In the context of a more linked and globalized society, the significance of proficient cross-cultural communication has been increasing to a position of utmost importance. Language functions as a crucial medium that establishes connections among people, corporations, and countries, thus demanding the implementation of precise and effective translation systems. This comprehensive review paper aims to contribute to the evolving landscape of AI-driven language translation by critically examining the existing literature, identifying key debates, and uncovering areas of innovation and limitations where the primary objective -is to provide a nuanced understanding of the current state of AI-driven language translation, along with emphasizing the advancements, challenges, and ethical considerations. In this review, ongoing debates surrounding AI-driven language translations were actively involved. By evaluating different viewpoints and methodologies, insights into unresolved questions that contribute to broader discourse in the field were provided. The future trajectory of this study is the incorporation of cross-lingual dialect adaptability and the advancement of Artificial Intelligence translation systems, with a focus on prioritizing inclusion and cultural understanding.

In the wake of the expanding digital realm, the imperative for robust cybersecurity measures has burgeoned significantly. This extensive investigation digs into the complicated realm of cybersecurity datasets, with the goal of improving our understanding and implementation of these critical tools. This study’s comprehensive evaluation of 37 distinct datasets shows a complicated world in which no one dataset stands out as totally suitable for all uses. A precise balance must be struck between crucial dataset qualities such as diversity, authenticity, and usefulness. Using a complete assessment technique, this paper illuminates the challenges and possibilities that developers and researchers face in the field of cybersecurity datasets. Although some databases accurately identify certain forms of cyberattacks, their coverage may not include the whole range of cyber threats. On the other hand, datasets with a strong emphasis on accurate portrayal may forgo comprehensiveness or practical use. This intricacy is heightened by the dynamic and sophisticated nature of cyber threats, emphasizing the delicate balance required between accuracy and practicality. The study emphasizes the necessity of selecting datasets strategically and contextually for cybersecurity studies, with the goal of matching research objectives with the most appropriate dataset selections. Furthermore, it emphasizes the need of continual cooperation and innovation within the cybersecurity community in developing datasets that accurately represent the ever-changing nature of cyber threats. After analyzing 37 cybersecurity datasets, it is obvious that no one dataset can meet all of the field’s unique demands, demonstrating the need of a flexible, adaptable, and developing dataset for intrusion detection systems (IDS). This inquiry offers a critical assessment of dataset characteristics and their related issues, providing essential insights for academics, professionals, and dataset creators, enabling the construction of a more resilient and adaptable cybersecurity infrastructure.

This review examines the ethical, social, and technical challenges posed by AI-generated text tools, focusing on their rapid advancement and widespread adoption. An exhaustive literature search across many databases, strict inclusion/exclusion criteria, and a rigorous analysis procedure are all parts of our systematic review technique. This guarantees an impartial and complete study of the current status of AI-generated text tools. The study analyzes prominent language models, including GPT-3, GPT-4, LaMDA, PaLM, Claude, Jasper, and Llama 2, evaluating their capabilities in natural language processing and generation. The analysis reveals significant advancements, with GPT-3 demonstrating a 92% accuracy rate on standard natural language understanding benchmarks, outperforming LaMDA (88%) and PaLM (85%). To illustrate real-world implications, the review presents a case study of ChatGPT's application in healthcare, where it achieved 80% consistency with expert opinions in assessing acute ulcerative colitis. This case highlights both the potential benefits and ethical concerns of AI in critical domains. Quantitative bias analysis shows that GPT-3 generated biased content in 15% of test cases involving sensitive topics, a higher rate than LaMDA (12%) and PaLM (10%). We provide an in-depth analysis of fairness and bias issues, particularly in image generation tasks depicting professional roles. Our research synthesizes insights from technical advancements, ethical considerations, and real-world applications across healthcare, education, and creative sectors. We address critical privacy concerns and data protection challenges, noting struggles in AI-generated text detection and investigating AI's potential in enabling cyberattacks. We underscore the need for comprehensive governance systems and multidisciplinary cooperation. To provide a cohesive analysis of the ethical considerations surrounding AI-generated text tools, we employ a multifaceted ethical framework drawing on established theories. Utilitarianism, which seeks to maximize happiness for everyone; deontology, which places an emphasis on right and wrong; and Virtue Ethics, which analyzes the moral nature of deeds and actors, are all included in this framework. In this article, we use this approach to investigate AI ethics from a variety of angles, including privacy, prejudice, and social implications, as well as concerns of justice and fairness. Moreover, the study critically examines existing and proposed legal frameworks addressing AI ethics, identifying regulatory gaps and proposing adaptive policy recommendations to address the unique challenges posed by AI-generated text tools. Our review contributes a critical analysis of AI-generated text tools, their impacts, and the need for responsible innovation. The study provides precise guidelines for the ethical development and implementation of AI, highlighting the need to strike a balance between technical progress and ethical concerns to guarantee that AI technologies have a beneficial effect on society while protecting human values. The emergence of generative artificial intelligence (AI) signifies a substantial revolution in our methods of interacting with language and information.

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors.

With the widespread adoption of face masks due to global health crises and heightened security concerns, traditional face recognition systems have struggled to maintain accuracy, prompting significant research into masked face recognition (MFR). Although various models have been proposed, a comprehensive and systematic understanding of recent deep learning (DL)-based approaches remains limited. This paper addresses this research gap by providing an extensive review and comparative analysis of state-of-the-art MFR techniques. We focus on DL-based methods due to their superior performance in real-world scenarios, discussing key architectures, feature extraction strategies, datasets, and evaluation metrics. This paper also introduces a structured methodology for selecting and reviewing relevant works, ensuring transparency and reproducibility. As a contribution, we present a detailed taxonomy of MFR approaches, highlight current challenges, and suggest potential future research directions. This survey serves as a valuable resource for researchers and practitioners seeking to advance the field of robust facial recognition in masked conditions.