Mohd Anjum

The artificial intelligence (AI)-enhanced Blockchain telehealth framework proposes a new architecture that addresses remote healthcare delivery challenges through AI and Blockchain technologies, making services secure, intelligent, and transparent. Traditional telehealth delivery models face significant data security, privacy, and trust concerns. This study addresses these issues by applying AI predictive analytics to assess service delivery risk among patient populations through patterns of telehealth utilization and accessibility across demographic, ethnic, and geographic cohorts. After collecting and preprocessing patient information from the Telehealth Services for Medicaid and CHIP Population dataset, a Gradient Boosting Machine model was developed, achieving 99.96 percent accuracy, 99.89 percent precision, 99.98 percent recall, and a 99.93 percent F1-score. The experimental evaluation showed promising performance, with very low gas consumption of about 22,500 units per transaction, a latency of 0.013 seconds, and a strong encryption throughput of 57.75 MB/s. These results confirm that the framework is scalable and highly efficient. Overall, the proposed framework would improve security, reliability, and interoperability in the telehealth ecosystem, supporting more robust, resilient, and patient-centered sustainable digital health initiatives.

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems.

Objective: The study's overarching goal is to improve E-health monitoring systems’ precision and performance by developing and implementing an Rendezvous Data Processing Model (RDPM) that is compatible with IoT-cloud architecture. The approach solves a problem with current E-health systems; these systems frequently make incorrect or redundant suggestions because they depend too much on static analytic methods and isolated data augmentation. Methods: The RDPM system recommended improves real-time decision-making by digesting historical suggestions and present analytical flaws. Divided features and data streams allow it to validate new hypotheses by comparing them to earlier observations. The state learning process has been improved by earlier efforts to avoid errors and data duplication, the model must distinguish intervening and non-intervening data. Internet-connected sensors collect massive volumes of patient and environment data. Cloud analytics evaluates the system's precision using these data. Results: Experimental results show that RDPM reduces data interruptions, analytical errors, and recommendation ratios while improving decision correctness. The model shows that it can quickly interpret many input streams without compromising accuracy. Compared to IoT-based healthcare analytics, the RDPM improves suggestion accuracy and reduces computing redundancy. Conclusion: IoT-cloud technologies with the RDPM system establish an adaptive and scalable platform for sophisticated E-health monitoring. State learning and dynamic data validation allow RDPM to make more accurate and convenient health recommendations. This approach allows a healthcare system to self-improve, understand context, and manage massive, real-time datasets.

Background/Objectives: The earlier, more accurate, and more consistent prediction of the brain tumor recognition process requires automated systems to minimize diagnostic delays and human error. The automated system provides a platform for handling large medical images, speeding up clinical decision-making. However, the existing system is facing difficulties due to the high variability in tumor location, size, and shape, which leads to segmentation complexity. In addition, glioma-related tumors infiltrate the brain tissues, making it challenging to identify the exact tumor region. Method: The above-identified research difficulties are overcome by applying the Swin-UNet with cuttlefish-optimized attention-based Graph Neural Networks (SCAG-Net), thereby improving overall brain tumor recognition accuracy. This integrated approach is utilized to address infiltrative gliomas, tumor variability, and feature redundancy issues by improving diagnostic efficiency. Initially, the collected MRI images are processed using the Swin-UNet approach to identify the region, minimizing prediction error robustly. The region’s features are explored utilizing the cuttlefish algorithm, which minimizes redundant features and speeds up classification by improving accuracy. The selected features are further processed using the attention graph network, which handles structural and heterogeneous information across multiple layers, improving classification accuracy compared to existing methods. Results: The efficiency of the system, implemented with the help of public datasets such as BRATS 2018, BRATS 2019, BRATS 2020, and Figshare is ensured by the proposed SCAG-Net approach, which achieves maximum recognition accuracy. The proposed system achieved a Dice coefficient of 0.989, an Intersection over Union of 0.969, and a classification accuracy of 0.992. This performance surpassed the most recent benchmark models by margins of 1.0% to 1.8% and with statistically significant differences (p < 0.05). These findings present a statistically validated, computationally efficient, clinically deployable framework. Conclusions: The effective analysis of MRI complex structures is used in medical applications and clinical analysis. The proposed SCAG-Net framework significantly improves brain tumor recognition by addressing tumor heterogeneity and infiltrative gliomas using MRI images. The proposed approach provides a robust, efficient, and clinically deployable solution for brain tumor recognition from MRI images, supporting accurate and rapid diagnosis while maintaining expert-level performance.

