The increasing adoption of electric vehicles (EVs) necessitates an understanding of their driving behavior to enhance traffic safety and develop smart driving systems. This study compares classical and machine learning models for EV car following behavior. Classical models include the Intelligent Driver Model (IDM), Optimum Velocity Model (OVM), Optimal Velocity Relative Velocity (OVRV), and a simplified CACC model, while the machine learning approach employs a Random Forest Regressor. Using a real world dataset of an EV following an internal combustion engine (ICE) vehicle under varied driving conditions, we calibrated classical model parameters by minimizing the RMSE between predictions and real data. The Random Forest model predicts acceleration using spacing, speed, and gap type as inputs. Results demonstrate the Random Forest's superior accuracy, achieving RMSEs of 0.0046 (medium gap), 0.0016 (long gap), and 0.0025 (extra long gap). Among physics based models, CACC performed best, with an RMSE of 2.67 for long gaps. These findings highlight the machine learning model's performance across all scenarios. Such models are valuable for simulating EV behavior and analyzing mixed autonomy traffic dynamics in EV integrated environments.

Word embeddings have revolutionized NLP by capturing the semantic associations between words effectively. However, sentence embeddings present notable benefits in the context of advanced language comprehension. While these developments have impacted high-resource languages like English, low-resource languages like Bangla have not benefited as much. The objective of this study is to address this disparity by creating sentence embeddings for the Bangla language, to improve information retrieval, sentiment analysis, content suggestion, etc. In this study, we developed Bangla Sentence-BERT by fine-tuning it on novel datasets generated through machine translation and utilizing diverse open-source datasets. The approach we used consisted of utilizing the stsb-xlm-r-multilingual as the teacher model and XLM-RoBERTa (XLMR) as the student model for multilingual interpretation. We evaluated the efficacy of our suggested approach on multilingual sentence-BERT models and classical machine learning algorithms. The performance of our model was remarkable as it achieved an accuracy of 97% on real text classification. The results demonstrate the efficacy of our Bangla sentence transformer model in comprehending meaning and its potential for a range of Bangla natural language processing applications, such as text classification.

In order to infer topics from unstructured text data, topic modeling techniques is extensively employed in the field of Natural Language Processing. Latent Dirichlet Allocation (LDA), a popular technique in topic modeling, can be used for the automatic identification of topics from a vast sample of textual documents. The LDA-based topic models, however, may not always yield good outcomes on their own. One of the most efficient unsupervised machine learning methods, clustering, is often employed in applications like topic modeling and information extraction from unstructured textual data. In our study, a hybrid clustering based approach using Bidirectional Encoder Representations from Transformers (BERT) and LDA for large Bangla textual dataset has been thoroughly investigated. The BERT has done the contextual embedding with LDA. The experiments on this hybrid model are carried out to show the efficiency of clustering similar topics from a noble dataset of Bangla news articles. The outcomes of the experiments demonstrate that clustering with BERT-LDA model would aid in the inference of more coherent topics. The maximum coherence value of 0.63 has been found for our noble dataset using LDA and for BERT-LDA model, the value is 0.66.

The field of sentiment and emotion analysis is a challenging problem that has received research attention. The complexity of emotion and sentiment recognition draws from variability in expression, cultural and individual differences, context dependency, etc. This work takes an exploratory approach to the problem by performing an extensive classification of emotion using machine learning (ML) applied to textual and auditory data sources. We create a pipeline that facilitates the examination of textual and auditory inputs, resulting in more reliable emotional classification. The study uses multiple audio and textual datasets for the prediction of four distinct emotions. A four-layer Bi-LSTM model achieved 95% accuracy in emotion analysis from auditory clips. The training set contained 2391 samples, with Angry (20%), Fearful (18%), Happy (38%), and Neutral (24%). In the validation set of 713 samples, emotions were similarly distributed. The test set had 312 samples, with percentages of emotions comparable to the training set. We merged four datasets for textual analysis and utilized the "emotion english distilroberta base" model [5], achieving 90% accuracy on the test data. In the training set, emotions were distributed as follows: Angry (25%), Fearful (23%), Happy (23%), and Neutral (29%). The validation set comprised 305 samples, with similar distributions across emotions. The test set consisted of 712 samples, with percentages of emotions similar to the training set. We develop an application that combines both classifications to obtain a robust classification of arbitrary audio tracks.

This paper investigates the use of ML models (KNN, DT, RF, MLP) for predicting CPU burst times based solely on submission-time attributes. These models are benchmarked against the exponential averaging (EA) method using the GWA-T-4 AuverGrid dataset. The study evaluates not only prediction accuracy but also inference latency for real-time scheduling. While KNN achieves high accuracy, the DT model shows the best trade-off between accuracy (MAE 3514.09, CC 0.84) and latency (0.0033 ms/sample). Confidence intervals and robustness testing confirm the feasibility of using ML for real-world CPU scheduling.