There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.

Disease diagnosis is of the utmost importance in providing appropriate medical treatment. Genetic diseases, such as hemoglobinopathies and thalassemia, need to be diagnosed accurately and on time. Though Hb variants are diagnosed using a HPLC-based hemoglobin typing machine. appropriate interpretation of the data obtained is still necessary and this requires trained professionals. Machine learning helps to interpret the obtained data and in predicting the type of Hb variants, thus reducing the workload of health professionals. In this study, the obtained data are classified using the following classifiers, namely logistic regression, support vector classifier (SVC), k-nearest neighbor (KNN), Gaussian naï ve bayes, perceptron classifier, linear SVC, stochastic gradient descent, decision tree, random forest, and multi-layer perceptron. The pre-processing, visualization and the classification steps were implemented using Python 2.7 on an Intel Core i5 computer. The performance of each classifier was then tested by initially creating a confusion matrix. Indices including "precision," "recall," and "f1-score" were used to quantify the quality of each model. KNN, decision tree, and random forest show better classification results in comparison to the other classifiers. With a precision of 93.89%, recall of 92.78%, and f1-score of 93.33%, the decision tree and random forest classifiers prove to be better classifiers in predicting the Hb variants with a higher accuracy rate.

Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge representation and reasoning” is devoted to the representation of information about the environment in a way where a computer system can utilise it to accomplish difficult problems. This research is an extensive survey of the knowledge representation techniques used in smart agriculture, and specifically in the urban agricultural domain. Relevant articles on the knowledge base are extracted from the retrieved set to study the fulfillment of the criteria of the system. Various interesting findings were observed after the review. Spatial–temporal characteristics were rarely approached. A generalised representation technique to include all domains in agriculture is another issue. Finally, proper validation technique is found to be missing in such an ontology.

Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%.