This survey emphasizes the significance of Explainable AI (XAI) techniques in detecting hateful speech and misinformation/Fake news. It explores recent trends in detecting these phenomena, highlighting current research that reveals a synergistic relationship between them. Additionally, it presents recent trends in the use of XAI methods to mitigate the occurrences of hateful land Fake contents in conversations. The survey reviews state-of-the-art XAI approaches, algorithms, modeling datasets, as well as the evaluation metrics leveraged for assessing model interpretability, and thus provides a comprehensive summary table of the literature surveyed and relevant datasets. It concludes with an overview of key observations, offering insights into the prominent model explainability methods used in hate speech and misinformation detection. The research strengths, limitations are also presented, as well as perspectives and suggestions for future directions in this research domain.

The exponential growth of multi-scale biomedical and behavioral data introduces both challenges and opportunities for Image 1 -driven analytics. Effectively managing the complexity and variability of these data sources requires advanced computational techniques for accurate interpretation and robust decision-making. Integrating Image 2 with colorimetric biosensing and multimodal data fusion offers scalable solutions that can improve diagnostic accuracy, enable early disease detection, and support personalized medicine. This work explores mobile-based colorimetry, an Image 3 -driven approach that uses image processing and Image 4 to detect colorimetric changes in chemical and biological solutions. We propose a modular conceptual framework that integrates mobile-based colorimetry with multimodal biomedical data, such as clinical, imaging, and environmental datasets, to develop scalable, low-cost tools for predictive modeling, real-time health monitoring, and personalized diagnostics. We review recent advancements in Image 5 -enabled colorimetric analysis and multimodal data fusion for healthcare applications, emphasizing innovations in Image 6 -assisted biosensors, Image 7 -driven biomedical imaging, and multimodal fusion techniques. In addition, we highlight the need for robust data management systems and interpretable AI/ML models to ensure security, privacy, and reliability in biomedical and behavioral research. This work also highlights practical directions for improving diagnostic accuracy and accessibility, particularly in resource-limited settings.

The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer’s disease (AD) is critical given the disease’s prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.

The intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women.

Adverse Drug Events (ADEs) are a major healthcare issue in the United States, contributing to millions of outpatient and emergency department visits and ranking as the fourth leading cause of death. While many ADEs are identified post-market, improved detection methods are crucial for enhancing patient safety. This study explores the application of large language models (LLMs) to the n2c2 task for ADE detection, evaluating optimal prompting techniques without requiring ADE-specific training data. Results indicate that an entity-only extraction approach outperforms the inline method, offering higher precision, recall, and token efficiency. This study highlights the potential of LLMs for accurate ADE detection in clinical text, improving performance while maintaining model efficiency.