Over the past decennium, Wi-Fi fingerprinting based indoor localization has seized substantial attention. Room level indoor localization can enable numerous applications to increase their diversity by incorporating user location. Real-world commercial scale deployments have not been realized because of difficulty in capturing radio propagation models. In case of fingerprinting based approaches, radio propagation model is implicitly integrated in the gathered fingerprints providing more realistic information as compared to empirical propagation models. We propose ensemble classifiers based indoor localization using Wi-Fi fingerprints for room level prediction. The major advantages of the proposed framework are, ease of training, ease to set up framework providing high room-level accuracy with trifling response time making it viable and appropriate for real time applications. It performs well in comparison with recurrently used ANN (Artificial Neural Network) and kNN (k-Nearest Neighbours) based solutions. Experiments performed showed that on our real-world Wi-Fi fingerprint dataset, our proposed approach achieved 89% accuracy whereas neural network and kNN based best found configurations achieved 85 and 82% accuracy respectively.

Rainfall has a huge impact on agriculture because it's one of the key causes of crops devastation. Farmers face a slew of issues when unexpected heavy rains fall, as their planted crops are washed away or damaged. Pakistan is an agricultural country where new methods and techniques are needed to improve the traditional farming methods. This research intends to provide aid in the protection of crops from severe rains using machine learning to accurately anticipate the possibility of rainfall, which is a well-known agricultural problem. Various weather factors such as temperature, humidity, and atmospheric pressure can be used to predict rainfall patterns. Rainfall prediction can be used to identify and furnish future rainfall descriptions for agricultural planning for food security, allowing farmers to take precautionary measures to safeguard rice fields. Naïve Bayes, LogitBoost, RIPPER, Decision Stump, AdaBoost, Random Forest, Artificial Neural network, and K* were evaluated for rainfall prediction based on accuracy, precision, recall, F1-measure, Root Mean Squared Error, area under receiver operating characteristic curve, elapsed training time and elapsed testing time. The results obtained indicate that the best performance is achieved by Random forest with maximum accuracy of 83.2%, followed by ANN (82.5%), LogitBoost (82.2%), RIPPER (82%), naïve Bayes (80.3%), AdaBoost (80.2%), and K*(79.2%) respectively. K* The lazy approach involved a minimum of training time and a maximum of test time. Maximum training time was consumed by Random Forest and minimum testing time was taken by Decision Stump.

Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. This work explores how different computing platforms influence the accuracy of classification results when employing the same models. Heart disease, a widespread global health issue affecting both men and women, results from a complex interplay of lifestyle factors and genetics. Through visual representations, we examined the diverse factors influencing heart stroke occurrences. We employed multiple classification methods such as Logistic Regression, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT), assessing their accuracy using WEKA and Google Colab (using Scikit-Learn library). Our evaluation revealed that SVM achieves 77% accuracy when implemented using Scikit-Learn, demonstrating superiority over other methods. However, when using WEKA, both logistic regression and SVM demonstrated nearly 91% accuracy using the exact same hyperparameters. This research demonstrated the significance of platform selection in influencing classifier performance, offering valuable insights on how results reported in research can be impacted by the selection of the software and tools, using heart disease prediction as a use case scenario.

Machine Learning (ML) significantly drives the advancement of smart cities. This survey, using databases like IEEE Explorer, Web of Sciences, and Google Scholar, thoroughly investigated 22 papers published between 2021 and 2023. The focus was on identifying the prevalent ML models in smart cities and the specific sub-areas capturing the most attention. The study says that out of 22 research papers, about 63% used supervised learning techniques for smart city applications. The most common models were Naive Bayes and Support Vector Machines, especially in the areas of transportation, energy, environment, and healthcare. The industry has significance, due to its potential for conversion, especially with the urbanization of rural areas. This highlights the necessity for extensive future advancements. The results of this survey about the significance of machine learning in smart cities give us a path that will demand ongoing innovation to ensure the sustainable growth of both urban and rural areas going forward. Using machine learning, we can not only enhance the productivity of the city system but also increase the efficiency in diverse aspects of urban life.