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Security encryption processes are basic principles of data protection. Traditional approaches of encryption pose a challenge in achieving the right balance of security and computational performance. It important to look for encryption processes with good level of security considering changing threats and advancement in computational power. Furthermore, traditional methods of encryption may not make the best use of properties that are unique to image data. In order to solve the aforementioned challenges, this paper presents new approaches based on deep learning models: Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) networks to improve the image encryption methods. The encryption using GAN based approach resulted in an entropy of 11.5745 and correlation coefficients of (0.5355, 0.4279, 0.3690), while the LSTM based approach reached the entropy of 11. 5850 and the corresponding correlation coefficients were (0.8173, 0.8844, 0.7468). Both methods demonstrated 100% NPCR, confirmed their high sensitivity to small changes of the input. Our methods achieved a higher entropy value than AES for encryption considering more uniformed histograms of the generated sequences. However, more work is needed to answer the main concern as both GAN and LSTM approaches yielded SSIM equal to 1.00. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

The way we learn has been profoundly altered by the mix of augmented reality (AR) and Internet of Things (IoT). AR has been shown to be beneficial in several industries, including education. Still, there is a lot to learn about how various AR content kinds affect learning objectives. IoT interfaces tangible items to the Internet, while AR advances education by blending digital resources with the actual world. This blend makes an integrated, intuitive, and immersive growth opportunity, offering students engaging and experimentally improved educational opportunities. By consolidating AR and IoT, students can explore virtual and intelligent items inside a genuine setting, improving the dynamism and significance of education. Incorporating IoT and AR into instructive settings can change education by encour-aging dynamic getting the hang of, further developing information maintenance, and helping innovativeness. This examination means to foster a savvy learning climate utilizing the communication among AR and IoT, distinguishing the difficulties and benefits of AR in training. The methodology includes two phases: first, making an electronic stage with incorporated educational plans, including pictures, record-ings, and PDFs; second, laying out a smart environment furnished with gadgets like tablets, cell phones, and broadcasting devices. This mix of technology could prompt the improvement of brilliant grounds by joining AR and IoT in the instructive cycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

An experimental and theoretical investigation was carried out to evaluate the effect of the flexural loads on the stress distribution oriented by “0, 45, 45,0” laminates of Kevlar-Epoxy composite. The mechanical response of a new advanced composite material was evaluated by comparing the measured and computed deflection values at the mid-point. The results are compared to other composites that have been built. The use of FEM to analyze composite laminates is limited to stacking sequences with symmetry in the midplane and orientated in the lower half of the laminate, as well as their reflections on the upper half plies. The results of the experimental and F E M show fair agreement. © 2025 Editura Politechnica. All rights reserved.

Clustering is essential for discovering patterns in data, but traditional methods like DBSCAN face challenges with varying densities and overlapping clusters. This study presents an Enhanced Adaptive DBSCAN (ADBSCAN) algorithm that dynamically adjusts clustering parameters based on local density variations and integrates multiple validation metrics for robust performance evaluation. Dimensionality reduction techniques further improve effectiveness on high-dimensional data. Benchmarking against modern clustering algorithms across several complex datasets highlights the improved accuracy, efficiency, and practical utility of the proposed approach. Future studies should concentrate on enhancing adaptation mechanisms to better manage overlapping features and varying data density, enhancing the algorithmś resilience and practicality. A comprehensive sensitivity analysis and comparison of clustering performance in original feature space versus dimensionality-reduced space further underscore the algorithm’s adaptability. Copyright © 2025 International Academic Press

Coal is a key fuel source and an important resource for a wide range of businesses. The hazardous and potentially poisonous nature of the work also has to be taken into consideration. High temperatures, humidity, and the discharge of hazardous gases are just a few of the challenges coal miners confront on a daily basis. This produces a risky work environment that places employees at risk of serious injury or death. In this paper, IoT based Dynamic Sensor Information Control System (IoT-DSICS) has been proposed to solve warm humidity, precipitation, and unhealthy carbon emissions of the coal mine. Using sensor networks and control systems that have been deployed in many sectors, the Industrial Internet of Things (IIoT) is combined in this article. The present security examination of information management has been evaluated since the national coal mining output remains serious and significant accidents are successfully limited. Wi-Fi microcontroller system IIoT is used to monitor and operate the prototypes, activate fans in the Pittsburgh Investigation of Mine, and trigger a surface alert to track the low cost of opening alternative coal. The findings of this feasibility study existing communication and tracking infrastructure are leveraged to examine the potential of IIoT in underground coal mines. © 2022 Elsevier Ltd

