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A baby stroller is a vehicle that is used by people to carry their toddlers making their life easier or carrying out other activities. However, such a vehicle still requires human labor to carry from one place to another place. Moreover, carrying such a vehicle can be troublesome for elderly people, disabled persons, or pregnant women. We live in an era where every aspect of human life is becoming simpler with the use of an autonomous machine. The recent development in the field of Artificial Intelligence (AI) and Machine Learning (ML) has allowed us to achieve autonomous surveillance robots to autonomous self-driving cars. This advancement of AI and ML can also be implemented in such a way that can help a person carry their babies for them which reduces their labor. This paper proposes a human following baby carrier car that can follow a human without any kind of help from the person. This reduces the labor work for a person and provides much freedom to carry out their activities. Such a carrier can be operated by elderly people, disabled people, pregnant women, or by any other without the need for much labor. The proposed system relies on the computer vision analysis that uses the camera feed to detect the person to be followed and sends a command to a microcontroller that operates the Carrier.

Malaria is one of the most common diseases in the world. It is caused by Plasmodium parasites and spreads among humans by the bite of female Anopheles mosquitoes. Only in 2019, children under the age of five accounted for 67% of all malaria deaths. The diagnosing process of malaria can be error-prone due to an inexperienced pathologist and may take a longer time. In this paper, we have presented a malaria detection framework based on wavelet packet 2d, Convolutional Neural Network. (CNN), and Whale Optimization Algorithm (WOA). We have extracted a global feature set using the wavelet packet 2D and CNN. Further, we removed noisy features and selected an effective feature using WOA and XGBoost. The selected feature set is almost half of the initial feature set. Additionally, we have used SHapley Additive exPlanations (SHAP) to interpret the trained model and assess the significance of each feature. Based on the selected feature set, the XGBoost algorithm provides accuracy, precision, recall, and F1 score of 94. 78%, 94. 39%, 95. 21%, and 94. 80% respectively.

Every year, the number of motor dysfunction patients has been rising. These patients require physical therapy and continuous observation and assessment of their exercises by a professional therapist. This process can take a longer time, leading to a staff shortage and increasing financial costs. Thus, a reliable rehabilitation framework is necessary to assess these exercises as precisely as possible. In this paper, we have proposed an exercise assessment framework using the 1D Local Binary Pattern (LBP) to extract valuable features from skeleton data and a genetic algorithm (GA) optimized Convolutional Neural Network (CNN) to predict the score. The KIMORE dataset has been used in this study. We have achieved 0.0165 Mean Absolute Deviation (MAD) on the training set and 0.13515 on the validation set.