Human action recognition (HAR), deeply rooted in computer vision, video surveillance, automated observation, and human computer interaction (HCI), enables precise identification of human actions. Numerous research groups have dedicated their efforts to various applications and problem domains in HAR systems. They trained classification models using diverse datasets, enhanced hardware capabilities, and employed different metrics to assess performance. Although several surveys and review articles have been published periodically to highlight research advancements in HAR, there is currently no comprehensive and up-to-date study that encompasses architecture, application areas, techniques/algorithms, and evaluation methods as well as challenges and issues. To bridge this gap in the literature, this article presents a comprehensive analysis of the current state of HAR systems by thoroughly examining a meticulously chosen collection of 136 publications published within the past two decades. These findings have significant implications for researchers engaged in different aspects of HAR systems.

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

Depression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. Many techniques have been developed for this aim over the years. Identifying relevant publications on this topic using current academic search systems is challenging due to the rapid growth of research publications, unclear or limited search terms, and the complexity of citation networks. Several review papers have been published to ease this task by summarizing the methodologies, key findings, and recommendations for future research. However, most current reviews often do not provide a clear overview of the evolution, latest techniques, and challenges. This paper aims to address that gap by providing a comprehensive review of ML and DL methodologies for detecting depression on social media. We propose a generic architecture for these systems and present a detailed analysis of methodologies and datasets used for evaluation in this field. In addition, we highlight key open research areas, providing a useful starting point for further research and development. By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.

Purpose: How to provide the most useful papers for searchers is a key issue for academic search engines. A lot of research has been carried out to address this problem. However, when evaluating the effectiveness of an academic search engine, most of the previous investigations assume that the only concern of the user is the relevancy of the paper to the query. The authors believe that the usefulness of a paper is determined not only by its relevance to the query but also by other aspects including its publication age and impact in the research community. This is vital, especially when a large number of papers are relevant to the query.Design/methodology/approach: This paper proposes a group of metrics to measure the usefulness of a ranked list of papers. When defining these metrics, three factors, including relevance, publication age and impact, are considered at the same time. To accommodate this, the authors propose a framework to rank papers by a combination of their relevance, publication age and impact scores.Findings: The framework is evaluated with the ACL (Association for Computational Linguistics Anthology Network) dataset. It demonstrates that the proposed ranking algorithm is effective for improving usefulness when two or three aspects of academic papers are considered at the same time, while the relevance of the retrieved papers is slightly down compared with the relevance-only retrieval.Originality/value: To the best of the authors’ knowledge, the proposed multi-objective academic search framework is the first of its kind that is proposed and evaluated with a group of new evaluation metrics.

Human Action Recognition is a rapidly evolving field at the intersection of computational intelligence, signal processing, and machine learning, driving innovations in healthcare, fitness, surveillance, and smart environments. This review bridges the gap between classical techniques, such as Fourier Transform (FT), Wavelet Transform (WT), and Principal Component Analysis (PCA), and advanced Deep Learning (DL) approaches, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and emerging Transformer-based architectures. A key focus is the integration of multimodal sensing modalities, including accelerometers, gyroscopes, Electromyography (EMG), and electroencephalography (EEG), in conjunction with noncontact technologies such as thermal and infrared imaging, enhancing HAR adaptability in diverse environments. Unlike prior surveys, this work presents a comprehensive synthesis of HAR methodologies, systematically analyzing 217 studies to quantify trends, assess challenges such as noise resilience, real-time constraints, and scalability, and highlight innovative solutions, including advanced multimodal fusion frameworks and energy-efficient architectures. Furthermore, a research roadmap is proposed to advance scalable, real-time HAR systems with deeper multimodal data integration and lightweight computational models, paving the way for robust and adaptable solutions in real-world applications.

Given the rapid increase of textual data in various fields, text summarization has become essential for efficient information handling. Over recent decades, numerous methods have been proposed to enhance summarization processes, and various review papers and books have been published to encapsulate these methodologies and discuss their implications. However, existing reviews often fail to provide a comprehensive retrospective of recent advancements, particularly concerning detailed architectural frameworks, the field’s current state, evaluation methodologies, and unresolved challenges. This paper addresses this gap by presenting a detailed analysis of the extractive approach, encompassing its inherent strengths, limitations, and underlying mechanisms. We present a detailed, multi-layered architectural framework designed to advance and develop summarization models, thereby supporting researchers in their endeavors. Furthermore, this review categorizes domain-specific summarization techniques, focusing on unique challenges and tailored strategies for news, scientific articles, and social media. We emphasize the impact of evaluation metrics and benchmark datasets in performance assessment, providing a detailed analysis of the commonly utilized datasets and metrics in the current literature. This paper is a valuable resource for advancing text summarization techniques in natural language processing and machine learning by identifying future research directions and open challenges.