Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.

Assessing the quality of agricultural products holds vital significance in enhancing production efficiency and market viability. The adoption of artificial intelligence (AI) has notably surged for this purpose, employing deep learning and machine learning techniques to process and classify agricultural product images, adhering to defined standards. This study focuses on the lemon dataset, encompassing ‘good’ and ‘bad’ quality classes, initiate by augmenting data through rescaling, random zoom, flip, and rotation methods. Subsequently, employing eight diverse deep learning approaches and two transformer methods for classification, the study culminated in the ViT method achieving an unprecedented 99.84% accuracy, 99.95% recall, and 99.66% precision, marking the highest accuracy documented. These findings strongly advocate for the efficacy of the ViT method in successfully classifying lemon quality, spotlighting its potential impact on agricultural quality assessment.

– Mutluluk bireyin yaşam kalitesini değerlendirme biçimidir ve ekonomik, sosyal, ulusal, kişisel vecoğrafi etmenlerden etkilenmektedir. Mutluluğu etkileyen bu faktörler makine öğrenmesi yöntemlerindekullanılarak dünya ülkelerinin mutluluğu tahmin edilebilmektedir. Bu çalışmada; makine öğrenmesialgoritmaları kullanılarak dünya ülkelerinin mutluluğu tahmin edilmiştir. Makine öğrenmesiyöntemlerinden SVM, Ensemble, GPR, Tree, Lineer Regression ve Neural Network algoritmalarıkullanılmıştır. Dünya ülkelerinin mutluluğunu tahminlemede en başarılı makine öğrenmesi algoritmasıvarsayılan hiperparametreler kullanıldığında 0.48281 RMSE değeriyle SVM algoritmasıdır. Daha sonrahiperparametre optimizasyonu yapılmıştır. Optimizasyon sonrasında ise en başarılı yöntem 0.45746 RMSEdeğeriyle yine SVM algoritmasıdır.