Deep learning models, particularly Convolutional Neural Networks (CNNs), have advanced automated Sleep Apnea (SA) detection from single-lead Electrocardiogram (ECG) signals. However, current approaches often face two limitations: (1) reliance on single-view representations (e.g., time-series or one image type) that can fail to capture the diverse feature set inherent in complex ECG dynamics; and (2) image-based methods that frequently employ standard architectures which may not optimally extract intricate local patterns crucial for SA identification. To address these challenges, we propose MVIC-Net, a Multi-View Interactive Convolutional Network framework that uniquely processes four complementary ECG representations simultaneously: 1D numeric data, 2D reshaped numeric data, Continuous Wavelet Transform (CWT) images, and circular plot images. We introduce the Convolutional and Interactive Learning Neural Network (CINet), an architecture inspired by SCINet and specifically engineered for enhanced ECG image feature extraction. CINet utilizes a hierarchical downsampling and interactive learning mechanism: it recursively splits feature maps, processes subsequences through distinct convolutional filters, and interactively combines them to mitigate information loss while capturing multi-resolution features effectively. Our MVIC-Net model integrates pre-trained CINet and standard CNN encoders, fusing their outputs via concatenation before final classification. Empirical validation on the Apnea-ECG database demonstrates superior performance, achieving 99.25% accuracy. This significantly surpasses individual-view models (with CINet reaching 96.32% accuracy on image-based views), pre-trained ResNet152V2 baselines, and a multi-model ensemble approach. To enhance clinical trust, we incorporate Explainable AI (XAI) via Grad-CAM, providing transparency into CINet’s decision-making process. Our results establish the effectiveness of combining multi-view learning with interactive convolutional architectures for robust physiological signal classification.

Diagnosis of cancer is one of the hardest problems faced in modern medicine and involves integrating different data sources such as medical images, genomic profiles and clinical records. Traditional machine learning methods have difficulty handling the high-dimensional and complex correlation properties of multimodal medical data. In view of this, we propose a new Quantum-Enhanced Multimodal Fusion Network (QEMFN) framework to break through traditional image-text matching based on quantum computing principles for CT imaging with genomic sequencing data and EHR information. Our approach utilizes variational quantum circuits for feature encoding, quantum kernel methods for crossmodal attention, and hybrid quantum-classical architectures for final classification. We realize the framework using Google Cirq quantum computing library and validate it on publicly available datasets including TCIA (The Cancer Imaging Archive), TCGA (The Cancer Genome Atlas), and MIMIC-III clinical database. The matched multimodal cohort comprises 847 lung cancer patients, 623 colorectal cancer patients, and 401 liver cancer patients with complete imaging, genomic, and clinical records, assembled via de-identified patient ID linkage across the three archives. The experiment takes steps toward the realization of quantum-enhanced diagnostic systems and offers a path for subsequent experimental confirmation. We theoretically analyze the potential quantum advantage, present detailed implementation details using Cirq, and describe a roadmap to clinical translation for quantum-enhanced diagnostic tools.

Viruses are a significant threat to human life, as demonstrated by the global COVID-19 pandemic and the Ebola outbreak. Diseases such as smallpox, AIDS, hepatitis, liver cancer, and cervical cancer often caused by the Human Papillomavirus can lead to fatal outcomes. Over the years, extensive research has focused on developing vaccines and antiviral drugs, which have successfully contained and, in some cases, eradicated viral infections. Recently, computational techniques, particularly machine learning algorithms, have made notable progress in identifying potential Antiviral Peptides (AVPs), thereby accelerating experimental validation for therapeutic applications. This study introduces machine learning techniques and a Light Gradient Boosting Machine (LGBM) model combined with three feature-encoding methods to predict whether a protein sequence contains effective antiviral peptides. Eight machine learning algorithms were evaluated using both single-feature and combined feature encodings. Among them, the lightweight LGBM model trained on combined encoded features achieved the best performance, with an accuracy of 98%, precision of 97%, recall of 98%, F1-score of 98%, and an AUC of 1.00. Compared to existing models, the proposed approach achieved approximately 2% higher accuracy using individual encoding methods and about 3% higher accuracy with combined features. The reliability and effectiveness of the proposed model highlight its potential value for pharmaceutical development and academic research.