Cloud services are the cutting edge technology, however the growing demand for the internet of things has certain limitations which are high latency expectation and high cost of cloud resources, and this is caused by long-distance between application and cloud. Fog computing is a distributed extension of the cloud, which provide storage and computation at the network level. It consists of an internet of things (IoT) application, a fog control node, and a fog access node. This research works towards minimizing the cloud cost in scheduling. For this purpose, a cost-effective task and user scheduling algorithm are performed. The first task scheduling model is composed based on composers' roles after that task scheduling algorithm is performed to handle the various task at the fog access node in an optimized manner. Finally, the reallocation mechanism reduces the time and service delay. For the analysis purpose extensive simulation is carried out and performance statistics were compared with other existing algorithms. It was observed that the proposed algorithm provides highly cost-optimized user and task scheduling with better performance statistics and reduces the delay in the task by providing optimization in the concurrent task at the fog node. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Multirotor Unmanned Aerial Vehicles (UAVs) play an imperative role in many real-world applications in a variety of scenarios characterized by a high density of obstacles with different heights. Due to the complicated operation areas of UAVs and complex constraints associated with the assigned mission, there should be a suitable path to fly. Therefore, the most relevant challenge is how to plan a flyable path for a UAV without collisions with obstacles. This paper demonstrates how a flyable and continuous trajectory was constructed by using any-angle pathfinding algorithms, which are Basic Theta*, Lazy Theta*, and Phi* algorithms for a multirotor UAV in a cluttered environment. The three algorithms were modified by adopting a modified cost function during their implementation that considers the elevation of nodes. First, suitable paths are generated by using a modified version of the three algorithms. After that, four Bézier curves-based approaches are proposed to smooth the generated paths to be converted to flyable paths (trajectories). To determine the most suitable approach, particularly when searching for an optimal and collision-free trajectory design, an innovative evaluation process is proposed and applied in a variety of different size environments. The evaluation process results show high success rates of the four approaches; however, the approach with the highest success rate is adopted. Finally, based on the results of the evaluation process, a novel algorithm is proposed to increase the efficiency of the selected approach to the optimality in the construction process of the trajectory. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Todays, networks security of has become the important problem in each distributed system. A lot of attacks are becoming less able to detect with software of antivirus and firewall. For improving the security, intrusion detection systems (IDSs) are utilized for detecting the anomalies in traffic of network. Network anomaly detection issue is determining, if incoming traffic of network is anomalous/ legitimate. The automated system of detection schemed for identifying the incoming anomalous patterns of traffic usually apply widely utilized techniques of machine learning. In the article, we have utilized the Information Gain- based algorithm. The algorithm chooses the features optimal number from dataset of NSL-KDD. Additionally, we have integrated selection of feature with the technique of machine learning namely as Support Vector Machine (SVM) by utilizing the algorithm of artificial bee colony as well as Optimization-Cuckoo Search Algorithm for optimizing SVM hyper parameters for dataset effective classification. Proposed method performance has been assessed on the modern intrusion dataset as NSLKDD. Experimental results show that the proposed method outperforms also achieves high accuracy in comparison to the other modern techniques in NSLKDD. © 2021 Zibeline International Publishing Sdn. Bhd.. All rights reserved.

There is a demand to change the contents and activities, and adapt the methods for higher education institutions, especially, universities to let researchers and educational act more efficiently in a digital context. A well-designed campus that combines technology is basic for developing digital university through facilities for learning, teaching, and research, enhancing the student trials, and supplying convenient settings. Within digital universities, technology can improve security, reduce costs, and offer devices for faculty, scholars, academics, and students. These advantages give more attention to university processes and evolutions, the experience of researchers and students. In this research, we have done a study on the Internet of things and its role in the development of education through the review of a group of previous research. Also, we have studied the smart class and its components and its difference with the traditional classes. Then we have displayed the smart laboratories and its applications. At the end of the research, the great importance of Internet of Things in universities and its importance to the teacher and the student was concluded by learning faster and developing and improving the educational process. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.

This paper presents a steganalysis model that uses an enhanced grayscale statistical feature set, in the detection of data hiding in uncompressed RGB color images. A dataset of 3000 RGB images is created, using natural images from public sources, in TIFF and JPEG formats, that are converted to BMP format and resized to 512x512 pixels. The clean images are embedded with secret image data, using two payload schemes, 2 bits per channel (bpc) and 4 bits per channel. The selected feature set consists of 24 features per color channel, 72 features per image, which includes the Gray Level Co-Occurrence Matrix (GLCM) features, Entropy features, and statistical measures of variation. The feature set elements are calculated for individual channels, combined into image features vector. The steganalysis process is based on supervised machine learning, utilizing the Support Vector Machine (SVM) binary classifier's implementation in MATLAB. The results show very high detection accuracy for the two cases of 2-bpc and 4-bpc embedding schemes. Also, there are no noticeable differences in the detection accuracy between the two sources of images, even though un-compression of the JPEG images has reduced their noise contents. The paper ends with a conclusion and suggestions for future work. © 2018 IEEE.