As Smart City (SC) infrastructures evolve rapidly, securing critical systems like smart water management (SWM) becomes paramount to protecting against cyber threats. Enhancing the security, sustainability and execution of conventional schemes is considered significant in developing smart environments. Intrusion detection systems (IDS) can be effectively leveraged to realise this security objective in an Internet of Things (IoT)‐based smart environment. This research addresses this need by proposing a novel IDS model called TabNet architecture optimised using Sailfish Optimisation (SFO). The TabNet‐SFO model was specifically developed for SWM in SC applications. The proposed IDS model includes data collection, preprocessing, feature selection and classification processes. For training the model, this research used the CIC‐DDoS‐2019 dataset, and for evaluation, real‐time data collected using an IoT‐based smart water metre are used. The preprocessing step eliminates unnecessary features, cleans the data, encodes labels and normalises the applied datasets. After preprocessing, the TabNet model selects significant features in the dataset. The TabNet architecture was optimised using the SFO algorithm, which allows hyperparameter tuning and model optimisation. The proposed model demonstrated improved detection accuracy and efficiency on both the simulated and real‐time datasets. The model attained a 98.90% accuracy, a 98.85% recall, a 98.80% precision, a 98.82% specificity and a 98.78% f1 score on the CIC‐DDoS dataset and a 99.21% accuracy, a 99.02% recall, a 99.05% precision, a 99.10% specificity and a 99.18% f1 score on real‐time data. Compared to existing models, the TabNet‐SFO model outperformed all existing models in terms of performance metrics and validated its efficiency in detecting attacks.

The growing prevalence of cardiovascular diseases (CVDs) underscores the critical need for accurate, real-time monitoring and personalized diagnostics in cardiac healthcare. This paper presents DeepCardioNet, an innovative deep learning framework integrated with Internet of Things (IoT) devices, designed to address the limitations of existing cardiac health monitoring systems. The proposed framework leverages multimodal physiological data, including electrocardiogram (ECG), heart rate variability (HRV), and blood pressure (BP), to provide comprehensive and precise diagnostics. DeepCardioNet incorporates a multi-stream convolutional neural network and transformer architecture, enhanced with an attention mechanism for dynamic feature prioritization. In addition, we optimize the model through techniques such as pruning and quantization to support the possibility of real-time edge AI deployment, ensuring efficient inference on resource-constrained IoT devices. Experimental results on the MIT-BIH arrhythmia database and MIMIC-III waveform database demonstrate the superiority of DeepCardioNet over state-of-the-art methods, achieving an accuracy of 93.6% and an F1-score of 91.8%. Extensive ablation studies validate the contributions of key architectural components, including multimodal data fusion and attention mechanisms. The findings highlight the framework’s robustness and suitability for real-world cardiac health monitoring applications. By integrating advanced AI techniques with IoT capabilities, DeepCardioNet represents a significant step toward achieving personalized and precise healthcare in the context of the healthcare industry.

Healthcare supply chains manage increasing volumes of high-resolution computed tomography (CT) scans daily. The scan of medical images uses storage infrastructure increases transmission costs and elevates carbon emissions. Traditional compression methods achieve limited ratios for medical images, while current deep-learning approaches require intensive computational resources, making them unsuitable for sustainable healthcare operations. This study introduces ESGC-Net, an Artificial Intelligence of Medical Things (AIoMT) driven framework that optimizes secure medical image distribution throughout the healthcare supply chain while reducing environmental impact. Our approach integrates deep spatiotemporal learning with subset-based coding to create an environmentally sustainable compression pipeline. The framework processes CT image sequences using regional healthcare edge devices, enabling efficient data flow from imaging centers to cloud storage and clinical access points. The AIoMT integration allows for distributed processing at multiple points in the supply chain, reducing data transfer volumes while maintaining data security through adaptive encryption that protects patient privacy across all supply chain nodes. Experimental results demonstrate that ESGC-Net attains a 4.85 compression ratio while preserving diagnostic image quality, reducing processing time by 41.0% and energy consumption by 31.5% compared to existing methods. The subset-optimal encoding technique decreases encoding time by 47.8%, enabling faster image transmission between healthcare facilities.

