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In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal–dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant’s private medical information.

Water is one of the most critical and finite resources on our planet. As the demand for freshwater continues to grow, effectively managing and purifying existing water sources becomes increasingly important. This study introduces a Machine learning-based approach for enhancing water quality monitoring and anomaly detection in treatment plants using a modified Quality Index (QI). The proposed method integrates an encoder-decoder architecture with real-time anomaly detection and adaptive QI computation, providing a dynamic evaluation of water quality. In addition to developing this model, we present a comparative analysis with several existing machine learning models, demonstrating the effectiveness of our approach in detecting water quality anomalies. The revised QI is continuously updated using real-time sensor data, aiding decision-making in treatment operations. Experimental results show that the proposed model achieves superior performance, with an accuracy of 89.18%, precision of 85.54%, recall of 94.02%, Critical Success Index of 93.42%, Matthews Correlation Coefficient of 88.40%, delta-P of 94.37%, and Fowlkes–Mallow’s Index of 89.47%. These results highlight the model’s strong predictive capability and its practical utility in improving water treatment plant efficiency. By combining Machine learning with adaptive quality assessment, this study contributes to advancing intelligent monitoring solutions in water management.

This paper presents AquaFlowNet, a machine learning-based algorithm for real-time wastewater flow management. It addresses critical challenges related to operational efficiency, resource optimization, and environmental sustainability. Wastewater management systems require innovative methods for dynamic and efficient flow control to meet growing demands driven by urbanization, climate change, and increasingly stringent regulations. However, most existing methods rely on static or rule-based models, which lack the flexibility to handle fluctuating flow rates, variable environmental loads, and unforeseen disruptions. These limitations often lead to inefficiencies such as energy wastage, treatment delays, and overflow incidents, negatively impacting system performance and sustainability.AquaFlowNet leverages state-of-the-art machine learning algorithms to analyze real-time data from sensors, forecast flow variations, and optimize wastewater treatment processes. By integrating predictive analytics with intelligent control strategies, it enhances resource efficiency, prevents overflow events, and ensures regulatory compliance. Experimental evaluations demonstrate that AquaFlowNet outperforms conventional approaches in prediction accuracy and operational efficiency, reducing energy consumption, improving treatment effectiveness, and mitigating environmental impacts.The results highlight AquaFlowNet’s potential to revolutionize wastewater management systems, making them more resilient, adaptive, and beneficial for urban and industrial applications.

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.

The use of numerous sensors on edge devices, combined with the emergence of AI techniques, makes the IoT environment more intelligent and interactive. The resulting paradigm encompasses device-centric systems that operate instantly and remotely with zero clicks. However, with these advantages, many functional challenges affect remote sensing, including incomplete data, communication delay, lack of context awareness, and dynamically switching topology. To address these challenges, we have proposed a novel scheme, \"LLM-Enabled Adaptive Scheduling in IoT Sensing for Optimized Network Performance (LLM-AS).\" This scheme uses LLM to adjust the system's sensing to avoid redundant and useless data sending and enhance decision-making for optimized network resources. First, LLM-AS is trained with a defined data set for different parameters, such as packet loss trends, time-based fluctuations, event triggers, network failure patterns, and congestion signals with contextual decisions. Then, this scheme is deployed in a dynamic remote monitoring system for learning and updating task descriptions to utilize the feedback for future decisions and enhance the system performance. Evaluation of LLM-AS on various parameters using the CASAS dataset shows that the optimization functions of LLM are useful and make the IoT more usable. The LLM-AS optimization function confirms an improvement of 57.8% to 60% in MTP, a decrease of 26% to 60% in median delay, and an optimized energy solution with a confidence interval of 95% and a very small error margin. It also indicates that the precision score is about 0.86, the recall score is about 0.82, and the RMSE is about 0.21; all these values suggest high separability for varying conditions of IoT systems in dynamically changing situations.

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.

