-74813b3e17f460286df-547e 20220407075336084 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 05 30 2022 11 1 Faculty of Computing, Engineering and Media, De Montfort University, United Kingdom. Naser Alazemi Abdullah M Al Tawari High Institute of Energy/Water Resources Department, Public Authority of Applied Education, Kuwait. The utilization of the wasted energy from power plants in power generation becomes a great challenge in recent times. This investigates the feasibility of using Organic Rankine Cycle (ORC) bottoming turbine to recover the energy generated from Al Zour South Power station in Kuwait. Both of qualitative and quantitative methods of data collection were used to collect the required data for this investigation. A block diagram was built for the new proposed model in which the location of the added ORC bottoming turbine is presented. The model includes four modules and each of them has different number of turbines. The amount of power generated per month by applying the new model with using two different extraction line capacities of 10% and 20% in addition to the produced power (1000 Mw) per month for each unit were measured and plotted. As a result, the four modules generated more power as the extraction line capacity increased to 20%. More profit was gained by module four at 10% extraction and it has the lowest rate of return which was 9 years. Based on these results, module 4 is the most suitable to be installed in Al Zour South Power station in Kuwait. 05 30 2022 47 52 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.A6916.0511122 https://www.ijrte.org/portfolio-item/a69160511122/ MBA Student, Talal Abu Ghazaleh University Collage for Innovations (TAGUCI), Amman, Jordan. Alaa Ghannam Jaber Alwidian Big Data Scientist, Department of Data Science and Artificial Intelligence, University of Petra (UOP), Amman, Jordan. Due to rapid changing in human lifestyles, a set of biological factors of human lives has changed, making people more vulnerable to certain diseases such as stroke. Stroke is a life-threatening disease leading to a long-term disability. It’s now a leading cause of death all over the word. As well as it’s the second leading cause of death after ischemic heart disease in Jordan. Stroke detection within the first few hours improves the chances to prevent complications and improve health care and management of patients. In this study we used patient’s information that are believed to be related to the cause of stroke and applied machine learning techniques such as Naive Bayes, Decision Tree, and KNN to predict stroke. Orange software is used to automatically process data and generate data mining model that can be used by health care professionals to predict stroke disease and give better treatment plan. Results show that decision tree classifier outperformed other techniques with accuracy level of 94.2%. 05 30 2022 53 59 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.A6900.0511122 https://www.ijrte.org/portfolio-item/a69000511122/