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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Csl Vijaya Durga Shruti Agrawal R J Anandhi Navdeep Singh Ravi Kalra Myasar Mundher Adnan Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal Ma. P India Department of Information Science Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

SDODI, which stands for Sustainable Development Optimization using Distributed Intelligence, advances sustainable development. SDODI is the first to examine environmental, social, and economic challenges together using EcoOpt, InclusNet, and ResilienceHub. Environmental optimization program EcoOpt increases the total effect while ensuring resource efficiency and environmentally friendly behavior. InclusNet uses math to include everyone and make communities more active, varied, and accessible. The ResilienceHub algorithm prioritizes financial stability, flexibility, and economic power. Sustainable initiatives are typically divided by traditional approaches. SDODI utilizes information to better allocate resources, engage the community, and strengthen the economy. SDODI is unique because it adapts to changing situations, giving it a comprehensive sustainable growth solution. SDODI is new because it supports and improves sustainability ideals.

关键词： Industries Technological innovation Leadership Transforms Sustainable development Socioeconomics Protection

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Spatial Modulation Techniques for Improved ISAC Throughputs

Spatial Modulation Techniques for Improved ISAC Throughputs

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sanjay Kumar Suman B Rajalakshmi Irfan Khan V Alekhya Sorabh Lakhanpal Abduljabbar A. Ali St. Martin’s Engineering College Secunderabad Telangana Bharat Department of Computer Science New Horizon College of Engineering Bangalore Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Hyderabad Dundigal Lovely Professional University Phagwara College of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces “Adaptive Spatial Modulation for Enhanced ISAC Throughputs (ASM-ISAC)” to speed up Intelligent Space-Air-Ground Communication (ISAC). An adaptive spatial modulation technique, CSD for cooperative spatial diversity, and EESM-ISAC for energy-efficient spatial modulation are used. To boost communication throughput and reduce bit error rates, ASM-ISAC adjusts modulation and spatial patterns in real time. Cooperation between close ISAC nodes improves performance in CSD. EESM-ISAC develops energy-efficient communication with restricted resources. This research evaluates ASM-ISAC against earlier approaches like “Conventional Modulation Scheme for ISAC (CMS-ISAC)”. The test evaluates frequency efficiency, error performance, latency, complexity, energy efficiency, and security. ASM-ISAC outperforms CMS-ISAC in several aspects, suggesting it might increase ISAC throughput.

关键词： Technological innovation Frequency modulation Spatial diversity Modulation Throughput Energy efficiency Real-time systems

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SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Bhairvi Pant Mohammad S. Obaidat Shreya Singh Mudita Pant Mohammad Wazid Ashok Kumar Das Kuei-Fang Hsiao Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China School of Computing SRM University Kattankulathur TN India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Computer Science Department University of Texas-Permian Basin Odessa USA

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name “SLA-SCA”. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features.

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Adaptive Web Crawling Strategies Based on Ontological User Interest Modeling for Personalized Content Retrieval

Adaptive Web Crawling Strategies Based on Ontological User I...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Csl Vijaya Durga Anandhi R J Saloni Bansal Navdeep Singh Ravi Kalra Nabaa M. Bader Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The exponential growth of web content necessitates efficient and personalized methods for information retrieval. This research introduces an innovative approach to web crawling, centered on adaptive strategies guided by ontological user interest modeling, aiming to enhance the precision of personalized content retrieval. The proposed method employs a dynamic user interest model, constructed, and continually refined through user interactions, leveraging ontology to represent and understand the complex and evolving nature of user interests. In contrast to traditional web crawlers that operate on fixed algorithms, the adaptive crawler anticipates changes in user interests and adjusts its strategies, accordingly, ensuring relevance and timeliness of the retrieved content. The system architecture integrates a multi-faceted profiling mechanism that captures and evolves with user preferences, combined with an intelligent crawling algorithm that prioritizes content based on predicted user interest levels. Empirical results demonstrate a significant improvement in retrieval precision and efficiency, highlighting the crawler's ability to discern and adapt to the nuanced shifts in user interests. Furthermore, the research delves into the implications of such personalized crawling on user privacy and web dynamics, offering insights and recommendations for a balanced approach. This work contributes to the fields of semantic web, personalized content retrieval, and adaptive systems, proposing a novel pathway for the next generation of intelligent web crawlers.

关键词： Adaptation models Adaptive systems Heuristic algorithms Computational modeling Biological system modeling Crawlers Systems architecture

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Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing

Adaptive Multi-Resolution Framework for Scalable and Efficie...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Ali Albawi K. Pushpa Rani Susmita Kalyani Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making.