Anemia is a condition which is characterized by insufficient red blood cells or hemoglobin. It creates a substantial health concern specially for children and women. Thus, the early and accurate detection of anemia is crucial for starting the treatment process and getting optimal health outcomes. The existing anemia detection technique is expensive and can create human error. To address these obstructions, this study proposes a novel anemia detection model based on Explainable Boosting Machines (EBM). The proposed EBM model, combined with effective data preprocessing and class balancing techniques, demonstrates exceptional performance in anemia detection. The simulation results on an anemia dataset show that the model can get an impressive accuracy of 98.69

Federated Learning (FL) and Transfer Learning (TL) are essential for ensuring data privacy and enhancing Machine Learning (ML) models. Small datasets often hinder effective model training, highlighting the importance of data sharing. However, security concerns and privacy constraints lead to the emergence of data island, where feature differences can negatively impact ML performance. Federated Transfer Learning (FTL) addresses these challenges by allowing devices to collaborate on model improvement without sharing raw data, thus preserving privacy and scalability in distributed ML scenarios. TL reduces the computational costs associated with developing models for new applications by transferring knowledge from pre-trained networks, enabling strong performance even with limited data. FTL is particularly valuable for transferring knowledge between domains with minimal feature overlap, saving time and resources. However, its broader adoption faces challenges, including ineffective knowledge transfer, scalability issues, privacy concerns, and the absence of a standardized framework. This work reviews 25 FTL models across various sectors, including machine fault diagnosis, healthcare, wind and photovoltaic power forecasting, disaster prediction, finance, and image steganalysis. These models were sourced from open-access repositories like Google Scholar, IEEE Xplore, and ScienceDirect. Key challenges such as data heterogeneity, label scarcity, communication overhead, and scalability remain open areas for further FTL research. This review aims to provide a unified roadmap to inspire future researchers to explore FTL methodologies for addressing sparse overlapping features in diverse data distributions.

This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.

Accidental falls have emerged as a major public health concern, especially among individuals aged 65 and older, due to their high incidence and severe consequences. Without timely intervention, such falls can result in fractures, traumatic brain injuries, and long-term complications. As a result, considerable research has focused on developing automated fall detection systems that integrate intelligent algorithms with sensor-based data acquisition to enable rapid response and medical assistance. This study follows the PRISMA framework to conduct a systematic literature review. A comprehensive search was performed across major databases including PubMed, Google Scholar, Scopus, and IEEE Xplore using fall detection–related keywords. From an initial pool of 596 articles, duplicates were removed and strict inclusion/exclusion criteria were applied, resulting in 182 relevant articles for in-depth analysis. This review examines a wide range of Artificial Intelligence (AI) and Machine Learning (ML) approaches applied to fall detection using diverse sensor modalities, including wearable, vision-based, ambient, and multimodal systems. Additionally, it summarizes the publicly available datasets and sensor configurations used in existing studies. This broad perspective is essential to compare trade-offs across sensing modalities and support effective system development for real-world deployment. This review provides a comparative overview of AI/ML-based fall detection approaches, highlighting differences in accuracy, sensitivity, and dataset usage as reported in existing literature. In the end, many open research challenges in using AI and ML to detect falls are outlined, along with potential future perspectives.

ABSTRACT Healthcare environments present uniquely demanding constraints for the deployment of artificial intelligence (AI). Clinical decisions of significant consequence are rarely the product of a single isolated computation; rather, safe and effective patient care relies on the continuous collaboration of distributed entities, including clinicians, hospital networks, laboratory systems, monitoring devices, and patients themselves. As generative models and large language models are increasingly deployed in clinical settings, the transition from monolithic AI models to Multi‐Agent Systems (MAS) offers unprecedented capabilities for distributed monitoring, coordinated decision support, and service integration. However, multi‐agent collaboration simultaneously introduces severe novel vulnerabilities, including topological contamination, privacy leakage, cascading systemic failures, and the socio‐psychological risks of undue algorithmic persuasion. This work introduces a comprehensive, privacy‐preserving, explainable, and human‐centred MAS framework explicitly designed for collaborative clinical decision support. Rejecting the paradigm of a single, omniscient AI model with unrestricted data access, the proposed architecture operationalises clinical AI as a heavily regulated team of specialised, interacting agents. The framework defines six distinct agent classes: Data Stewardship, Modality, Explanation, Consensus, Safety Monitor, and Clinician Interface agents. Furthermore, the implementation is governed by six foundational design principles grounded in classic Autonomous Agents and MAS theory, adapted for modern generative systems. By enforcing minimum‐necessary data exchange, verifiable team‐level explainability, explicit hierarchical escalation, secure communication topologies, and human‐centred safety protocols, this framework provides both a domain‐grounded blueprint and a transferable set of robust design principles for deploying trustworthy MAS in high‐stakes environments.

