The healthcare sector faces several challenges, such as rising costs, rising demand, and the need for sustainability. A new area of healthcare has emerged due to these problems, focusing on long-term improvements in management, social policy, and health economics. This research explores the cutting edge of healthcare, concentrating on long-term advancements in management, social policy, and health economics. To better understand the problems affecting the healthcare sector and to pinpoint the areas where sustainable solutions are most required, a survey of 2000 healthcare professionals and policymakers was performed. The data were analyzed using structural equation modeling (SEM), and a thorough sustainable healthcare model was created. According to the survey's findings, the healthcare sector now faces three significant challenges: growing prices, increased demand, and the need for sustainability. According to the respondents, the three main areas where sustainable innovations are most required are management, social policy, and health economics. These conclusions were supported by the (SEM) analysis, which also showed that sustainable practices in these fields significantly impact the sustainability of the healthcare system. These findings lead this research to conclude that to guarantee the accessibility and affordability of healthcare for everyone, a move towards sustainable practices in health economics, social policy, and management is needed. Cooperation between healthcare providers, policymakers, and other stakeholders is required to create creative solutions that support sustainability in the healthcare sector. This study offers a thorough framework for sustainable healthcare that may act as a guide for further research and the formulation of new regulations.

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.

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.

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.

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.

Muscle fibers act as a source of force in the human body. The joint actions occur due to contraction of these muscle fibers controlled by central nervous system (CNS). Surface electromyography (EMG) signals produced during any muscular activity can play a vital role in measuring the muscular force under non-fatigued and fatigued conditions. Due to nonlinear, irregular, and continuous characteristics, sEMG signals need to be preprocessed to estimate muscle force correctly. The upper limb Biceps and Triceps muscles controls the movement of the elbow joints. Any disorder in this muscle can lead to difficulty in bending the elbow, turning palms in and out, and movement of arms. This research paper proposes different deep learning-based sEMG-to-force estimation models. The proposed models estimate the force from the sEMG signals acquired from the Biceps and Triceps muscles during maximal and submaximal contractions. The proposed deep learning models are based on hybrid 1D Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and 1D Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) approaches along with self-attention mechanism. The dataset contains raw sEMG signals acquired from the human arm’s Biceps and Triceps muscles. These signals are first preprocessed to remove DC offset and high frequency noises. The models are then trained with the pre-processed signals. Results of the proposed deep learning models are compared with the conventional machine learning algorithms including Support Vector regression (SVR) and Gaussian process regression (GPR) algorithms. Different performance metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R ² ) are utilized to measure the performance of the models based on the predicted and actual force. Results shows that the proposed CNN-LSTM and CNN-GRU models achieved better performance (MSE: 0.001, RMSE: 0.023, MAE: 0.017, and R ² : 0.987) and (MSE: 0.001, RMSE: 0.025, MAE: 0.019, and R ² : 0.985), respectively, as compared to GPR (MSE: 0.080, RMSE: 0.282, MAE: 0.162, and R ² : 0.856) and SVR (MSE: 0.008, RMSE: 0.082, MAE: 0.067, and R ² : 0.482). The proposed research study is helpful in developing a rehabilitation control system beneficial for people suffering with muscular disorder.