Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.

Background; Heart attack prediction is one of the serious causes of morbidity in the world’s population. The clinical data analysis includes a very crucial disease i.e., cardiovascular disease as one of the most important sections for the prediction. Data Science and machine learning (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc... can be considered. The objective of this study is to optimize the prediction of heart disease using ML. Methods: In this paper, we are presenting a machine learning-based heart attack prediction (ML-HAP) method in which the analysis of different risk factors and prediction for heart attacks is done using ML approaches of Support Vector Machines, Logistic Regression, Naïve Bayes and XGBoost. The data of heart disease symptoms has been collected from the UCI ML Repository and analysis has been performed on the data using ML methods. The focus has been on optimizing the prediction on the basis of different parameters. Results: XGBoost provided the best prediction among the four. The Area under the curve achieved with XGBoost is .94 and Logistic Regression is .92. The prediction with ML models in identifying heart attack symptoms is highly efficient, especially with boosting algorithms. The prediction was done to evaluate accuracy, precision, recall, and area under the curve. ML models are being trained to perform optimized predictions. Conclusions: This prediction can help clinically in analyzing the risk factors of the disease and interpretation of the patient scenario. Boosting the algorithm provided promising results to predict symptoms of heart disease. It can further be optimized by working further on risk factors associated with this condition.

Artificial Intelligence is providing immense areas to work with and areas like Deep Learning and Machine Learning is taking over many research areas nowadays. The analysis and prediction of time series with machine and deep learning techniques are providing very promising results in the field of healthcare. The future values can be predicted with the help of time series. Therefore, the prediction of time series in healthcare based financial management provides organization with the useful information that supports in decision making. In this paper, the time series prediction on healthcare financial data is done by implementing Long Short Term Memory approach of Neural Networks for prediction of output for the time series data to predict business capabilities. Temporal characteristics of healthcare financial data are analyzed for time series forecasting. From the results, it is evident that this model is highly feasible to analyze the data with high precision and accuracy.

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance.

Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.