关键词： Accuracy Computational modeling Weapons Switches Big Data Data models Data mining Informatics Indexing Load modeling

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Advancing Image Segmentation, Analysis, and Feature Extraction Techniques

Advancing Image Segmentation, Analysis, and Feature Extracti...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Susmita Kalyani K. Pushpa Rani Ali Albawi Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study centers on review, feature extraction, and image segmentation, integrated through the Dual-Path Convolutional Neural Network (DP-CNN). The DP-CNN is designed to accurately identify objects in images, even in the presence of intricate lines and discontinuities. The MAMAM technique efficiently prioritizes critical image regions, expediting the review process and enhancing accuracy in image analysis. Additionally, feature extraction is refined using the Adaptive Feature Selection (AFS) method, facilitating precise and adaptable image processing. Each component of this approach is systematically developed to meet the research objectives. The combined methodology demonstrates robustness and versatility, proving effective across diverse and challenging data scenarios, thereby presenting a promising solution for rapid and accurate complex image processing tasks.

关键词： Image segmentation Image analysis Accuracy Reviews Feature extraction Solids Robustness Object recognition Convolutional neural networks Informatics

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Ai-Assisted Character Design in Medical Storytelling with Stable Diffusion

SSRN

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SSRN 2024年

作者： Mittenentzwei, Sarah Garrison, Laura A. Budich, Beatrice Lawonn, Kai Dockhorn, Alexander Preim, Bernhard Meuschke, Monique Department of Simulation and Graphics University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Informatics University of Bergen Thormøhlens gate 55 Bergen5020 Norway Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07743 Germany Institute of Artificial Intelligence Leibniz University Hannover30167 Germany

Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. In the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Stable Diffusion, a deep learning text-to-image model, is a crucial component of our pipeline for converting data into visual representations of characters. This reduces the time and artistic expertise required to create characters that accurately reflect the underlying data, even in time-oriented studies such as cohort studies. We evaluate the characters in a crowd-sourced study according to their authenticity to the underlying data, resemblance across different time steps, and compare static images with AI-driven animations. Study participants were able to recognize many aspects of the underlying data in our characters. Adjusting the weight and age of a character remains a challenge, as many facial features tend to change that make the character almost unrecognizable. While study participants preferred the static images, our evaluation shows the potential of animating the characters. © 2024, The Authors. All rights reserved.

关键词： Pipelines

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Secure Image Steganography Technique based on Hybrid Transforms and Enhanced AES

Secure Image Steganography Technique based on Hybrid Transfo...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Syed Basha Shaik D. Sudha Devi M. Sujithra Sarika G Vikas Tripathi Dhiraj Kapila Department of Computer Science and Engineering K L University Vijayawada India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Baselios Mathews II College of Engineering Sasthamkotta Kerala India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9781665489638

This, work uses a hybrid technique that combines the Modified Advanced Encryption Standard (MAES) and Elliptic Curve Cryptography to encrypt medical images (ECC). This article describes a modified AES for image encryption. For the global cryptosystem, a co-design approach is employed, with MAES processing on the NIOS ii processor and hardware-accelerated scalar ECC multiplication. The suggested cryptographic system executes in a significantly shorter amount of time, which is a critical consideration for real implementation. This security assessment was effective, and our trials reveal that our proposed technique simplifies and corrects cryptography's essentials. In actuality, the results of the evaluation reveal that the proposed method is efficient, rapid, and secure.

关键词： Steganography Embedded systems Correlation Transforms Elliptic curve cryptography Encryption Standards

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SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning

arXiv

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arXiv 2025年

作者： Ivanovska, Marija Todorov, Leon Damer, Naser Jain, Deepak Kumar Peer, Peter Štruc, Vitomir Faculty of Electrical Engineering University in Ljubljana Slovenia Faculty of Computer and Information Science University in Ljubljana Slovenia Dalian University of Technology China Fraunhofer Institute for Computer Graphics Research Germany

With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to sub-optimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://***/LeonTodorov/SelfMAD. © 2025, CC BY-NC-ND.

关键词： Self-supervised learning

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Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

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SN computer science 2020年第5期1卷 1-24页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Department of Informatics and Statistics (INE) Brazilian Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Santa Catarina Florianópolis Brazil

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle’s movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state;and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Driver behaviour Driving style Inertial sensors Intelligent transport systems Road conditions Road surface anomaly

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