Smart medical rescue vehicles (SMRVs) are crucial in providing timely and effective emergency medical services in urban environments. However, the efficiency and safety of SMRVs are often hindered by dynamic and complex traffic conditions, leading to longer response times and increased risk of accidents. To address these challenges, this article proposes a novel lane-changing strategy called AIoT-LC, which leverages the Augmented Intelligence of Things (AIoT) framework to enable efficient vehicle road cooperation for smart medical rescue. First, a deep Q-network is employed to process real-time data streaming from SMRVs and support their collaborative management with roads and pedestrians. Then, a cooperative lane-changing strategy is developed to ensure the safe and efficient navigation of SMRVs through dynamic traffic environments, considering factors, such as safety distance, lane-changing trajectory planning, and multivehicle coordination. Finally, a collision resolution and avoidance strategy is proposed for SMRVs at intersections, leveraging the vehicle-infrastructure cooperative environment and the AIoT framework to minimize the risk of collisions and optimize the intersection crossing process. The experimental results demonstrate that the proposed AIoT-LC method significantly outperforms existing approaches, reducing average travel time for SMRVs by up to 25%, improving average speed by 15%, and increasing the success rate of emergency responses by 20% points compared to the best performing baseline method. Additionally, the proposed method reduces fuel consumption by 11.7% and CO2 emissions by 11.7% while improving overall traffic flow efficiency by 8.9%.

In the realm of medical data processing, particularly in the diagnosis and monitoring of cardiac diseases, the analysis of electrocardiogram (ECG) signals represents a critical challenge, especially with the burgeoning volume of ECG Big Data. Traditional methods and existing research often fall short in effectively analyzing this data, limited by their inability to fully capture the complex and nonlinear patterns inherent in ECG signals. Addressing these limitations, in this article, we introduce a novel deep neuro-fuzzy model augmented with multimodal feature fusion. Our method ingeniously combines the power of neuro-fuzzy systems with the robust feature extraction capabilities of deep learning, specifically leveraging a Transformer-based architecture, to analyze both ECG signals and their corresponding spectral images. This multimodal fusion not only enriches the model's input data, providing a comprehensive understanding of cardiac signals, but also enhances the adaptability and accuracy of cardiac arrhythmia detection. We rigorously validate our approach on the MIT-BIH arrhythmia database, conducting a series of experiments, including performance evaluations and ablation studies, to highlight the significant contributions of the multimodal feature fusion and neuro-fuzzy module. The results achieve significant improvements in classification metrics: an accuracy of 98.46% and an F1 score of 99.1%. Moreover, we benchmark the Transformer's feature extraction performance against other architectures, such as ResNet. The results unequivocally demonstrate our model's superiority and illustrate the potential of integrated neuro-fuzzy and deep learning approaches in overcoming the current limitations of ECG signal analysis.

Driver fatigue detection is a critical challenge in Transportation Cyber-Physical Systems (T-CPS), where existing methods often face significant limitations. Conventional approaches typically struggle with issues such as limited accuracy, slow convergence, and high computational costs. These methods often fail to capture the complex temporal and spatial patterns inherent in multimodal physiological signals, such as EEG and EOG, leading to suboptimal performance in real-world scenarios. To address these challenges, we propose a novel approach that leverages Depthwise Separable Convolutional Neural Networks (DSCNNs) for driver fatigue detection. Our method integrates electroencephalogram (EEG) and electrooculogram (EOG) signals through an innovative feature extraction and fusion process, enhancing the system’s ability to detect fatigue with high accuracy and efficiency. The DSCNN model is designed to overcome the limitations of traditional methods by utilizing depthwise separable convolutions, which reduce computational complexity while maintaining robust performance. We validate our approach using the SEED-VIG dataset and the NTHU drowsy driver dataset, which encompasses a range of driving conditions. The DSCNN model outperforms conventional models in both accuracy and computational efficiency. Specifically, DSCNN achieves the highest F1 score and the lowest time per epoch, making it highly suitable for real-time applications in T-CPS. This advancement represents a significant improvement in driver safety by providing more timely and reliable fatigue warnings, thus advancing the capabilities of intelligent transportation systems.

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions; 2) cryptographic hash feature; and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process.

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer‐aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE‐Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1‐score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1‐score for mammogram images.

Summary Medical imaging systems have broadly used in the diagnosis and identification of breast cancer. It is essential to recognize breast tumor as soon as possible. Mammography is a widely utilized method for the identification of breast cancer. The identification of cancer is trailed by the segmentation of the cancer area in an image of the mammogram. Numerous researches have been made on the diagnosing and identification of breast cancer utilizing different classification and image processing methods. In this work, we proposed the Convolutional Neural Network (CNN) classifier for diagnosing breast cancer utilizing MIAS (Mammographic Image Analysis Society)‐dataset. CNN established as an efficient class of methods for image recognition problems. CNN is a deep learning system that extricates the feature of an image and utilizes those features for classification of the image. Because deep learning methods are utilized for high task objective Computer Vision, Medical Diagnosis, Image processing, and so on. Wiener filter is utilized to expel the noise and background of the image and the K‐means clustering technique was utilized for the segmentation. After segmentation, the features are extracted and classified utilizing CNN classifier. The performance of this proposed method was analyzed and compared with the conventional techniques based on accuracy, sensitivity, and specificity result parameters. From the comparison result, it is seen that the CNN classifier performed better compared with different techniques with 0.5‐4% additional accuracy and 3‐13% specificity.

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter‐ and intra‐expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.