Alzheimer’s Disease (AD) is a progressive neurological disease that severely impairs cognitive function. Early detection is critical for effective treatment and management. Machine Learning (ML) methods are often used to ensure early detection and prediction. However, ML has various issues, including the data island problem. The fragmentation that results from the data island problem makes building reliable, effective ML models more complex, and it is particularly problematic in industries where privacy is a concern, like healthcare. Federated Learning (FL) can help tackle the data island problem by keeping sensitive patient data decentralized and enabling many institutions to work together on model training without exchanging raw data, all while maintaining privacy compliance. As Random Forest (RF) is proven to be the best-performing classifier in this research, an RF classifier is used to create FL. The model incorporates multiple data modalities, such as Magnetic Resonance Imaging (MRI) segmentation and clinical and psychological data, to capture the variety of characteristics influencing the progress of AD. Another concerning issue with ML is its uninterpretable character. We use SHapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) techniques that emphasize important factors impacting model decisions in order to improve predictability and transparency. This explainability promotes confidence in AI-based diagnoses by enabling researchers and physicians to comprehend the underlying mechanisms guiding the predictions. The combination of XAI, FL, and Open Access Series of Imaging Studies (OASIS-3) Multimodal data offers an interpretable, scalable, reliable, and privacy-centered solution for multiple complex issues, such as predicting AD. This approach results in better diagnosis precision, greater security, and increased confidence in AI technologies, making it a novel methodology in medical sciences. With data privacy maintained, our method produces 98.93% accurate predictions, providing a solid detection strategy for AD. The suggested approach’s F1-score, Precision, Recall, and AUC are 98.93%, 98.94%, 98.93%, and 99.97%, respectively. This work also shows that a multimodal dataset performs better than a single modal dataset.

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° (σ=0.8°, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, σ=12 ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics.

The rapid advances in Artificial Intelligence (AI), microservices architecture, and cloud computing highly impact airlines in optimizing reservation systems. This review paper attempts to analyze the ongoing trend, challenges, and future opportunities of AI-enabled microservices toward cloud-based airline reservation systems. AI with machine learning and deep learning supports dynamic pricing, personalized services, and better demand forecasting, transforming booking, pricing, and customer experience. While microservices architecture offers scalability and flexibility, in conjunction with cloud computing, it optimizes airline demands and system performance. At the same time, challenges such as data privacy and security, scalability, interoperability, and costs are still present with adopting cloud-based microservices. Case studies are provided in this paper, as well as a thorough exposition of future directions of AI in airline reservation systems, such as the application of emerging AI techniques like reinforcement learning, evolution toward micro service architecture, and the influence of new cloud computing models such as edge and hybrid clouds. In addition, the work points out research gaps in areas such as real-time predictive analytics and security measures. Findings indicate a transforming potential of service on airline reservation systems for AI-facilitated micro services and cloud computing and provide possible suggestions for overcoming the current challenges to harness great efficiency from them.

The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.

The wearable inertial sensors enhanced by real-time monitoring employed to monitor and improve human activities relating to health, safety, and well-being. This paper provides a framework for detecting anomalies in activity data, based on records and collected values from accelerometers and gyroscopes with the help of machine learning. By jointly considering advanced signal processing, feature extraction, and Random Forest classification technique optimized with SMO, it solves acute noise, computational costs, and contextual ambiguity issues. The extracted features, like mean and max values, are capable of capturing static and dynamic patterns quite efficiently. The proposed system has shown good improvements in the detection of anomalies in critical events, such as falls and abnormal target behaviour. The framework increases the prospects of safety and improvement in living standards and could evolve into the mainstream market with reliable monitoring systems.

