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This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the “glacier” and “mountain” categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and the sharing of the data is restricted under global privacy laws. Furthermore, the sharing and collection of biomedical data have a significant network communication cost and lead to delayed heart disease prediction. To address the training latency, communication cost, and single point of failure, we propose a hybrid framework at the client end of HSP consisting of modified artificial bee colony optimization with support vector machine (MABC-SVM) for optimal feature selection and classification of heart disease. For the HSP server, we proposed federated matched averaging to overcome privacy issues in this paper. We tested and evaluated our proposed technique and compared it with the standard federated learning techniques on the combined cardiovascular disease dataset. Our experimental results show that the proposed hybrid technique improves the prediction accuracy by 1.5%, achieves 1.6% lesser classification error, and utilizes 17.7% lesser rounds to reach the maximum accuracy.

This study delves into the vulnerability of the smart grid to infiltration by hackers and proposes methods to safeguard it by leveraging blockchain and artificial intelligence (AI). A categorization and analysis of cyberattacks against smart grids will be conducted, focusing on those targeting their communication layers. The main goal of the work is to address the challenges in this area by implementing novel detection and defense strategies. The authors categorize attacks on smart grid networks based on the communication classes they want to compromise. They propose novel taxonomies specifically designed to detect and implement defense strategies. The study investigates artificial intelligence and blockchain techniques to identify cyber-attacks that employ deceptive data injection. The study indicates that cyberattacks against smart grids are increasing in frequency and complexity. The paper proposes innovative strategies for defense, such as enhancing cybersecurity with artificial intelligence and blockchain technology. The research further enumerates several challenges, such as counterfeit topological data, imprecise data identification, and combining big data with blockchain technology. Given the increasing risks, the study emphasizes the crucial need for robust cybersecurity safeguards in smart grids. This work contributes to the protection of smart grid infrastructures by categorizing attacks, suggesting novel defenses, and exploring solutions integrating artificial intelligence and blockchain technology. Research should prioritize enhancing technology to maximize security and counter emerging attack methods. The intended audience of our paper comprises graduate-level academics and independent researchers.

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types—pituitary, meningioma, glioma, and normal—this study investigates a potent artificial intelligence (AI) technique. Even though AI has been utilized in the past to detect brain tumors, current techniques still have issues with accuracy and dependability. Our study presents a novel AI technique that combines two distinct deep learning models to enhance this. When combined, these models improve accuracy and yield more trustworthy outcomes than when used separately. Key performance metrics including accuracy, precision, and dependability are used to assess the system once it has been trained using MRI scan pictures. Our results show that this combined AI approach works better than individual models, particularly in identifying different types of brain tumors. Specifically, the InceptionV3 + Xception combination hit an accuracy level of 98.50% in training and 98.30% in validation. Such results further argue the potential application for advanced AI techniques in medical imaging while speaking even more strongly to the fact that multiple AI models used concurrently are able to enhance brain tumor detection.

Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.

A high growth rate in network traffic and the complexity of cyber threats have made it necessary to create more effective and flexible intrusion detection systems. Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting new patterns of attacks due to the use of obsolete data or traditional machine learning models. To overcome the mentioned constraints, the current research presents a new deep learning solution that combines Sequential Deep Neural Networks (DNN) and Rectified Linear Unit (ReLU) activation unit with an Extra Tree Classifier feature selection procedure. The proposed model is trained and tested on the new rich and up-to-date UNSW-NB15 set, which provides a realistic reflection of the real-life network traffic and attack vectors. The interesting novelty of this study is the tactical use of ReLU-based DNN combined with feature optimization through the Extra Tree Classifier, which not only overcomes general problems like vanishing gradients and overfitting but also greatly increases the interpretability of the model and the efficiency of its computation. This dimensional reduction of the feature space (43 to only 8 highly relevant features) retains the high accuracy of the model but with better inference speed, which is a crucial aspect of the real-time deployment of NIDS. The results show that with the Sequential DNN approach, the binary class (0 for normal and 1 for attack records) achieved 97.93% accuracy, 97% Precision, 97% Recall and 97% F1-score. Furthermore, the detailed experimental testing, such as ROC curves and Confusion Matrices, confirmed that the Sequential DNN performed well in comparison to other Existing Studies. These findings underscore the effectiveness of deep learning architectures enhanced with optimized feature selection in detecting network intrusions, making the proposed system a promising solution for securing critical infrastructure in sectors such as finance, healthcare, and government networks.

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers.