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Electric vehicles (EVs) are rising in popularity due to technological developments and the nation’s decarbonization efforts, which are enticing drivers to move away from gasoline-dependent transportation. However, a major challenge in EV charging is optimal day-ahead charging planning under time-varying conditions, such as fluctuating charging prices and traffic conditions. Additionally, these conditions can lead to uncertain energy consumption while driving an EV. Predicting the energy consumption of an electric vehicle is complicated because it is influenced by more factors than a typical battery, such as driving habits and traffic conditions. This paper proposes an optimal EV charging planning strategy using an artificial intelligence-based energy consumption prediction model to address this problem. The energy consumption prediction model, based on long short-term memory (LSTM), was developed and validated against other artificial intelligence models. The LSTM-based model uses real-time training, making it suitable for uncertain scenarios encountered by EVs. The predicted results and traffic information are considered as weight factors in solving the EV charging planning and routing optimization problem. The optimization problem is formulated as mixed integer linear programming. Simulation studies under various driving and traffic conditions validate the proposed optimal charging planning and routing method. Since travel time can vary according to driving characteristics and traffic conditions over the same driving distance, the results confirm that the proposed method can predict energy consumption more accurately than other methods. It also selects routes with lower energy consumption relative to total driving time and provides stable operation.

Background Acid-fast bacilli  Mycobacterium tuberculosis and Mycobacterium leprae  are the causative organisms behind two major diseases of developing nations, tuberculosis and leprosy, respectively. To efficiently tackle these diseases in developing nations, drugs must be augmented with improved detection modalities. This necessitates the development of enhanced tools that can aid the current detection modalities being used in high-incidence areas. A no-code artificial intelligence model based on image classification is one such tool that can be used in the identification of acid-fast bacilli. This study utilizes three such no-code artificial intelligence models that originate from three different platforms but share identical training, testing, and subsequent evaluation. Thereafter, the study is directed at comparing the three models created and identifying the one that can function as a promising support system for the detection of acid-fast bacilli. Methods To begin with, a total of 1000 images per class, i.e., positive and negative for each disease, were captured from the diagnosed slides of tuberculosis and leprosy, taken from the Department of Pathology. Subsequently, these slides were reviewed again by a pathologist to demarcate them as positive or negative for acid-fast bacilli. Once the required number of images was captured, 600 images of each class were selected as the training set, 300 images as the testing set, and the remaining 100 images as the evaluation set. Data augmentation was then performed using techniques such as rotating, mirroring, cropping, and position shifting. These designated data sets were then used to train the image classification software available on the following three platforms: Lobe (Microsoft Corporation, Redmond, Washington, United States), Create ML (Apple Inc., Cupertino, California, United States), Python-based open-source software (PerceptiLabs, Stockholm, Sweden). The final evaluation was based on different parameters such as sensitivity, specificity, ease of use, learning curve, technological resources required, and feasibility of implementation. All parameters put together served the purpose of comparison to identify the most promising model. Results Out of the three models tested, the one built using Lobe is the most promising in terms of the evaluation parameters considered. For tuberculosis, the sensitivity and specificity values obtained were 96% each, while for leprosy, they were 100% and 96%, respectively. Also, the model built using Lobe had a near-negligible learning curve, in addition to being the most cost-effective and feasible model to implement. Furthermore, it had a unique real-time training feature, which constantly improved the model throughout the testing period, till the final sensitivity and specificity values were achieved. Conclusions In clinical situations where a high number of cases are encountered each day, a no-code artificial intelligence model built using Lobe would get exposed to a huge database, getting trained in real time. Subsequently, such a model would reach considerable levels of sensitivity and specificity and in turn, act as a promising support system for the detection of acid-fast bacilli., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Arya et al.)