Explore the vast range of titles available.

In the present scenario, competitive sports require athletes to achieve a phenomenal balance between cognitive abilities, motor skills, nutritional intake, and energy deficiencies. Such stability would enable the athletes to excel in their sporting field. Evidence shows that athletes develop specific cognitive abilities based on their sporting field. Nutrition is vital in creating an athlete's cognitive ability and physical needs required to participate in competitive sports. The reduction in the intake of nutrients required before, after and during sports participation could result in relative energy deficiency in sports (RED-S), affecting the parts of the body. The rationale behind the survey is to understand the role of nutrition and energy deficiency on the athletes' cognitive abilities. The review's research areas were identified as athletes' cognition and nutrition in the context of RED-S. Search keywords were found based on the research area, such as \"cognitive\", \"nutrition\", and \"energy deficiency/availability\" in athletes. The search keywords were combined to form search queries (SQs). SQs were used to carry out the search on the Web of Science and Scopus databases. Sports play an important role in athletes' cognitive abilities, such as decision-making, attention, memory, . Nutritional intakes, such as caffeinated, carbohydrate, alkaline, and protein-based supplements and diets, also significantly affect athletes' cognitive and motor abilities. Low energy availability (LEA) causes cognitive and physical health problems in both female and male athletes. The review identified that nutrition and LEA play crucial roles in athletes' cognitive performance. Deficits in nutritional intake and energy availability lead to RED-S. Hence, cognitive performance could be used as an early indication to identify the nutritional and energy deficits in advance, enabling athletes to combat RED-S.

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio.

Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalability, frequently necessitating specialized equipment and expertise. This study presents a deep learning-based automated diagnostic system for rice leaf diseases, leveraging a large-scale dataset comprising annotated images spanning six common rice diseases: bacterial stripe, false smut, leaf blast, neck blast, sheath blight, and brown spot. We evaluated seven advanced deep learning architectures—MobileNetV2, GoogLeNet, EfficientNet, ResNet-34, DenseNet-121, VGG16, and ShuffleNetV2—across a range of performance metrics including precision, recall, and overall diagnostic accuracy. Among these, GoogLeNet, DenseNet-121, ResNet-34, and VGG16 demonstrated superior performance, particularly in minimizing class confusion and enhancing diagnostic accuracy. These models were selected based on diverse architectural principles to ensure complementary feature extraction capabilities. An ensemble model, integrating these four high-performing networks via a simple average fusion strategy, was subsequently developed, significantly reducing misclassification rates and providing robust, scalable diagnostic capabilities suitable for deployment in real-world agricultural settings. The model’s performance was further validated on independent test data collected under varying environmental conditions.

In recent years, the widespread dissemination of fake news on social media has raised concerns about its impact on public opinion, trust, and decision-making. Addressing the limitations of traditional detection methods, this study introduces a hybrid deep learning approach that enhances the identification of fake news. The objective is to improve detection accuracy and model robustness by combining a Long Short-Term Memory (LSTM) network for contextual feature extraction with a Convolutional Gaussian Perceptron Neural Network (CGPNN) for classification. To further optimize performance, we integrated a metaheuristic Moth-Flame Whale Optimization (MFWO) algorithm for hyperparameter tuning. Experimental evaluation was conducted on four benchmark datasets ISOT, Fakeddit, BuzzFeedNews, and FakeNewsNet using standardized preprocessing techniques and TF-IDF-based text representation. Results show that the proposed model outperforms existing methods, achieving up to 98% accuracy, 95% F1-score, and statistically significant improvements ( p  < 0.05) over transformer-based and graph neural network models. These findings suggest that the hybrid framework effectively captures linguistic patterns and textual irregularities in deceptive content. The proposed method offers a scalable and efficient solution for fake news detection with practical applications in social media monitoring, digital journalism, and public awareness campaigns. Overall, the framework delivers 3–8% higher accuracy and F1-score compared to state-of-the-art approaches, demonstrating both robustness and practical applicability for large-scale fake news detection.

