There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to implement intrusion detection systems (IDS) that work. In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. This methodology improves performance and cuts training time by 27-63% for a variety of classifiers. By utilizing the most discriminative features, the suggested methods lower the computational overhead and improve the detection accuracy. The IDS achieved over 99.9% accuracy, precision, recall, and F1-Score on the dataset IoTID20, with consistent performance on the NSLKDD dataset.

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library.

Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision. Masking autoencoding is a promising self-supervised learning approach that greatly advances computer vision and natural language processing. For robust 2D representations, pre-training with large image data has become standard practice. On the other hand, the low availability of 3D datasets significantly impedes learning high-quality 3D features because of the high data processing cost. We present a strong multi-scale MAE prior training architecture that uses a trained ViT and a 3D representation model from 2D images to let 3D point clouds learn on their own. We employ the adept 2D information to direct a 3D masking-based autoencoder, which uses an encoder-decoder architecture to rebuild the masked point tokens through self-supervised pre-training. To acquire the input point cloud’s multi-view visual characteristics, we first use pre-trained 2D models. Next, we present a two-dimensional masking method that preserves the visibility of semantically significant point tokens. Numerous tests demonstrate how effectively our method works with pre-trained models and how well it generalizes to a range of downstream tasks. In particular, our pre-trained model achieved 93.63% accuracy for linear SVM on ScanObjectNN and 91.31% accuracy on ModelNet40. Our approach demonstrates how a straightforward architecture solely based on conventional transformers may outperform specialized transformer models from supervised learning.

Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.

Customer turnover is a crucial issue in banking since maintained profitability depends on keeping clients. This work aims to categorize consumer turnover in banks by using a new ensemble approach combining many machine learning methods, hence enhancing churn prediction models. Using a comprehensive dataset including demographic, financial, and behavioral data—such as credit score, account balance, tenure, and activity levels—the study employs the goal variable revealing if a customer has left the bank. The study starts with univariate, bivariate, and multivariate feature exploration and subsequently uses the Interquartile Range (IQR) approach to identify outliers thereby improving the data quality. Five models—K-Nearest Neighbors, Support Vector Classifier, Random Forest, Decision Tree, and XGBoost—a Voting Classifier ensemble—are used to estimate project churn. Building upon all the strengths of each model, this approach improves the prediction of classification and provides a balanced and highly robust classification system. The applied approaches are K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Decision Tree, and XGBoost within a Voting Classifier configuration. The performance of the Voting Classifier without SMOTE yields the following results: Accuracy: 0.87, precision: 0.87, recall: 0.80, and F1-Score: 0.87. The proposed model that extend the base model using SMOTE (Synthetic Minority Over-sampling Technique), yields a higher prediction accuracy of 0.90, precision of 0.90, recall of 0.90 and F1-Score of 0.90. This enhancement is proving the efficiency of SMOTE to handle the class imbalance problem in order to render the churn prediction more balanced and reliable system. The proposed approach assures a reliable solution to the strategies to retain the customers in the banking organisations.

Future communication paradigms, such as 6G networks, emphasize self-sustainability, intelligent networking, and secure, adaptive communication. This research presents an innovative routing framework tailored for Underwater Sensor Networks (UWSNs) and Underwater Acoustic Networks (UANs), addressing critical challenges like energy constraints, security vulnerabilities, limited bandwidth, and interference. The proposed system integrates a Multi-Agent System (MAS), blockchain technology, and acoustic communication to enhance security, optimize energy usage, and improve data transmission efficiency. Key features include intelligent node mechanisms, proactive bandwidth and interference management, a multi-hop paradigm, distance-aware longevity strategies, and robust cryptographic protocols. The system is benchmarked against established routing protocols such as GCORP, PER, MARL-MC, and MLAR, demonstrating superior performance. The proposed cognitive intelligence (CI) protocol achieves energy consumption below 120 J per transmission, significantly lower than existing methods. It also achieves end-to-end latency under two seconds in multi-hop scenarios, outperforming alternatives like MARL-MC and GCORP. Additionally, the CI protocol exhibits a packet delivery ratio (PDR) exceeding 90% and an extended network lifetime surpassing 1850 s, making it a robust solution for resource-constrained underwater environments. This work not only addresses the unique demands of underwater networks but also contributes to the vision of self-sustainable and intelligent communication systems, aligning with the broader context of 6G paradigms through energy-efficient routing, cognitive intelligence, and secure, adaptive communication frameworks. The results underscore the effectiveness of the CI protocol in enhancing energy efficiency, reducing latency, and ensuring reliable long-term operation, thereby supporting critical applications like disaster management and environmental monitoring.

The sports industry wields a direct and significant impact on the natural environment, with human activities having wrought considerable damage and contributed to climate change over the years. The urgency of addressing climate change has become undeniable, and the responsibility rests with sports enthusiasts to mitigate its harmful effects. Central to this endeavor is the dissemination of awareness about sustainable practices within the sports sector. Accordingly, two research questions are posed: What key aspects do sports organizations prioritize concerning climate change? And what role do digital technologies play in promoting sustainability in the context of climate change? The Sports for Climate Action (S4CA) framework outlines the fundamental principles and goals necessary for achieving ecological sustainability through sports. As a case study, this paper presents a comprehensive analysis of 22,647 tweets collected over two years, contributed by 14,354 unique-verified Twitter users. The resulting content and statistical analyses underscore the pivotal role of pro-environmental practices in the quest for sustainability within the realm of sports. Twitter users actively share information about athletes, sports events, leagues, products, and business classifications, all within the context of sports ecology. Notably, these discussions emphasize the integration of digital technologies such as artificial intelligence techniques, blockchain, and the Internet of Things, as the means to construct intelligent solutions for sports management practices.

Skin lesion segmentation presents significant challenges due to the high variability in lesion size, shape, color, and texture and the presence of artifacts like hair, shadows, and reflections, which complicate accurate boundary delineation. To address these challenges, we proposed ARCUNet, a semantic segmentation model including residual convolutions and attention techniques to improve segmentation accuracy to address the challenges of skin lesion segmentation, By incorporating residual convolutions and attention mechanisms, ARCUNet enhances feature learning, stabilizes training, and sharpens focus on lesion boundaries for improved segmentation accuracy. Residual convolutions ensure better gradient flow and faster convergence, while attention mechanisms refine feature selection by emphasizing critical lesion regions and suppressing irrelevant details. The model was tested on the ISIC 2016, 2017, and 2018 datasets with outstanding segmentation results with accuracy measures of 98.12%, 96.45%, and 98.19%, Dice measures of 94.68%, 91.21%, and 95.34%, and Jaccard measures of 91.14%, 88.33%, and 93.53%, respectively. These findings signify the ability of ARCUNet to segment skin lesions accurately and thus as an effective tool for computerized skin disease diagnosis.

The adoption of wireless charging for Electric Vehicles (EVs) is on the rise, promising enhanced user convenience. Concurrently, there is a pressing need for increased integration of renewable energy into the transportation sector to mitigate greenhouse gas emissions. However, wireless charging systems face challenges such as power transfer fluctuations under load and variations in coupling coefficients. This research introduces a novel solution: a Photovoltaic (PV)-integrated hybrid-compensated wireless charging system tailored for EV applications. The study addresses critical hurdles in achieving optimal efficiency and power delivery while accommodating significant misalignments inherent in Wireless Power Transfer (WPT) systems, particularly beneficial for EV charging. Compared to conventional compensation WPT systems, the proposed hybrid compensation approach significantly enhances transfer efficiency and reduces system current stress. The paper proposes the utilization of S-LCC/SP-compensated constant current (CC) and constant voltage (CV) system for PV-integrated static WPT systems. Experimental validation of the proposed system is conducted on a 3.3-kW laboratory-scale prototype. The findings demonstrate consistent power transfer across a 100–200 mm air gap, maintaining an efficiency of 91.3%. These results confirm the effectiveness of the proposed system in real-world applications.

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.

Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the autonomous categorization of six forms of nail disorders by employing images: Blue Finger, Clubbing, Pitting, Onychogryphosis, Acral Lentiginous Melanoma, and Normal Nail or Healthy Nail Appearance. Based on this, we build an initial baseline CNN model, which is then further advanced by the introduction of the Hybrid Capsule CNN model by the reduction of space hierarchy deficiencies of the classic CNN model. All these models were trained and tested using the Nail Disease Detection dataset with intensive uses of techniques of data augmentation. The Hybrid Capsule CNN model, thus, provided superior classification accuracy compared to the others; the training accuracy was 99.40%, while the validation accuracy was 99.25%, whereas the hybrid model outperformed the Base CNN model with astounding precision, recall of 97.35% and 96.79%. The hybrid model additionally leverages the capsule network and dynamic routing, offering improved robustness against transformations as well as improving spatial properties. The current study consequently provides a very viable, economical, and accessible diagnostic tool, especially for places with a paucity of medical services. The proposed methodology provides tremendous capacity for early diagnosis and better outcomes for the patient in a healthcare scenario.Clinical trial number Not applicable.

Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early detection mechanism increases the chances of successful treatment and survival, as early diagnosis promptly offers interventions that can dramatically reduce the rate of deaths attributed to this disease. Here, a customized Convolutional Neural Network (CNN) model is proposed for cervical cancerous cell detection. It includes three convolutional layers with increasing filter sizes and max-pooling layers, followed by dropout and dense layers for improved feature extraction and robust learning. By using ResNet models as inspiration, the model further innovates by incorporating skip connections into the CNN design. By enabling direct feature transmission from earlier to later layers, skip links enhance gradient flow and help preserve important spatial information. By boosting feature propagation, this integration increases the model’s ability to recognize minute patterns in cervical cell images, hence increasing classification accuracy. In our methodology, the SIPaKMeD dataset has been employed which contains 4049 cervical cell images that are arranged into five different categories. To address class imbalance, Generative Adversarial Networks (GANs) have been applied for data augmentation; that is, synthetic images have been created, that improve the diversity of the dataset and further enhance the robustness of the same. The present model is astonishingly accurate in classifying five cervical cell types: koilocytes, superficial-intermediate, parabasal, dyskeratotic, and metaplastic, thus significantly enhancing early detection and diagnosis of cervical cancer. The model gives an excellent performance because it has a validation accuracy of 99.11% and a training accuracy of 99.82%. It is a reliable model in the diagnosis of cervical cancerous cells because it ensures advancement in the computer-assisted cervical cancer detection system.

This research investigates control theory using an advanced two-dimensional inverted magnetic needle system. The complex dynamics of the system are caused by a non-uniform external magnetic field. The system dynamics are established using Euler’s equations, and an energy-based controller is proposed to stabilize the needle near an unstable equilibrium point. We propose energy-based control techniques and compare their performance to the Model Predictive Controller (MPC) performance. An important contribution of this research is the rigorous investigation of closed-loop system stability using Lyapunov function analysis and tracking the performance of energy-based control techniques and MPC controller. The dynamic behavior of the magnetic needle is further enriched by two rotational degrees of freedom, influenced by attractive and repulsive forces from external magnets. Moreover, we assess the effectiveness of energy-based control strategies in both uniform and non-uniform magnetic fields, thereby expanding the applications of control theory.

Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG.

Rapid growth in technology has created opportunities to design and develop high-end applications and tools. Conventional mowers in practice are mostly fuel-powered and require personnel assistance for operation. This work develops a smart lawn mower powered by a solar photovoltaic (PV) panel and controlled by an Internet of Things- (IoT-) based technique. The designed lawn mower comprises one brushless direct current (BLDC) motor, four gear motors, sensors, an Arduino-based charge controller, and a Raspberry Pi-powered renewable energy source making it a sustainable device. A lawn mower is operated and controlled through an Android application. Raspberry Pi is used as an edge computing device for transmitting data through the Internet and for communication with Android applications. Arduino UNO is used for energy management and motor control operation. The main novelty of this research is IOT-based motion control feature which provides the user the provision to operate the mower remotely. Results of the designed model depict an average of 89.5% electrical efficiency of the system based on varying weather conditions. Application of the designed model is golf clubs, playgrounds, and lawns eliminating operator costs, saving energy, reducing noise pollution, and achieving environmental sustainability goals.

The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.

This paper presents an intelligent system containing FSR-based posture detection using machine learning algorithms. This paper is aimed at detecting the sitting posture of a wheelchair user. Individuals using wheelchairs are at increased risk of pressure ulcers when they hold an incorrect position for too long because the blood supply desists at some points of their skin due to increased pressure. The main objective of this research is to find a better configuration combined with the best machine learning algorithm for the detection of posture to prevent pressure ulcers. In the proposed monitoring system, two configurations consisting of a 3 × 3 matrix configuration (9 sensors) and a crossconfiguration (5 sensors) of FSR sensors are embedded on a wheelchair seat to get pressure data generated and collected in a real-time processing unit and then compared. The posture recognition is performed for five sitting positions: ideal, backward-leaning, forward-leaning, right-leaning, and left-leaning based on five machine learning algorithms: K -nearest neighbors ( K -NN), logistic regression (LR), decision tree (DT), support vector machines (SVM), and LightGBM. The research study provides a system to detect a real-time pressure sitting posture on a processing unit (laptop) wirelessly using the ESP32 module. Consequently, a posture classification accuracy of up to 95.41% is accomplished using a 3 × 3 matrix configuration. The proposed system helps prevent pressure ulcers and is valuable in risk assessment related to pressure ulcers. This system describes the relationship between accuracy, different sensor configurations, and performance of the multiple machine learning algorithms.