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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Medical applications

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An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN computer Science 2024年第7期5卷 813页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

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Solarsense: Enhancing Energy Efficiency Through Iot-Enabled Solar Tracking

Solarsense: Enhancing Energy Efficiency Through Iot-Enabled ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Kaushalya Thopate Mayuri Gawade Surabhi Kakade Suvarna Bahir Mukund Kulkarni Krishna Nilesh Jaiswal Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Computer Engineering Trinity Academy of Engineering Pune India Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

The surging global demand for energy is primarily fueled by a burgeoning world population and elevated living standards. However, with traditional fossil fuels like oil dwindling in availability, there is an urgent need to shift towards renewable energy sources. Given the rapid progress in renewable energy technologies, a key objective of the project is to develop a solar energy tracking panel that can rotate for enhanced power generation. This innovative panel, capable of adjusting its position to optimize energy output by tracking the sun, proves invaluable in monitoring power consumption. The design of this rotatable solar energy tracking panel, incorporating four essential components (solar energy tracking panels, light-dependent resistors (LDRs), an Arduino, and a motor), is intricately explained in this study. This configuration not only serves as an advanced educational tool but also provides engineering technology students with a comprehensive understanding of alternative energy sources like solar power.

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A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: Integrating DGNN and Tyrannosaurus Optimization 4

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Agrawal, Anurag Vijay Anuradha, A. Patil, Kranti D. Patil, Vilaskumar Karthikumar, K. Tallapragada, S. V. V. Satyanarayana Bhagwant Institute of Technology Department of Electronics and Communication Engineering Uttar Pradesh Muzaffarnagar251315 India Sri Aditya Degree College Department of Computer Science Andhra Pradesh Bhimavaram534202 India Trinity Academy of Engineering Ghrcem Department of E & Tc Maharashtra Pune411048 India Sharnbasva University Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronic Engineering Avadi Tamil Nadu Chennai600062 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

One of the primary technologies necessary for the upcoming sixth generation of communication networks (6G) is massive multi-input multi-output, or MIMO. However, enormous MIMO presents a challenge in reducing energy use and the sum rate demand due to the power use of the several radio frequencies (RF) links in 6G technology. Therefore, this research presented a novel Deformable Graph Neural Network (DGNN) with Tyrannosaurus optimization for the optimal power distribution that maximized the sum rate and energy efficiency of the MIMO NOMA system. The adaptive mechanism of DGNN enables more accurate and efficient power allocation decisions. The integrated Tyrannosaurus optimization ensures the model converging on optimal power allocation strategies that maximize both sum rate and energy efficiency. The efficiency of the recommended model is compared in regarding sum rate and Energy Efficiency (EE) with the some of the recent approaches. The proposed method gained the greater sum rate and EE outperforming all other compared models. © 2024 IEEE.

关键词： Radio links

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IoT Based Smart Home with a Thief Detection and Tracking System

IoT Based Smart Home with a Thief Detection and Tracking Sys...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Amruta Surana Priyanka K. Kendre Jayashri D. Palkar Anusha O. Vaidya Chetana H. Jain N. Gopinath Computer Engineering Department Sinhgad Institute of Technology Lonavala India Department of Information Technology Trinity Academy of Engineering Pune India Department of Electronics and Telecommunications Trinity Academy of Engineering Pune India Department of CSE B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India

As automation technology advances exponentially, human-operated systems are progressively replaced by automated ones. Massive adoption and pervasive impact of the Internet of Things have drastically altered how humans relate to and comprehend the physical world. The exponential expansion of internet-enabled devices is incredible, from the smallest sensors to the largest industrial robotics. In recent years, “smart home” technologies have garnered considerable interest due to their potential to enhance people's daily lives. With the assistance of smart home technology, homeowners can swiftly and comfortably control the climate in their homes. Recently, increasingly sophisticated and inventive technologies have been developed to support “smart living” systems. The primary objective of this article is to use the Internet of Things to design a smart home automation system that enables remote monitoring and administration of a conventional home via an Android-based smartphone application and cutting-edge security technology. The proposed research uses cutting-edge technologies such as machine learning and the Internet of Things to locate and track intruders. It substantially increases safety when compared to more traditional methods.

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Prajakta Musale Balasaheb Jadhav Suvarna A Bahir Kaushalya Thopate Dipali Anarase Krishna Jaiswal Department of Engineering Sciences and Humanities Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Computer Engineering Trinity Academy of Engineering Department of Computer Engineering Vishwakarma Institute of Technology Pune India Wipro Technology

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command.

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Optimizing MEMS Optical Arrays with Silicon Micro Mirrors using Machine Learning Techniques

Optimizing MEMS Optical Arrays with Silicon Micro Mirrors us...

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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Vilas Ramrao Joshi Surender Kumar Sharma Vijay Agatrao Galande Pallavi Sachin Patil Ushadevi Patil Hussain Suttarwala Deepak K. Sharma Department of Computer Engineering Savitribai Phule Pune University Pune India Department of Electrical Engineering Poornima University Jaipur India Department of Engineering Science KJEI’s Trinity Academy of Engineering Pune India Department of Civil Engineering Genba Sopanrao Moze College of Engineering Pune India Senior Software Engineer American Express Phoenix AZ USA University of Technology Jaipur India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

In the rapidly evolving landscape of optical microsystems, the infusion of Machine Learning (ML) techniques stands out as a catalyst for unprecedented progress. This research paper delves into the intricate interplay between Micro-Electro-Mechanical Systems (MEMS), optical arrays, and silicon micro mirrors, shedding light on the transformative prowess unleashed by Machine Learning in optimizing these intricate components. Focusing on the limitations posed by conventional designs, the paper not only illuminates the challenges encountered but also presents a compelling narrative of how ML algorithms emerge as formidable allies, poised to surmount these hurdles with ingenuity and precision. The exploration extends beyond a mere technical discourse; it unfolds as a narrative of innovation, where the convergence of MEMS, optical arrays, and silicon micro mirrors, guided by the intelligence of Machine Learning, transcends traditional boundaries, propelling optical microsystems into an era of unparalleled efficiency and adaptability. This research beckons a new epoch, where the intricate dance of silicon micro mirrors and the intricate mechanics of MEMS find harmony through the symphony of Machine Learning, reshaping the landscape of optical microsystems and inspiring future possibilities that were once deemed beyond reach.

