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AES-Based Steganography Using Blockchain: A Novel Approach for Secure Text Hiding in Encrypted Images

Informatica (Slovenia) 2024年第21期48卷 67-78页

作者： Salim, Batool Arif Aljabery, Maalim A. Younis, Hameed Abdulkareem Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah Iraq

Steganography is a technique used to hide data within other data, emerging from the realization that information is valuable and must be concealed. By considering the potential of blockchain technology, which produces and stores data in an immutable chain, it is clear that steganography can be effectively applied alongside blockchain to hide information. This approach eliminates the need for traditional hiding methods. In this study, we aim to hide text messages within encrypted images using a new steganography-based blockchain, making them appear as ordinary encrypted images. The AES algorithm in CBC mode was used to encrypt both images and texts. Each image was split into 32-byte blocks, with a special block allocated for text, allowing for a text size of 32 characters. The robustness of the proposed technique against differential attacks was assessed using unified averaged changed intensity (UACI), number of pixels change rate (NPCR), entropy analysis, and correlation analysis. The outcomes are 99.6221% for NPCR, 33.5886 for UACI, and 7.9992 for the entropy value. Both statistical measures and differential metrics confirm the algorithm's effectiveness. This shows that the proposed encryption method generates random images and secure texts that are resistant to differential attacks and offer a prominent level of security. © 2024 Slovene Society Informatika. All rights reserved.

关键词： AES algorithm blockchain CBC mode correlation analysis entropy analysis NPCR secure texts steganography UACI

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Anomaly Detection Control Through One-class Learning in Defect Inspection Systems

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Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 328-333页

作者： Jeon, Janggun Kim, Namgi Dept of AI Computer Engineering and Dept of computer science Kyonggi University Korea Republic of

This paper presents a novel image-level anomaly detection control method for defect inspection, which comprises featureextraction from high-dimensional data and feature-learning to analyze the observed patterns. we focus on the feature-learning step, especially in Deep SVDD. To determine anomalies from images of object suspected to be defective, Deep SVDD learns the decision boundaries for the normal feature distribution in the latent space. When a new image is received by the defect inspection system, Deep SVDD maps its features to the latent space and detects anomalies based on whether the observed features fall within the decision boundaries for normal features. The decision boundary of Deep SVDD is determined based on the center-point, which is the center position of the decision boundary. The center-point is randomly fixed at the beginning of training of Deep SVDD. However, this limits the possibility of finding the optimal decision boundary. Therefore, we propose a center-point-moving method to optimize the center-point location and improve the performance of Deep SVDD. To confirm the suitability of the proposed method, we conducted experiments on MVTec-AD, a defect inspection dataset for several products in the manufacturing industry. Based on the results, the proposed method improved the performance of Deep SVDD in 11 of the 15 class domains in the MVTec-AD dataset. © ICROS 2025.

关键词： Inspection equipment

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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Hybrid Book Recommendation System Using Collaborative Filtering and Embedding Based Deep Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第8期49卷 189-204页

作者： Remadnia, Ouahiba Maazouzi, Faiz Chefrour, Djalel Dept. of Computer Science University of Souk Ahras Algeria

We propose a hybrid e-book recommendation mechanism that leverages collaborative filtering and content-based recommendation paradigms to address inherent challenges in e-learning systems. For collaborative filtering, we present an innovative deep learning framework that utilizes embeddings to enhance accuracy and manage large datasets efficiently. This framework effectively addresses the cold start problem, thereby improving recommendation precision. In content-based recommendation, we introduce a regression-based technique to elevate system capabilities by incorporating content attributes. The integration of these techniques into our deep learning model creates a comprehensive and adaptable solution with scalability and effectiveness. Experiments on the Book Recommendation dataset demonstrate that our solution provides better suggestions and outperforms existing works in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), achieving values of 0.69 and 0.51, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Collaborative learning

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Differential Privacy-Based Data Collection for Improving Data Utility and Reducing Computational Overhead

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 102-108页

作者： Kim, Jong Wook Dept. of Computer Science Sangmyung University Korea Republic of

With the rise of real-time data collection through mobile devices such as smartphones, user-driven decision-making systems in various fields such as transportation and healthcare have advanced significantly. However, increasing concerns about the privacy of sensitive information have emerged, leading to the widespread use of differential privacy (DP) to mitigate these risks. Among DP-based data collection methods, local differential privacy (LDP) and distributed differential privacy (DDP) are prominent. While LDP provides low computational overhead, it compromises data utility, while DDP provides better utility at the cost of higher computational overhead. To address these limitations, this paper introduces a DP-compliant (θ,λ) data collection method. The proposed approach collects a fraction of θ of the total items and a fraction of λ of the total user data, achieving improved data utility compared to LDP while significantly reducing computational overhead compared to DDP, resulting in more efficient data collection. Experimental results on real-world datasets show that the proposed method not only improves data utility over LDP, but also effectively reduces computational overhead over DDP. Copyright © The Korean Institute of Electrical Engineers.

关键词： Data collection

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computer Vision-Based Object Detection and dept. Estimation to Generate Point Clouds and Estimate Distances

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 164-169页

作者： Eom, Tae-Hyun Paik, Woojin Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of

This research proposes a distance estimation method using Mono Camera-based object detection and dept. estimation to generate Point Cloud data. The study aims to enhance the applicability of Mono Cameras in autonomous vehicles and robots, reducing costs compared to Stereo Camera systems. The method utilizes YOLOv8 for object detection and dept. Anything V2 for dept. estimation. Results indicate that while the proposed method offers potential, it exhibits higher error rates in distance estimation compared to Stereo Camera-based approaches, primarily due to the limitations of current dept. estimation technologies. The study highlights the need for further improvements in dept. estimation models, particularly to address environmental factors such as lighting. Additionally, the research demonstrates that combining dept. estimation with bounding box methods helps reduce estimation errors, showing promise for more stable performance. Future work will focus on improving dept. estimation accuracy and making the proposed method more efficient for real-time applications, with the potential to integrate into Visual SLAM, a key technology in autonomous driving systems. © 2025 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Stereo image processing

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Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

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International Journal of computers and Applications 2024年第8期46卷 651-665页

作者： Manna, Supriya Mudigonda, Krishna Siva Prasad Dept. of Computer Science and Engineering SRM University AP Mangalagiri Andhra Pradesh India

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Efficiency

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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