Muhammad Shuaib Qureshi

Bone deterioration from osteoporosis creates fractures that primarily affect females who have reached menopause and older adults. Early detection of osteoporosis requires affordable methods because current diagnostic systems are both expensive and challenging to use. The current application of deep learning models for bone radiography analysis faces three major limitations: shallow architecture, insufficient dataset adaptation, and inadequate feature extraction methods. Talk-based machine learning techniques rely heavily on human-generated feature construction; however, they fail to recognize the delicate image patterns that are present in medical contexts. VGG-16 provides strong power, but businesses must invest in advanced and expensive hardware platforms while maintaining challenges with data quantity. The detection of osteoporosis in knee X-rays through deep learning and the ResNet-50 model using transfer learning remains our primary proposal for solving existing issues. An overview of 372 X-ray images with verified medical information was constructed for T-score ratings determined using a quantitative ultrasound system. Following fine-tuning of our model based on ResNet-50, we achieved 90% accuracy, which exceeded all alternative models tested, including VGG-16 with 88% accuracy using fine-tuning, 80% without fine-tuning, a 3-layer CNN at 66%, ResNet-18 at 79%, and non-fine-tuned ResNet-50 at 83%. The clinical value of the ResNet-50 model stems from its high accuracy, strong sensitivity, and specificity rates. The detection reliability of osteoporosis by the deep transfer learning CNN ResNet-50 has been proven through these findings, which provide healthcare practitioners with an effective diagnostic tool for early detection and prevention of fractures. Our research indicates that deep learning using transfer learning strategies produces a crucial enhancement in medical imaging systems, leading to better patient results.

The economy of a country is directly proportional to the power sector of that country. An unmanaged power sector causes instability in the country. Pakistan is also facing this phenomenon due to uncontrolled power outage and circular debt. Pakistan’s power sector is analyzed as a case study to find out the root cause for the unmanaged power sector and for proposing the most effective data-driven solution. After a literature review and discussion with domain experts, it was found that inaccurate power demand forecast is one of the main reasons for power crisis in Pakistan. Under-forecasting caused load shedding, and over-forecasting increased circular debt due to idle capacity payments. Previously, traditional statistical methods were used for power demand forecasting. The multiple linear regression model that is being used since 2018 (IGCEP) uses features such as previous year load and demographic and economic variables for long-term peak power demand forecasting till 2030. The problem is that the independent variables used in existing models are manipulated and cause a gap between actual and forecasted power demand. Moreover, even yearly peak power demand is not absolutely linear in nature; hence, it is necessary to apply AI-based techniques that can handle nonlinearity effectively. Not using system-generated data, not using the most appropriate features, not using an appropriate forecasting time horizon, and not using the appropriate forecasting model are main reasons for inaccurate peak power demand forecasting. The issue can be resolved by forecasting monthly peak power demand for the next 5 years by using the National Power Control Center’s (NPCC) system-generated data. Accurate monthly peak load forecasting leads to accurate yearly peak power demand. The monthly peak load forecasting strategy not only helps in managing operational issues of the power sector such as fuel scheduling and power plant maintenance scheduling but also guides decision-makers toward power and transmission expansion or contraction in the long term. More accurate monthly peak power demand forecasting can be achieved by applying nonlinear AI models in a comprehensive dataset comprising new engineered features, climate features, and the number of consumers. All these features are mostly system-generated and cannot be manipulated. As a result, the accuracy is improved and the results are more reliable than those of the existing models. The new features can be engineered from recent monthly peak load data generated by the system operator (NPCC). Climate features are collected from the Meteorological Department of Pakistan through sensors or database connectivity. The number of electricity consumers can be extracted from NEPRA’s state-of-industry report. All three datasets are combined on a common key (month–year) to a comprehensive dataset, which is passed through different AI models. In the experimental setup, it is found that support vector regression (SVR) produces the most accurate results, with an R-square of 99%, RMSE of 28, and MAPE of 0.1355, which are the best results compared to the literature reviewed.