Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This study applies Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to classify plant leaf diseases using a dataset containing 70,295 training images and 17,572 validation images across 38 disease classes. The CNN model was trained using the Adam optimiser with a learning rate of 0.0001 and categorical cross-entropy as the loss function. After 10 training epochs, the model achieved a training accuracy of 99.1% and a validation accuracy of 96.4%. The LSTM model reached a validation accuracy of 93.43%. Performance was evaluated using precision, recall, F1-score, and confusion matrix, confirming the reliability of the CNN-based approach. The results suggest that deep learning models, particularly CNN, enable an effective solution for accurate and scalable plant disease classification, supporting practical applications in agricultural monitoring.

In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing protocol to further reduce the energy consumption. The proposed energy-efficient coding-aware cluster based routing protocol (ECCRP) scheme applies network coding at cluster heads to reduce number of transmissions. We also modify the queue management procedure of COPE protocol to further improve coding opportunities. We also use an energy efficient scheme while selecting the cluster head. It helps to increase the life time of the network. We evaluate the performance of proposed energy efficient cluster based protocol using simulation. Simulation results show that the proposed ECCRP algorithm reduces energy consumption and increases life time of the network.

Recent studies have highlighted the importance of implementing clustering schemes in Wireless Body Area Networks (WBANs) to address challenges such as scalability, network topology changes, spectrum scarcity, and management. However, many existing approaches focus only on conventional performance metrics and overlook the integration of spectrum trading and efficient spectrum utilization. This paper proposes a novel clustering control scheme based on fuzzy logic and Nash equilibrium to enhance scalability, network stability, and resource management in WBANs. Our approach employs multi-criteria decision-making to optimize cluster head (CH) selection and routing strategies using reinforcement learning to achieve quality of service (QoS). Additionally, a secure lightweight Diffie–Hellman key exchange is used to protect data transmission. The proposed protocol outperforms existing protocols, including TAFLR, EQRSRL, and SEBA, in terms of throughput (3.2 kbps), packet delivery ratio (93%), delay (0.31 s), cluster efficiency (95%), and energy consumption (0.43 J).

Heart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the likelihood of heart failure occurrence in individuals. In our research, the heart failure prediction and comparison are done using Logistic Regression, KNN, SVM, decision tree and random forest The accurate identification of high-risk individuals enables early intervention and better management of heart failure, reducing the risk of mortality and morbidity associated with this condition. Overall, machine learning algorithms play a major role in improving the accuracy of heart failure risk assessment, allowing for more personalized and effective prevention and treatment strategies.

Flavonoids are phytochemical compounds found in a variety of plants, fruits, vegetables, leaves, and barks, it offers a wide range of health benefits. Quercetin is a polyphenolic flavonoid that is found in onions, apples, berries, broccoli, grapes, nuts, cherries and many other food products. Pharmacologically, quercetin has been examined for various disorders and is reported to possess the ability to prevent as well as treat them. It possesses anti-oxidant, anti-allergic, anti-inflammatory, anti-viral and anti-cancer activity as well. Developing suitable formulation for the delivery of quercetin to specific site leads to better compliance and effective treatment of the disease. This review article highlights the beneficiary activities exhibited by quercetin along with the mechanism behind its activity, followed by reporting the different quercetin formulations developed in last few years.

Network coding is a new technology that motivates us to rethink about networking in wireless networks. Network coding of wireless helps to improve the performance networks. The existing network infrastructure will not support to the network coding communications. This concept requires us modifying the existing network communications. In this paper we brief the principles of network coding, network coding techniques, benefits and applications. We also discuss the current research topics of interest and the issues related to network coding aware routing in wireless ad hoc networks and we also present the simulation results which describe the energy gain and number of transmissions in traditional routing versus network coding.