关键词： Micromechanical devices Integrated optics Technological innovation Machine learning Optical computing Optical fiber networks Silicon

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Deep Fake Detection for Preventing Audio and Video Frauds Using Advanced Deep Learning Techniques

Deep Fake Detection for Preventing Audio and Video Frauds Us...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Anusha O. Vaidya Monika Dangore Vishal Kisan Borate Nutan Raut Yogesh Kisan Mali Ashvini Chaudhari E & T C Department Trinity Academy of Engineering Pune India AIML Department G.H Raisoni Institute of Engineering and Management Computer Engineering Department Dr D Y Patil College of Engineering & Innovation Pune India AIML Department G.H Raisoni College of Engineering and Management NMIMS Center for Distance & Online Education Pune India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Deepfakes allow for the automated gen- eration of fake video content, often accomplished through the use of generative adversarial networks. To address the increasing issue of deepfakes, this study focuses on constructing a model that incorporates advanced techniques. The researchers combined the ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, selecting them based on their effectiveness in handling complex visual data and capturing temporal dependencies. Prior to detection, the dataset underwent pre-processing using the Multi-Task Cascaded Convolutional Neural Network (MTCNN), which facilitated the accurate extraction of facial regions. Importantly, the study evaluated the model across three diverse and significant datasets: the Face Forensics++ dataset (FF-DF), the Celeb-DF dataset, and the Facebook Deepfake Detection Challenge (DFDC) dataset. This comprehensive evaluation en- sured the model's ability to generalize and its suitability for real-world scenarios, as demonstrated by its exceptional detection accuracy. The combination of models employed in this study yielded highly accurate results and remained robust in the face of evolving deepfake technology.

关键词： Deep learning Deepfakes Visualization Accuracy Social networking (online) computer architecture Multitasking Convolutional neural networks Long short term memory Faces

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Early Detection of Breast Cancer Subtypes with Convolutional Neural Networks

Early Detection of Breast Cancer Subtypes with Convolutional...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Bhagyashree Swapnil Kadam Leelkanth Dewangan Prasanta Kumar Parida Tanushree Chaterjee Kirti Nahak Ebhad Swagat Sadashiv IT department Sinhgad Institute of Technology and Science Narhe Pune Dept of Computer Science & Engineering Faculty of Engineering & Technology (FEAT) Swangi Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardh Dept of Computer Science & Engineering KIIT University Bhubaneswar Odisha India Department of Biotechnology Raipur Institute Of Technology Raipur Dept of Computer Science & Engineering Kalinga University Raipur Chhattisgarh India Department of Engineering Science Trinity Academy of Engineering Kondhwa Pune

Breast cancer continues to be a major worldwide health issue, demanding accurate subtype categorization for personalized treatment approaches. In order to accurately identify subtypes, this work introduces a unique hybrid classifier that uses multi-modal data integration as well as cutting-edge machine learning algorithms. The model uses a Convolutional Neural Network (CNN) for image-based feature extraction alongside machine learning techniques for genomic and clinical features, combining mammographic, histopathological, genomic, and clinical data. The interpretative framework, which depends on an interpretivist philosophy, makes it less difficult to comprehend subtype discrimination on a more in-depth level. With an overall accuracy of 92.5% across HER2-positive, estrogen receptor-positive, and triple-negative subtypes, the results clearly demonstrate the hybrid classifier's better performance. The model's sensitivity and specificity ratings regularly outperformed those of existing techniques, demonstrating its aptitude for picking up on fine subtype distinctions. Particularly, the classifier demonstrated a low incidence of false positives, which is critical for reducing pointless interventions. Key genetic markers, physical traits, and hormone receptor status have been recognized as important discriminators employing feature importance analysis. These discoveries broaden the understanding of the biological foundations of each subtype. The study offers a useful tool for accurate breast cancer detection and treatment planning in clinical practice. The hybrid classifier's integrative methodology makes use of several data sources in order to provide a thorough understanding of breast cancer subtypes. This work lays the groundwork for future oncology and multi-modal data analysis studies, with potential implications in personalized therapy and other cancer types. There is still much more to discover about ethical issues, model generalization, and therapeutic use in th

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A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: Integrating DGNN and Tyrannosaurus Optimization

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Anurag Vijay Agrawal A. Anuradha Kranti D Patil Vilaskumar Patil K. Karthikumar S V V Satyanarayana Tallapragada Department of Electronics and Communication Engineering Bhagwant Institute of Technology Muzaffarnagar Uttar Pradesh India Department of Computer Science Sri Aditya Degree College Bhimavaram Andhra Pradesh India Department of E & TC Trinity Academy of Engineering GHRCEM Pune Maharashtra India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Kalaburagi Karnataka India Department of Electrical and Electronic Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

关键词： 6G mobile communication Radio frequency NOMA Adaptation models Simulation Energy efficiency Hybrid power systems Resource management Optimization Signal to noise ratio

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