IntroductionDetermining the HER2 status accurately is a critical determinant in breast cancer treatment planning. Manual scoring remains highly susceptible to inter- and intra-observer variability, particularly in diagnostically ambiguous cases (HER2 1+/2+). Furthermore, contemporary deep learning models frequently lack the robustness and interpretability required for safe clinical integration.MethodsWe propose a hybrid ensemble framework that synergizes EVA-02-Large, Vision Transformer-Base, and ConvNeXt-V2-Nano architectures via an adaptive late-fusion mechanism. The model was trained on the expert-annotated HER2-IHC-40x dataset ( patches) and rigorously evaluated across ten independent trials. The experimental protocol incorporated comprehensive perturbation analysis across 20+ image corruption types and multi-perspective Explainable AI (XAI) assessment using Grad-CAM, Grad-CAM++, and Layer-CAM to validate alignment with pathologist-recognized morphological features.ResultsThe fused ensemble achieved state-of-the-art weighted accuracy ( ) and Balanced Accuracy ( ), outperforming the strongest single backbone by 0.45%. Misclassification rates for equivocal classes decreased by >18%. The framework demonstrated high resilience to structural perturbations ( ) while exhibiting expected sensitivity to extreme photometric variations. XAI analysis confirmed that model attention consistently prioritizes clinically relevant membrane staining patterns, mirroring expert diagnostic reasoning.ConclusionThis study establishes a highly accurate and interpretable pipeline for automated HER2-IHC scoring, demonstrating that hybrid transformer-CNN ensembles can effectively resolve diagnostically challenging cases. By combining superior predictive performance, structural robustness, and transparent decision-making, the proposed framework offers a highly promising foundational framework that, following prospective multi-reader clinical validation, could serve as a robust decision-support tool to standardize HER2 assessment, reduce inter-observer variability, and accelerate precision oncology workflows.

Effective landslide monitoring is essential for mitigating risks to infrastructure and communities, particularly in geologically unstable regions. Traditional monitoring methods, such as ground surveys and visual inspections, are time-intensive and lack early detection capabilities. To address these limitations, this study employs feature fusion and enhanced Deep Convolutional Neural Networks (DCNNs) for landslide detection. The model is built upon a fine-tuned, pre-trained VGG16 architecture, adapted to a new landslide dataset. Key modifications include the integration of a spatial attention mechanism, optimized learning rate schedules, attention-based Global Average Pooling (GAP), and the Lookahead Adam optimizer, all aimed at improving feature extraction, model convergence, and generalization. Experimental results demonstrate that the proposed approach achieves high accuracy, with performance ranging from 90% to 96% across different datasets and training iterations. Using the Kaggle Landslide Dataset, the model attained a training accuracy of 93%, with validation and testing accuracies of 95.2% and 95.8%, respectively. Comparable results were observed with the NASA Landslide Inventory, confirming the robustness of the method. The findings highlight the potential of DCNN-based models, augmented with attention mechanisms, as a reliable and efficient tool for landslide monitoring, significantly outperforming conventional assessment methods.

Wireless sensor networks (WSNs) are used for recording the information from the physical surroundings and transmitting the gathered records to a principal location via extensively disbursed sensor nodes. The proliferation of sensor devices and advances in size, deployment costs, and user-friendly interfaces have spawned numerous WSN applications. The WSN should use a routing protocol to send information to the sink over a low-cost link. One of the foremost vital problems is the restricted energy of the sensing element and, therefore, the high energy is consumed throughout the time. An energy-efficient routing may increase the lifetime by consuming less energy. Taking this into consideration, this paper provides a multi-criteria clustering and optimal bio-inspired routing algorithmic rule to reinforce network lifetime, to increase the operational time of WSN-based applications and make robust clusters. Clustering is a good methodology of information aggregation that increases the lifetime by group formation. Multi-criteria clustering is used to select the optimal cluster head (CH). After proper selection of the CH, moth flame and salp swarm optimization algorithms are combined to analyze the quality route for transmitting information from the CH to the sink and expand the steadiness of the network. The proposed method is analyzed and contrasted with previous techniques, with parameters such as energy consumption, throughput, end-to-end delay, latency, lifetime, and packet delivery rate. Consumption of energy is minimized by up to 18.6% and network life is increased up to 6% longer compared to other routing protocols.