Introduction: Accurate and automated fruit classification plays a vital role in modern agriculture but remains challenging due to the wide variability in fruit appearances.Methods: In this study, we propose a novel approach to image classification by integrating a DenseNet121 model pre-trained on ImageNet with a Squeeze-and-Excitation (SE) Attention block to enhance feature representation. The model leverages data augmentation to improve generalization and avoid overfitting. The enhancement includes attention mechanisms and Nadam optimization, specifically tailored for the classification of date fruit images. Unlike traditional DenseNet variants, proposed model incorporates SE attention layers to focus on critical image features, significantly improving performance. Multiple deep learning models, including DenseNet121+SE and YOLOv8n, were evaluated for date fruit classification under varying conditions.Results: The proposed approach demonstrated outstanding performance, achieving 98.25% accuracy, 98.02% precision, 97.02% recall, and a 97.49% F1-score with DenseNet121+SE. In comparison, YOLOv8n achieved 96.04% accuracy, 99.76% precision, 99.7% recall, and a 99.73% F1- score.Discussion: These results underscore the effectiveness of the proposed method compared to widely used architecture, providing a robust and practical solution for automating fruit classification and quality control in the food industry.

In the rapidly evolving landscape of the Internet of Things (IoT), cybersecurity remains a critical challenge due to the diverse and complex nature of network traffic and the increasing sophistication of cyber threats. This study investigates the application of the Artificial Bee Colony (ABC) algorithm for hyperparameter optimization (HPO) in machine learning classifiers, specifically focusing on Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) for IoT network traffic analysis and malware detection. Initially, the basic machine learning models demonstrated accuracies ranging from 69.68% to 99.07%, reflecting their limitations in fully adapting to the varied IoT environments. Through the employment of the ABC algorithm for HPO, significant improvements were achieved, with optimized classifiers reaching up to 100% accuracy, precision, recall, and F1-scores in both training and testing stages. These results highlight the profound impact of HPO in refining model decision boundaries, reducing overfitting, and enhancing generalization capabilities, thereby contributing to the development of more robust and adaptive security frameworks for IoT environments. This study further demonstrates the ABC algorithm’s generalizability across different IoT networks and threats, positioning it as a valuable tool for advancing cybersecurity in increasingly complex IoT ecosystems.

Dates in Saudi Arabia hold immense cultural, economic, and nutritional importance, being a staple food and a symbol of heritage. Saudi Arabia produces approximately 1.5 million metric tons of dates annually, accounting for nearly 17% of global date production, underscoring its pivotal role in the global market. Precise and automated classification of fruits is a crucial aspect of modern agriculture, yet it remains a challenging endeavor due to the diverse appearances of fruits. The classification of date fruit varieties presents additional complexities, given variations in size, shape, and texture, making it a critical focus for technological advancements. Dates are highly nutritious, providing approximately 277 cal per 100 g and serving as an excellent source of dietary fiber, natural sugars, and energy. Their substantial nutritional value makes them indispensable in addressing food security challenges and promoting global health benefits. In this paper, we introduce a novel DenseNet-based model augmented with attention mechanisms and optimized using the Nadam algorithm. Unlike traditional DenseNet variants, the model integrates attention mechanisms to enhance focus on pertinent image features, thereby improving classification accuracy under challenging conditions. To evaluate its efficacy, the model was benchmarked against several state-of-the-art deep learning architectures, including DenseNet with Adam optimization, EfficientNet, GoogleNet, HRNet, MobileNet, and VGG, optimized with both Adam and Nadam algorithms. The proposed model achieved outstanding performance metrics, including 98.05% accuracy, 98.00% precision, 97.04% recall, and a 98.32% F1-score.

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

Near-Field Communication (NFC) has enabled mobile devices to emulate contactless smart cards, which has also rendered them susceptible to relay attacks. Numerous countermeasures have been proposed that use ambient sensors as an anti-relay mechanism. However, there are concerns regarding their efficacy in time-critical scenarios, such as transport ticketing and contactless payments. This paper empirically and comprehensively evaluates whether ambient sensors are an effective anti-relay mechanism for such NFC-based contactless transactions. To this end, we examine 17 sensors available via the Android platform. Each sensor, where feasible, was used to record measurements in 1,000 contactless transactions with 252 users across four physical locations. We then conduct an extensive four-part evaluation using similarity metrics, traditional machine learning models, and deep learning methods used in existing work and beyond. We conclude that mobile ambient sensors are currently unsuitable for detecting relay attacks on NFC contactless transactions under realistic timing constraints, contrary to the suggestions and proposals made in existing work.