The world’s median population is projected to reach 8.8 billion by 2050, making water management, especially groundwater, increasingly important. This research article seeks to address the pressing need for an effective method of evaluating and forecasting groundwater quality. Traditionally, water quality testing methods entail significant experimentation, a time-consuming procedure. To overcome this challenge in an alternative way, our proposed methodology involves parameterizing groundwater physicochemical parameters (Q-value) using machine learning algorithms. Focusing on groundwater samples collected from the Russell River of Australia between December 2016 and April 2020 (scaled to approximately 1300 datapoints on average), this research article uses the concept of Q value, a standardized measure facilitating comprehensive water quality assessment. This study significantly contributes to sustainable water resource management by providing a comprehensive examination of groundwater quality through the utilization of deep learning algorithms. The proposed models, namely Conv-LSTM with Attention, Conv-Bi-LSTM with Attention, LSTM with Attention, and Bi-LSTM with Attention, not only offer a distinctive framework for forecasting Q values but also serve as essential tools for timely decision making in water resource distribution. Among these, the Bidirectional LSTM with Attention model achieved the highest predictive accuracy, with a root mean square error of 0.0057, a mean absolute error of 0.0022, a symmetric mean absolute percentage error of 3.8875%, and a coefficient of determination of 0.9910. These results demonstrate its effectiveness in capturing variability and accurately explaining observed trends in groundwater quality. The proposed framework is a reliable, scalable, and timely decision-support tool for water resource management and policy making.

Many advanced technologies have been developed in the medical field where leukaemia plays a vital role, which may cause serious issues when it is unidentified. In the convolution method, human error may occur, so to avoid it, many tools have been introduced, like Adaptive Network-Based Fuzzy Inference Systems (ANFIS), which helps to diagnose and classify systems for leukaemia, and it is also shown to be an excellent function approximation tool. ANFIS also uses the ANN theory, which is used to conclude the attributes of neuro-fuzzy systems. But the accuracy is not up to the mark. To overcome this drawback, we have proposed an improved ANFIS (I ANFIS) model to predict leukaemia data using a Euclidean distance to measure between the trained feature data and the test feature data. An Improved Adaptive Neuro-Fuzzy Neural Network (ANFNN) is also introduced, which helps the input space be partitioned into many local regions by the fuzzy clustering, in which the computation complexity is decreased and, based on both the separation and the compactness among the clusters, the fuzzy rule number is determined by the validity function. Then, the premise parameters and consequent parameters are trained by a hybrid learning algorithm which uses forward and backward passes. Following the arrangement of principle parameters, until layer 4, a node outputs move ahead in the forward pass and, using Least Square Estimate (LSE), the consequent parameters are calculated for each node. Then an error measure is calculated for each node. To update principal parameters, the error signals are distributed backward with gradient descent in the backward pass. Improved ANFIS obtains the best accuracy, sensitivity, specificity of 97.14%, 96% and 90%, and classification for all the cell types, especially in the microscopic blood cell dataset.

This paper presents a novel Twin Convolutional Neural Network (CNN)-based framework for classifying rice leaf diseases. The framework integrates an optimized feature fusion algorithm using pre-trained CNN models to improve disease detection accuracy. Rice leaf images are processed to classify plants as either healthy or diseased with greater accuracy compared to conventional methods. Experiments conducted on publicly available datasets demonstrate that the proposed Twin CNN architecture, combined with a robust feature fusion mechanism, outperforms existing methods in terms of accuracy and computational efficiency. The proposed framework shows promising results for real-world applications in precision agriculture.

Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model’s performance is compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges.

One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new application, the proposed system will present a better technique that focuses especially on resolving the cold start issue. The suggested system will compute similarity using the Pearson Correlation Coefficient (PCC). Collaborative filtering that uses PCC suffers from the cold start problem or a lack of information on new users to generate useful recommendations. The proposed system solves the issue of cold start by gauging each new user by certain arbitrary parameters and recommending based on the choices of other users in that demographic. The proposed system also solves the issue of users’ reluctance to provide ratings by implementing a keyword-based perception system that will aid users in finding the right product for them.

Graph theory is a useful mathematical structure used to model pairwise relations between sensor nodes in wireless sensor networks. Graph equations are nothing but equations in which the unknown factors are graphs. Many problems and results in graph theory can be formulated in terms of graph equations. In this paper, we solved some graph equations of detour two-distance graphs, detour three-distance graphs, detour antipodal graphs involving with the line graphs.