Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color Histogram) and modified deep-learning architectures. We propose two models for this purpose: PestNet-EF and PestNet-LF. PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features, followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination (RFE). PestNet-LF utilizes a late fusion technique, incorporating three additional layers (fully connected, softmax, and classification) to enhance performance. These models were evaluated across 15 classes of pests, including five classes each for rice, corn, and wheat. The performance of our suggested algorithms was tested against the IP102 dataset. Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%, and the PestNet-LF model with majority voting achieved the highest accuracy of 94%, while PestNet-LF with the average model attained an accuracy of 92%. Also, the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques, showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield.

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 $ms$ of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage. Besides, the DNAC-WSAVQ model initially performs DNA encoding on the input images to generate a binary encoded form. In addition, a Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for the compression process and thereby storage area can be considerably minimized. In order to generate optimal codebook for LBG, the WSA is applied to it. The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures. The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

Device to Device (D2D) communication is expected to be an essential part of 5G cellular networks. D2D communication enables close-proximity devices to establish a direct communication session. D2D communication offers many advantages, such as reduced latency, high data rates, range extension, and cellular offloading. The first step to establishing a D2D session is device discovery; an efficient device discovery will lead to efficient D2D communication. D2D device further needs to manage its mode of communication, perform resource allocation, manage its interference and most importantly control its power to improve the battery life of the device. This work has developed six distinct scenarios in which D2D communication can be initiated, considering their merits, demerits, limitations, and optimization parameters. D2D communication procedures for the considered scenarios have been formulated, based upon the signal flow, containing device discovery, resource allocation, and session teardown. Finally, latency for each scenario has been evaluated, based on propagation and processing delays.

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for proficient classification of phishing emails, the Machine Learning (ML) techniques and the Deep Learning (DL) models have been employed in the literature. The current study presents an Intelligent Cuckoo Search (CS) Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification (ICSOA-DLPEC) model. The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones. At the initial stage, the pre-processing is performed through three stages such as email cleaning, tokenization and stop-word elimination. Then, the N-gram approach is; moreover, the CS algorithm is applied to extract the useful feature vectors. Moreover, the CS algorithm is employed with the Gated Recurrent Unit (GRU) model to detect and classify phishing emails. Furthermore, the CS algorithm is used to fine-tune the parameters involved in the GRU model. The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset, and the results were assessed under several dimensions. Extensive comparative studies were conducted, and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches. The proposed model achieved a maximum accuracy of 99.72%.

In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm for inter-cluster dialogue, our framework orchestrates CHs into an efficient communication chain. Through comparative analysis, the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing (CCMAR) and the energy-efficient cluster-based dynamic algorithms (ECCRA). Specifically, it showcases an impressive 15% increase in energy conservation and a 20% reduction in data transmission time, highlighting its advanced performance. Furthermore, this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN, emphasizing the vital role of sophisticated computational strategies in optimizing network operations.