The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the system. The AI algorithms forecast demand patterns, optimize the allocation of resources, and enhance decision-making driven by Microservices architecture, hence decentralizing system components for scalability, fault tolerance, and reduced downtime. The model provided focuses on major problems associated with the travel reservation systems such as latency of systems, load balancing and data consistency. It endows the systems with predictive models based on AI improved ability to forecast user demands. Microservices would also take care of different scales during uneven traffic patterns. Hence, both aspects ensure better handling of peak loads and spikes while minimizing delays and ensuring high service quality. A comparison was made between traditional reservation models, which are monolithic and the new model of AI-Microservices. Comparatively, the analysis results state that there is a drastic improvement in processing times where the system uptime and resource utilization proved the capability of AI and the microservices in transforming the travel industry in terms of reservation. This research work focused on AI and Microservices towards real-time optimization, providing critical insight into how to move forward with practical recommendations for upgrading travel reservation systems with this technology.

Facebook and Twitter are the most popular social media sites where cyberbullying is common. It is defined as the exploitation of digital communication to harass, intimidate, or degrade people, and this may cause serious psychological and social harm. Cyberbullying can occur at any time and is more difficult to avoid as opposed to traditional bullying. In this paper, we aim to identify cyberbullying in Bengali social media comments, a lowresource language, and to detect abusive content in depth. To address the problem, we trained and evaluated five models: Bangla-BERT, m-BERT, LSTM, Bi-LSTM, and SVM with a labelled dataset of Bengali social media comments. The models were trained to label the comments as abusive and non-abusive. The models were trained to classify comments as abusive or non-abusive. Experimental results show that Bangla-BERT achieved the highest accuracy (94 %), followed by m-BERT (91 %), Bi-LSTM (89 %), LSTM (87 %), and SVM (76 %). Although the Bangla-BERT is better than the other models, it can still be improved with bigger and more varied datasets. The study illustrates the usefulness of machine learning in cyberbullying detection in Bengali and also leads to safer online communication, although it also indicates future directions of low-resource language processing.

Handling online travel agents globally requires efficient and flexible software solution architectures. When it needs to handle thousands of agents and billions of clients data globally. Microservices architecture is used to break down a large program into numerous, smaller services which can run individually and perform individual tasks. This paper analyses and integrates a unique Microservices Cloud Framework designed to support Online Travel Platforms (MCF-OTP). MCF-OTPs main goal is to increase the performance, flexibility, and maintenance of online travel platforms via cloud computing and microservice technologies. Large-scale travel apps, including managing numerous data sources, dealing with traffic peaks, and providing fault tolerance, can be addressed by the suggested framework. The framework increases good interpretation between flawless data synchronization, microservices, and dynamic scaling based on demand technology. An organization framework that optimizes service borders and minimizes inter-service dependencies is recommended. Thus, this can result in elevated development adaptability. In this research, the principal goal is to evaluate MCF-OTPs efficiency using the indicators of fault tolerance and response time. It is indicated by the findings that the MCF-OTP structure excels traditional monolithic designs in terms of dependability and scalability, managing traffic spikes seamlessly and decreasing downtime. The cost-effective analysis helps ascertain the net gain attained by the startup fees and the ongoing operational costs. The cloud-based environment is used to reduce the fracture cost which also helps to increase the efficiency of resource allocation, according to the research.

Autism spectrum disorder (ASD) is a developmental disorder typically diagnosed in early childhood. With the advent of machine learning (ML) and deep learning (DL) models, accurate diagnosis of ASD has been enhanced. However, the widespread adoption of these AI models in real-life scenarios has been limited due to their “black box” nature, which lacks transparency and interpretability. To address this, eXplainable Artificial Intelligence (XAI) models have gained popularity, offering more transparent and interpretable detection methods. This review systematically explores XAI frameworks and underlying AI models by addressing four critical research questions (RQs). Relevant research outputs were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach from five major databases: IEEE, PubMed, Springer, ScienceDirect and ACM. From an initial pool of 1551 articles, 38 studies were identified that focused on learning models and XAI in ASD prediction. These studies were critically analysed across six modalities, twenty classifiers, and five XAI frameworks. The selected studies demonstrate the application of various XAI frameworks in enhancing the transparency and interpretability of AI models used for ASD prediction. The review highlights the benefits of XAI in improving model trustworthiness and adoption, while identifying challenges, such as the trade-off between interpretability and model performance. This review provides a comprehensive overview of the current state of the art of XAI in ASD prediction, identifying key benefits, challenges, and future research avenues. The insights gained from this review could guide researchers in further developing XAI frameworks that balance interpretability and predictive accuracy, thereby facilitating broader adoption in clinical practice.