The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO₂, SO₂, CO, CH₄, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution. Sensor data is processed using decision tree algorithms for classification, enabling threshold-based detection with environmental context. A Progressive Web Application (PWA) interface provides users with accessible, cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings, with real-time alerts triggered when thresholds were exceeded. The collected data showed consistent classification of normal, warning, and critical states for methane, CO₂, temperature, humidity, and noise levels. These results confirm the system’s reliability in dynamic environmental conditions.The proposed framework offers a scalable, energy-efficient, and user-friendly solution for pollution detection and public awareness. Future enhancements will focus on extending the sensor suite, improving machine learning accuracy, and integrating meteorological data for predictive pollution modeling.

The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats. The early detection of threats is both necessary and complex, yet these interconnected healthcare settings generate enormous amounts of heterogeneous data. Traditional Intrusion Detection Systems (IDS), which are generally centralized and machine learning-based, often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy. Moreover, traditional AI-driven IDS usually face challenges in handling large-scale, heterogeneous healthcare data while ensuring data privacy and operational efficiency. To address these issues, emerging technologies such as Big Data Analytics (BDA) and Federated Learning (FL) provide a hybrid framework for scalable, adaptive intrusion detection in IoT-driven healthcare systems. Big data techniques enable processing large-scale, high-dimensional healthcare data, and FL can be used to train a model in a decentralized manner without transferring raw data, thereby maintaining privacy between institutions. This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing, privacy, and compliance with ethical regulations. To strengthen the reliability of the reported findings, the results were validated using cross-dataset testing and 95% confidence intervals derived from bootstrap analysis, confirming consistent performance across heterogeneous healthcare data distributions. This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability, privacy, adaptability, and early-detection capabilities. The proposed global model achieves a test accuracy of 99.93% ± 0.03 (95% CI) and a miss-rate of only 0.07% ± 0.02, representing state-of-the-art performance in privacy-preserving intrusion detection. The proposed FL-driven IDS framework offers an efficient, privacy-preserving, and scalable solution for securing next-generation healthcare infrastructures by combining adaptability, early detection, and ethical data management.

Gastrointestinal polyps are well-known precursors to colorectal cancer (CRC), making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention. Deep learning–based segmentation models trained on publicly available datasets such as Kvasir-SEG have demonstrated promising performance; however, two key challenges remain: limited robustness across diverse polyp morphologies and endoscopic imaging conditions, and the lack of interpretable decision-making mechanisms that support clinical trust and validation. Many existing centralized segmentation approaches are primarily optimized using overlap-based metrics such as the Dice coefficient and intersection over union (IoU), without adequately analyzing challenging cases such as small, flat, or low-contrast polyps or providing insight into the visual cues influencing model predictions. This study presents an explainable centralized deep learning segmentation model for gastrointestinal polyp segmentation using the Kvasir-SEG dataset. The approach integrates a ResUNet++-Lite encoder–decoder segmentation model with Grad-CAM and masked Grad-CAM visualizations to analyze the spatial regions influencing segmentation predictions. The study focuses on establishing a reproducible and interpretable experimental model that combines systematic preprocessing, data augmentation, centralized training, and explainability analysis. Experimental evaluation on an 80:20 train–test split of the Kvasir-SEG dataset, where data augmentation was applied after splitting, demonstrates stable training behavior and competitive segmentation performance, achieving a pixel accuracy of 0.964, a Dice coefficient of 0.858, and an IoU of 0.791 on the held-out test set. Qualitative explainability results further indicate that the model consistently focuses on anatomically relevant polyp regions. Overall, the study illustrates how segmentation performance and explainable AI techniques can be integrated to support the development of clinically interpretable AI-assisted colonoscopy systems.

Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, recall, and F1-score each exceeding 98% on an independent test set. Crucially, LRP-derived heatmaps consistently localize anatomically and pathologically significant regions such as tumor margins in agreement with established clinical criteria. The proposed framework enhances clinician trust by delivering pixel-level justifications alongside state-of-the-art predictive performance. It facilitates informed decision-making, thereby addressing a key barrier to the clinical adoption of DL in oncology.

The rapid evolution of smart cities through IoT, cloud computing, and connected infrastructures has significantly enhanced sectors such as transportation, healthcare, energy, and public safety, but also increased exposure to sophisticated cyber threats. The diversity of devices, high data volumes, and real-time operational demands complicate security, requiring not just robust intrusion detection but also effective feature selection for relevance and scalability. Traditional Machine Learning (ML) based Intrusion Detection System (IDS) improves detection but often lacks interpretability, limiting stakeholder trust and timely responses. Moreover, centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures. To address these limitations, this research introduces an Interpretable Federated Learning (FL) based Cyber Intrusion Detection model tailored for smart city applications. The proposed system leverages privacy-preserving feature selection, where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability. These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data. Furthermore, the global model is enhanced with Explainable AI (XAI) techniques such as SHAP and LIME, offering both global interpretability and instance-level transparency for cyber threat decisions. Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%, with a significantly low miss rate of 1.49%, outperforming existing models while ensuring explainability, privacy, and scalability across smart city infrastructures.

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of Medical Things (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardous attacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74% misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stages with the help of patient clinical records, and all medical professionals will get beneficial diagnosing approaches from this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due to cervical cancer.