment were examined.  Data was drawn from maintenance records as well as in-depth hardware assessments. Analysis revealed that, after a 4-year period of active usage, 73 of the computers remained operational, while 27 had malfunctioned. Survival analysis methods were employed to determine the probability of computers failing at specific points in time and to identify various factors contributing to early computer failure. The findings disclosed that at the two-year mark, the probability of computers remaining operational is 80%, decreasing to 62% at the three-year juncture. The median survival time was established at 3 years and 4 months. Furthermore, an analysis of causative factors revealed that computers with faulty motherboards and power supply units associates with a lower rate of survival, while computers with issues of hard drives, operating systems, and miscellaneous components has a higher rate of survival. This study provides comprehensive data-driven evidence that offers insights on the need to implement maintenance strategies to proactively extend the lifespan of computers.

The purpose of this study was to describe Academic Cyber Loafing in Management Departement Soegijapranata Chatolic University students in online learning because of the covid 19 pandemic. The instrument in this study was developed from previous research by adding two indicators, namely physical activity, and enrichment of knowledge as a novelty in this study. Methods of data collection using a questionnaire. The data were analyzed descriptively quantitatively and categorized into minor, serious, or not doing Academic Cyberloafing, and the causes were identified. The results of the study show that the level of Academic Cyberloafing is in a low category and the level of Academic Cyberloafing is in the high seriousness in the aspect of enrichment to knowledge, which means that students independently access the internet to enrich knowledge even by ignoring ongoing online learning. The research results found something different from the results in previous research, where this research found a positive impact of Academic Cyberloafing, namely enriching knowledge, while in previous research it was more towards a negative impact. Another thing is that physical activities are the cause of Academic Cyberloafing, which was not the case in previous research. This study found that students were “forced” to engage in Academic Cyber Loafing because of the sudden increase in demands for personal needs as a result of online learning, and changes in family income because parents were laid off or their businesses suffered setbacks as a result of the “COVID-19” pandemic. 

In the age of computing, there is a vast assortment of medical equipment and software available. Software and medical equipment that can be online connected to healthcare Information Technology (IT) systems are referred to as Internet of Medical Things (IoMT). This research study elaborates healthcare connectivity and its security issues to the different dimension of IoMT. During the pandemic situation in 2020-21 Covid, importance of virtualization and its dependencies have got the momentum. The security challenge of IoMT needs to be addressed. The research analysis is evaluating the impact of security factors in IoMT. By systematically evaluating research studies based on the keywords IoMT, security of IoMT, and security in healthcare sector, security attributes and factors were discovered from the different digital library. This evaluation uses soft computing and Artificial Intelligence (AI) techniques, quantitatively elaborates the factors of IoMT and their impact based on security. The results provide guidance for the development of IoMT with security attributes that can help to ensure the security of the device and software based applications on networks or in the cloud. To assess the importance of the criteria and the ranking of the alternatives, the AI technique of Analytic Hierarchy Process (AHP) and Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) were applied. The hybrid Fuzzy AHP, Fuzzy TOPSIS techniques are utilizing the concept of decision making in security of IoMT. The items were evaluated using a multi rules choice investigation with several standards. In this research study, eight factors and ten alternatives of IoMT were selected to determine their impact on security. The creating new funding, operating and business model factor of IoMT got the top weight and successfully navigating regulatory change got the least. The AI research on IoMT security determination helps the developer, medical practitioner, and medical device operator to consider the impact of security in IoMT. 

With the advancements of Deep Learning technologies, its application has broadened into the fields of food classification from image recognition using Convolutional Neural Network, since food ingredient classification is a very important aspect for eating habit recognition and also reducing food waste. This research is an addition to the previous research with a clear illustration for deep learning approaches and how to maximize the classification accuracy to get a more profound framework for food ingredient classification. A fine-tuned model based on the Xception Convolutional Neural Network model trained with transfer learning has been proposed with a promising accuracy of 95.20% which indicates a greater scope of accurately classifying food objects with Xception deep learning model. Higher rate of accuracy opens the door of further research of identifying various new types of food objects through a robust approach. The main contribution in the research is better fine-tuning features of food classification. The dataset used in this research is the Food-101 Dataset containing 101 classes of food object images in the dataset.