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In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, TinyOS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the quality of service. But due to the high cost of the network, WSN components have limited resources to manage the network. There is a need to design a lightweight solution that ensures the quality of service in WSN. In this given manner, this study provides the quality of services in a wireless sensor network with a security mechanism. An incorporated hybrid lightweight security model is designed in which random waypoint mobility (RWM) model and grey wolf optimization (GWO) is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35 (NS2.35) are used in this research to evaluate the results. The overall cost factor is reduced at 60% without the optimization technique and 90.90% reduced by using the optimization technique, which is assessed by calculating the signal-to-noise ratio, overall energy nodes, and communication overhead. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, particularly those over 60. It is a significant cause of disability and can impact an individual’s quality of life. The condition occurs when the cartilage in the knee joint wears away over time, leading to bone-onbone contact, which can result in pain, stiffness, swelling, and decreased range of motion. Deep neural networks, especially convolutional neural networks (CNN), are powerful tools in medical applications such as diagnosis and detection. This research proposes a CNN model to classify knee osteoarthritis into five categories using x-ray images. These classes are labeled: Minimal, Healthy, Moderate, Doubtful, and Severe. Furthermore, the proposed CNN model has been compared with two pre-trained transfer learning models: Xception and InceptionResNet V2. These models were evaluated based on precision, recall, F1 score, and accuracy. The results showed that although all three models performed very well, the proposed model outperformed both transfer learning models with 98% accuracy. It also achieved the highest values for other parameters such as precision, recall, and F1 score. The proposed model has several potential applications in clinical practice, such as assisting doctors in accurately classifying knee osteoarthritis severity levels by analyzing single X-ray images. [ABSTRACT FROM AUTHOR]

Throughout the use of the small battery-operated sensor nodes encourage us to develop an energy-efficient routing protocol for wireless sensor networks (WSNs). The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN. Many routing protocols are available, but the issue is still alive. Clustering is one of the most important techniques in the existing routing protocols. In the clustering-based model, the important thing is the selection of the cluster heads. In this paper, we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster. Initially, the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance. The proposed scheme performs hierarchal routing and direct routing with some energy thresholds. The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance. Moreover, the simulations will be performed in two scenarios, gateway-based and without gateway to achieve more energy-efficient results. [ABSTRACT FROM AUTHOR]

• The abnormal conduction or disturbance in the cardiac activity is called arrhythmia, except for sinus rhythm. • Over the last decades, contemporary health-related device usage has increased the demand for efficient computational models for real-time analysis of cardiac arrhythmia. • As a result, there is a need to investigate the exact features of PVC arrhythmia, which assist in avoiding biased diagnosis. • This study opens the door for a new direction of research using our unique, fully automatic model for PVC arrhythmia classification (FAPAC). • Our proposed FAPAC model successfully achieved 99.97% of accuracy, 99.99 % sensitivity,99.99% specificity, and 99.98% positive predictivity. Cardiac arrhythmias are one of the leading causes of increased mortality worldwide and place a heavy burden on the medical environment. Premature ventricular contraction is the disturbance in electrical activity which is the most dangerous arrhythmia. Frequent occurrence of this type of arrhythmia in a regular heartbeat can lead to sudden cardiac death. Over the last decades, contemporary health-related device usage has increased the demand for efficient computational models for real-time analysis of cardiac arrhythmia. Despite notable experiments that have been done in the past decades, due to the intricate nature of PVC arrhythmia, success stories are still unsatisfying. There are numerous morphological and temporal variations present in ECG signals due to the inter-patient variability issue; extracting important characteristics of ECG signals is the most challenging task. As a result, there is a need to investigate the exact features of PVC arrhythmia, which assist in avoiding biased diagnosis. Precisely predicting it is a difficult task due to the negative polarity of PVC arrhythmia, the irregular mechanic of the ECG cycle, and anomalies between the normal cardiac rhythm. Furthermore, most of the studies in the literature followed the public benchmark dataset for the PVC arrhythmia classification, which is already pre-processed dataset. This study opens the door for a new direction of research using our unique, fully automatic model for PVC arrhythmia classification (FAPAC). This study designed an ECG monitoring module using the IoMT devices to obtain the real-time dataset for experiments and extract the relevant features from ECG signals. To classify the ECG beats, the fastest extended version of the recurrent neural network (RNN) model cyclic echo state networks to predict PVC arrhythmia. Our proposed FAPAC model successfully achieved 99.97% of accuracy, 99.99 % sensitivity,99.99% specificity, and 99.98% positive predictivity using the MIT-BIH-arrhythmia dataset, which is relatively higher than compared studies. [ABSTRACT FROM AUTHOR]

