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Crop pests are a major cause of economic loss and environmental damage globally. Timely detection of pests is crucial for protecting crops and maintaining the global food supply. However, existing diagnostic methods are especially manual, demanding significant time and expert knowledge. Incorrect pest identification can result in the misuse of pesticides, affecting both crop yields and the surrounding ecosystem. Therefore, there is a need for an automated solution that offers more precise pest identification and classification. So, in this research work, a new Internet of Things (IoT)-based pest detection and classification technique is implemented. In the initial phase, essential images are collected from a standard database that includes the IoT sensor-based pest images. Next, the IoT sensor-based images are offered as the input to the Joint pest detection and classification phase. In this phase, a new framework named Feature Pyramid Network with Multi-Attention Fusion Vision Transformer-based Adaptive Long Short Term Memory (FPN-MAFViT-ALSTM) is employed to execute the pest detection and classification procedure. Moreover, parameters in FPN-MAFViT-ALSTM are tuned using Enhanced and Intelligent Gooseneck Barnacle Optimization with Randomized Exploration (EIGBO-RE), which helps in improving pest detection and classification. At last, pest detection and classified outcomes are obtained from FPN-MAFViT-ALSTM, and then various experiments are carried out to verify its efficiency under varying conditions.

The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.

The development of 6G communication networks necessitates transformative advancements in massive MIMO systems to accommodate escalating data traffic and user demands. Different issues faced by the classical MIMO models are higher computational complexity, poor adaptability to dynamic environments, and suboptimal spectral-energy trade-offs. Classical algorithms often suffer from high computational complexity, limited adaptability to dynamic channel conditions, and suboptimal spectral-energy efficiency trade-offs. The primary objective of the research is to develop a hybrid precoding design using deep learning to optimize resource allocation and antenna selection in massive MIMO systems. Unlike classical telecommunication approaches, the implemented approach may achieve a superior trade-off between spectral and energy efficiency, setting a new benchmark for intelligent precoding strategies. Hence, to tackle several issues that takes place in the prior massive MIMO in 6G, a novel deep learning-based framework is designed by optimizing spectral and energy balancing in the 6G network for enhanced communication. In this research work, better spectral and energy balancing is performed using a novel technique, an Adaptive Residual Recurrent Neural Network (ARes-RNN), which is efficient to learn the structural information of the MIMO system along with the design of hybrid precoders. The applied Enhanced Dung Beetle Optimizer (EDBO) algorithm is used to optimize ARes-RNN parameters, enhancing the network’s learning ability and performance. Unlike the conventional models, the presented ARes-RNN model attained a spectral efficiency of approximately 79.4% for the SNR variation of 25 dB. The method shows improved energy and spectral efficiency balance, reduced computational complexity, and higher throughput. The performance of the 6G network in the massive MIMO is increased by the proposed deep learning with optimized parameters. The method achieves better spectral energy balance and is suitable for future wireless communication networks when compared to other classical approaches already existing in this domain.

Presently, blockchains are widely employed to execute the secure data transmission process among users. However, sharing the sensitive information about the patients among multiple users in the healthcare sector is difficult due to integrity and confidentiality issues. Thus, to tackle these issues in prior models, a secure data-sharing framework for Software-Defined Wireless Body Area Networks (SDWBANs) is designed. In order to ensure privacy and controlled access in SDWBANs, blockchain technology and encryption techniques are considered, which help to protect sensitive medical data. Then, the Adaptive Deep Conditional Random Field (ADCRF) is employed to perform the decision-making procedure. Further, an advanced encryption technique, Optimal Key-based Multi-Authority Attribute-Based Encryption (O-MA-ABE), is employed to ensure that authorized users can access the confidential health data. Here, the Modified Escape Search-based Piranha Foraging Optimization Algorithm (MES-PFOA) is employed to tune the Hyperparameters of ADCRF and keys of O-MA-ABE. Then, the overall performance of the developed framework is compared with classical approaches using metrics such as computational time, decryption time, and decision-making accuracy. In various validations, the developed MES-PFOA-O-MA-ABE + ADCRF-based data sharing model accomplished higher accuracy as 98.7%, precision as 98.3%, minimal encryption time as 210 (ms) and throughput as 275 (TPS) than the recent data sharing models like DTAC-TL-QM, SCCE-DS, BFL-hIoT and PPFL-ICP.

Maintaining image fidelity during transmission is challenging for live optical quantum image transmission. This paper introduces a novel \"Quantum-Inspired Adaptive Loss Detection and Real-time Image Restoration\" approach. The method incorporates adaptive loss detection and real-time restoration techniques, drawing inspiration from quantum principles to model the optical quantum environment. The core innovation is a near-to-far continuous approach adapted to the quantum environment's dynamics, enhancing image clarity and quality. A Network-in-Network architecture with MLPConv layers is proposed for the system model to estimate the transmission map for image de-hazing using the Reinforcement Learning system (ID-RL). A depth-aware dehazing reinforcement learning framework tackles image regions separately. Experiments demonstrate superior over prior SSIM and PSNR arts, even with minimal training data. Efficiency for real-time usage is shown, with potential for autonomous surveillance applications in smart cities. This quantum-inspired adaptive technique is a promising advancement for live optical quantum image transmission fidelity.