The rapid increase in electric vehicles (EVs) and the widespread deployment of charging stations have made secure authentication a critical requirement in Vehicle-to-Grid (V2G) environments. The growing interconnection among EVs, charging stations, and grid infrastructure introduces serious security and privacy challenges, including impersonation, replay, ephemeral secret leakage, and physical tampering attacks. Although several authentication protocols have been proposed for EV charging services, many existing schemes lack robust tamper-resistant and location-aware authentication capabilities and remain unsuitable for dynamic and resource-constrained V2G conditions. To address these limitations, this paper proposes a robust, tamper-resistant, and location-sensitive authentication protocol for securing EV charging services in V2G environments. The proposed protocol integrates configurable Arbiter Physical Unclonable Functions (A-PUFs) to provide device-level protection against physical tampering and unauthorized charging access. It also employs lightweight cryptographic primitives and techniques, including one-way hash functions, XOR operations, concatenation operations, and timestamp-based freshness verification, to support efficient mutual authentication and secure session key agreement. The security of the proposed protocol is evaluated through informal analysis and formal verification under the Random Oracle Model (ROM). Furthermore, comparative analysis demonstrates that the proposed protocol achieves a 17.16% reduction in communication cost and a 7.49% reduction in average computation cost compared with existing authentication protocols while maintaining strong security features. The results confirm that the proposed scheme enhances the security, efficiency, and practical deployability of EV charging authentication for next-generation V2G networks.

Online platforms are nurturing social interactions, yet regrettably, they have also led to the proliferation of antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The identification of hate speech and aggression has become indispensable in the fight against cyberbullying and online harassment. Cyberbullying encompasses the use of aggressive and offensive language, including rude, insulting, hateful, and teasing comments, to inflict harm on individuals through social media platforms. Human moderation is both sluggish and costly, rendering it impractical in light of the exponential growth of data. Consequently, automated detection systems are imperative to effectively combat trolling. This study addresses the challenge of automatically discerning cyberbullying in tweets sourced from a publicly available cyberbullying dataset. The proposed methodology leverages the robustly optimized bidirectional encoder representations from transformers approach (RoBERTa), integrating principle component analysis (PCA) extracted global vectors for word representation (GLOVE) word embedding features. Furthermore, our proposed approach is benchmarked against state-of-the-art machine learning, deep learning, and transformer-based methods, utilizing the GLOVE word embedding technique. Statistical analyses reveal that our proposed model outperforms its counterparts, achieving a 0.98 accuracy and recall rate with 0.97 of precision and F1 score in detecting cyberbullying tweets. Results from$k$-fold cross validation further corroborate the superior performance of our proposed model.

Abstract Efficient word representation techniques (word embeddings) with modern machine learning models have shown reasonable improvement on automatic text classification tasks. However, the effectiveness of such techniques has not been evaluated yet in terms of insufficient word vector representation for training. Convolutional Neural Network has achieved significant results in pattern recognition, image analysis, and text classification. This study investigates the application of the CNN model on text classification problems by experimentation and analysis. We trained our classification model with a prominent word embedding generation model, Fast Text on publically available datasets, six benchmark datasets including Ag News, Amazon Full and Polarity, Yahoo Question Answer, Yelp Full, and Polarity. Furthermore, the proposed model has been tested on the Twitter US airlines non-benchmark dataset as well. The analysis indicates that using Fast Text as word embedding is a very promising approach.

Abstract Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset.

It is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECO-CNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92% precision, and 99.97% specificity.

With the advancement of mobile technologies, pedagogical innovations have been emerging since last two decades. The potential use of mobile devices has attracted many educators and researchers to conduct inquiry based learning (IBL) activities in a classroom or a fieldwork. In recent years, augmented reality (AR) technology has become widespread for conducting learning activities using mobile devices. In the literature related to science education, there are limited mobile systems found in which AR technology is used for IBL activities. We have therefore designed a mobile system MAPILS to conduct plant IBL activities using AR technology. Further, two application-related aspects of MSI (Mobile science inquiry) evaluation framework are used. For evaluation purposes, this system was tested with 42 local secondary high school students in order to identify the potential benefits of such technology in science education. The results showed that a designed mobile system possesses motivating and enjoyable learning experience for science students.