Objectives: Brain tumors have been a major factor in the development of mental disorders like anxiety and depression. The primary target of this research is to develop a smart, economically feasible system that can detect and classify brain tumors by analyzing MRI images, reducing manual labor, and shortening diagnostic time. Methods: Although diagnosis with medical imaging is efficient for various health issues, accurately classifying brain tumors remains challenging for medical experts. This research introduces a new energy channel-based hybrid optimized network (ECHO-Net) as an automatic identification tool for brain tumors, aiming to facilitate medical diagnosis remotely. Energy shape prior segmentation (ESPS) is part of the framework, providing accurate segmentation and cropping of tumor regions from MRIs. Channel and spatial attention-based neural network (CSA-Net) differentiates between normal and tumor-affected images. Additionally, the hybrid chimp-based whale optimization (HCWO) algorithm enhances prediction precision and optimally sets sigmoid activation function parameters for better convergence and generalization. Results: Performance evaluation of ECHO-Net using publicly available MRI brain datasets, including Figshare, BRATS 2018–2020, and a clinical MRI dataset, shows a peak signal-to-noise ratio of 41.87 dB, a Structural Similarity Index Measure of 0.992, a sensitivity of 99.4%, an accuracy of 99.2%, and an average computational time of 2.68 s, outperforming current state-of-the-art methods. Conclusion: The proposed ECHO-Net is an automated, accurate, fast, and robust system for brain tumor detection. With low computational costs, it effectively segments abnormal regions and recognizes tumor types, demonstrating strong potential as a tool in intelligent healthcare systems and real-world clinical applications.

With growing urbanization, there are increasing demands on waste management systems that can be performed in an environmentally friendly way as well as efficiently. Current approaches to managing waste often have issues with efficiency, transparency, and engaging with the public. Blockchain technology has been identified as one potential solution to these problems because it offers several benefits including decentralization, security, and transparency. The selection of the best blockchain-based waste management (BBWM) system is very difficult due to the many different evaluation criteria that may conflict with each other. Therefore this research uses a multi-criteria decision making (MCDM) approach using CIMAS (Criteria Importance Assessment), for determining weights based upon subjective input, and LOPCOW (Logarithmic Percentage Change-Driven Objective Weighing), for determining weights based upon objective data within the MCDM framework. To rank alternatives effectively, an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) technique is applied, ensuring a precise evaluation process. The use of T-Spherical Fuzzy Sets (T-SFS) captures all three (membership, non-membership, hesitation degree) and is used to address the variability that exists when making an expert judgment. Some of the key factors include; Technological Feasibility, Operational Costs, Scalability, Data Security, Regulatory Compliance, Environmental Impact. Based on the evaluation criteria, it appears that the Blockchain Enabled Waste Tracking System is the most appropriate alternative due to its high potential for Transparency, Regulatory Compliance and Fraud Prevention. In addition, this research will provide Policymakers, Urban Planners and Investors with a methodical way of making Data Driven Decisions on BBWM Investments.

User experience (UX) with Human-Computer Interaction (HCI) determine the system’s performance based on latency, response, and interaction rate. To improve the UX, feedback optimizations are irresistible for modern HCI systems. In this consideration, this article introduces a Performance-centric Optimization Model using a Migrant Trainer Algorithm for HCI. The user feedback for the aforementioned factors is accounted for by their different variants throughout the interaction time. The steep variation in individual and cumulative factors is used to provide optimization recommendations for consecutive interaction performance improvements. The migration agents are trained by random and linear pairing and isolation based on the maximum steep (drop) observed for the performance feature. This is recurrent for continuous interaction intervals such that the optimization is provided as a tuning parameter for improving UX. The migrant training process identifies the least and best-fit performance factor using the associativity rule. This associativity factor is valid to maximize the HCI performance for different user feedbacks observed. Therefore the improvements are included to maximize the UX through performance-leveraged HCI systems. The data-centric experimental results demonstrate that the proposed POM-MTA improves UX assessment by 9.912 ± 0.41%, enhances associativity by 10.88 ± 0.36%, reduces the interaction drop rate by 10.23 ± 0.28%, and decreases performance degradation by 8.48 ± 0.33% under maximum interaction-duration conditions.