High-dimensional medical data makes prediction a complex and difficult task. This study aims at modeling predictive models for medical data. Two datasets of medical data are applied in the study — one online available dataset (Heart Disease data) and another real clinical dataset (Eye Infection Data). A wide range of machine learning algorithms are applied in the modeling stage: Decision Tree, Multilayer Perceptron, Naive Bayesian, Random Forest, and Support Vector Machine. Furthermore, bagging and voting ensemble methods have also been applied with base learners. Both split and cross-validation methods are adopted for the model validation, and well-established evaluation metrics such as accuracy, precision, recall, and F-measure have been considered as evaluation metrics for the predictive models. The method applied for the modeling is comprised of two stages. The first stage uses available features for the predictions. In the second stage, selected features based on positive correlation are used. The adopted method is also for deep learning, especially Convolutional Neural Network (CNN) is applied to analyze the outcomes compared to conventional machine learning algorithms. The experimental results reveal that better predictions are achieved in the second stage. Besides, experiments also indicate split percentage produces better predictive models, and marginally better outcomes are observed in the presence of ensemble methods in comparison with base models. NB outperformed other algorithms with the highest accuracy rate as 88.90%, and MLP obtained 97.50% accuracy for Heart Disease and Eye Infection data, respectively, using 80–20 splits in the second stage. However, the CNN model performed poorly due to the size of the considered datasets. • Two medical datasets are applied in the study: one online available and the other real. • Machine learning algorithms including DT, MLP, NB and RF are applied in the modeling. • The method applied for the modeling is comprised of two stages. • The experimental results reveal that better predictions are achieved in the second stage. • NB outperformed for an online dataset with 88.90% accuracy and MLP with 97.50% for real dataset. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature's local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay. [ABSTRACT FROM AUTHOR]

For better customer service and customer retention, businesses take proactive measures, including troubleshooting and solving potential challenges promptly. Blockchain technology integrates different recognition techniques of distributed pattern for monitoring database of a dedicated network and has proven a very promising technology. An automated pattern recognition decentralizes and distributes customized specific data. Machine learning, originated from artificial intelligence is primarily related to recognition of behavior patterns. Methods like knowledge discovery in database (KDD) and data mining focus on unsupervised approaches and are widely used in business and climatic weather applications. Blockchain addresses data-security concerns and builds trust by creating distributed ledger. Theft, willful fraud, software and hardware are considered by blockchain in data protection. Blockchain has greater significant feature as it makes the secure data access without enabling the central management entity. Two design features of blockchain technology help in this task. Recommended customer data pattern recognition technique using blockchain may eliminate all these problems. Two kinds of cryptographic algorithms employed in blockchain are asymmetric-key algorithms and hash functions. The current study analyzes the asymmetric cryptography approach along with key pair which supports in system security. Recurrent neural network (RNN) and support vector machine (SVM) classifier techniques consider both old customers and new customers as stable. The predictive model aids in identifying customers at churn risk in the telecommunication system. Existing proactive methods are unable to explain difficulties in customer interaction understanding and meeting their genuine needs. The proposed model organizes the customer situation and designs a customer proactive re-engagement over mobile-based telecommunication systems. Performance measures like churn prediction, classifier, confusion matrix, machine learning in the telecommunication system are used to evaluate and validate the results. • Designs a customer proactive re-engagement over weather climatic application. • To Utilize the data set by controlling and diminish the subscriber churn issue. • Ensure secure data access without enabling the central management entity based on block chain for weather climate. • Integrating the block chain and cognitive communication ensure the secure data access. • To optimize and predict the cost and power by validating the proposed model based on churn factors and cluster profiling in climatic weather. [ABSTRACT FROM AUTHOR]