The rise of Internet of Things (IoT) applications and the widespread use of mobile devices have made Mobile Ad Hoc Networks (MANETs) more important in modern day. Enabling effective data transport in decentralized wireless networks is largely dependent on the routing mechanism. In the present research, Mountain Gazelle Optimizer (MGO) is integrated with Low Energy Adaptive Clustering Hierarchy (LEACH) protocol to offer an effective routing and clustering framework for MANETs. Intelligent approaches are necessary for dependable data transmission and energy conservation in MANETs due to their inherent mobility and dynamic topology changes. To overcome these obstacles, the LEACH protocol is used for energy efficient and adaptive clustering, in which cluster heads are chosen at random to distribute energy usage among nodes. The Mountain Gazelle Optimizer, an evolutionary algorithm modelled after the adaptive movement behavior of gazelles, is used to optimize routing between cluster heads and destination nodes. By reducing parameters like energy usage, transmission latency, and route instability, MGO optimizes the routing paths. When compared to conventional methods, the proposed MGO LEACH model improves Packet Delivery Ratio (PRD) and network lifetime and lowers routing costs. The proposed design greatly enhances MANET’s performance, as shown by extensive simulation findings, which makes it ideal for dynamic and energy constrained wireless network situations.

Facial recognition is an essential component of human communication, impacting interactions and decision making processes. In order to facilitate more natural and efficient interactions between humans and machines, it is becoming more and more critical to include emotional awareness into machines. In this paper, a Bidirectional Gated Recurrent Unit classification is proposed to quickly recognise the facial reaction. Firstly, image resolution conversion is applied to CK+ facial recognition dataset to resize the image, then pixel values of an image based on its intensity histogram is adjusted using histogram equalization. Then it smoothen the image and removes the noise using Gaussian filter to get high quality image. Next, the processed image is given to feature extraction by Scale Invariant Feature transform it is used to find the key points. After that image features are selected using Grey Wolf Optimization (GWO) designed for further analysis. Finally, a Bidirectional Gated Recurrent Unit (Bi GRU) framework is processed to enhance the classification of face recognition. Using python software proposed framework have accuracy of 92% is accomplished when compared to other techniques.

Distributed Denial of Service (DDoS) attacks are a security challenge for the Software Defined Network (SDN). Effective traffic is critical for network durability and routine, necessitating intelligent and adaptive mechanisms. This paper presents a novel hybrid approach integrating Ant Colony Optimization (ACO) with Attention Convolutional Neural Network (CNN) to enhance cyber attack. ACO optimizes Attention CNN parameters to improve learning efficiency and convergence, enabling dynamic adaptation to real time network conditions. By using the global search capability of ACO and the predictive strength of Attention CNNs, the proposed method achieves better network traffic compared to other techniques. Using python software the ACO Attention CNN significantly increase precision recall accuracy and ROC curve for cyber attack. The proposed framework contributes to the development of intelligent, network traffic for DDoS attack, ensuring sustainable and resilient network communication.

Cucumber leaf pathogenic spores identification is an significant stage in achieving quick disease analysis for cultivation. Image processing techniques primarily rely on manually created characteristics that are challenging to handle when it comes to pathogen spore detection. In this paper, a Mobile Net classification is suggested to quickly recognise harmful spores in cucumbers leaf. Firstly, grayscale conversion is applied to the cucumber leaf disease dataset to change the color image in to gray and the unwanted noises are removed using the median filter to get high quality image. Next, the processed image is given to the K Means clustering for the separation of relevant regions. After that cucumber leaf image is featured by Gray Level Co occurrence Matrix (GLCM) textured is designed for further analysis. Finally, a Mobile Net framework is processed to enhance the classification of cucumber leaf pathogenic spores. Using python software proposed framework have accuracy of 94.12% is accomplished when compared to other techniques.

Ensuring crop health and increasing agricultural productivity depend on the diagnosis of leaf diseases.  This research work offers a sophisticated deep learning based strategy for automatically classifying leaf diseases.  To improve accuracy, the proposed method combines features from both classification and feature extraction.  Research findings indicate that the model outperforms traditional models like CNN, AlexNet, and MobileNet V2 with a high classification accuracy of 95%.  The confusion matrix and ROC curve demonstrate little misclassification and good class separation, confirming the model’s efficacy. Additionally, high precision, recall, and F1 scores across multiple disease categories confirm balanced performance. Training and validation trends indicate effective learning, with minor overfitting be addressed using dropout regularization and data augmentation. The findings establish proposed model as a trustworthy and efficient solution for automated leaf disease diagnosis in precision agriculture.