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Item Modeling and Parametric Analysis of Solar Energy Systems(UMT,Lahore, 2022-04)Renewable energy is becoming important due to increase in global warming caused by fossil fuel based energy generation. World population has signifi- cantly increased in the last two decades and growing industrial revolution has also been happening since 21st century. Hence, energy demand is multiplying due to increasing world population and growing industrial revolution with each passing year. Energy demand is being largely met by conventional fossil fuel energy generation sources, which results in higher greenhouse gas emissions and global warming. Researchers, scientists, and academicians pro- posed renewable energy generations viable for resolving the global warming issue. In renewable energy, solar energy is the most abundant energy source available to cater the issues of meeting global energy demand and reducing greenhouse gas emissions in the environment. Solar energy generation is achieved through photovoltaic panels and solar energy collectors with the help of small particles to capture energy from solar radiations.Item Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network(UMT,Lahore, 2022) ALI HAIDER KHANElectrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but are still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification