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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Anas Bhavye Gupta Chirag Mishra Puneet Garg Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

Pneumonia is recognized as a highly infectious disease, which spreads rapidly among infants. According to UNICEF, 16% of all infant fatalities under the age of 5 were attributed to pneumonia in 2016. Due to less number and availability of doctors in India makes it a more vulnerable disease. The ultimate motive of this research paper is to utilize a Chest X-ray image to assess whether that individual has symptoms of pneumonia or normal. The reason why Convolution Neural Network (CNN) is utilized in this project is because of its robust processing abilities that make it well-suited for both image classification and image processing job. Several researchers in this wide domain use CNN, which is a quick and well-liked image processing and classification technology. This model will help in analyzing whether the person has pneumonia or not. In this model, we must provide the X-Ray images and it will predict whether he/she is normal or has pneumonia. It will reduce the dependency on the medical staff and results will be produced quickly. Furthermore, it can generate more accurate outcomes than the human eye, which may miss small details in an X-ray. The Chest X-Rays Images have been taken from Kaggle because it has a large dataset and is being divided into two types i.e., Pneumonia and Normal. The dataset comprises over 17,000 Chest X-rays depicting both healthy and pneumonia-infected lungs. The overall accuracy of this model is 88.62%

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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Investigating Blockchain Security Mechanisms for Tamper-Proof data Storage 2

Investigating Blockchain Security Mechanisms for Tamper-Proo...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thilakavathy, P. Jayachitra, S. Aeron, Anurag Kumar, Narendra Ali, Syed Sumera Malathy, M. Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India School of Computing Dit University Dehradun Computer Science and Engineering India Amity University Department of Computer Science and Engineering Jharkhand Ranchi India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350369960

Across a wide range of sectors, blockchain technology has emerged as a game-changing option for secure data storage. This research dives into the mechanisms that enable blockchain to protect data integrity and immutability. This research investigates how blockchain technology reduces the chances of data tampering and unauthorized adjustments by examining its underlying concepts and features, such as decentralization, cryptography, consensus procedures, and immutability. Blockchain networks are decentralized, so they don't rely on any one entity to function. This eliminates one potential point of failure or manipulation. data is protected from tampering and disclosure thanks to the use of hash functions and digital signatures, two examples of strong cryptographic algorithms. By confirming and agreeing on transactions, consensus methods like Proof of Work (PoW) and Proof of Stake (PoS) keep the blockchain ledger up-to-date and accurate. By linking blocks together and recording timestamps, blockchain makes it practically impossible to alter data after it has been recorded. The distributed ledger's availability to the public and ease of use encourage accountability and deter dishonest participants. Smart contracts eliminate the need for third parties by automating operations based on predetermined rules that cannot be altered after they have been created. This study also delves into blockchain fork resistance, incentives for truthful involvement, and other network security measures. It also covers the security implications of both permissioned and permissionless blockchains. Blockchain technology provides strong security procedures, but it has several drawbacks that must be taken into account. These include issues with scalability and the possibility of security flaws in smart contracts. Therefore, securing security and performance requires cautiously considering particular use case needs and picking the right blockchain platform and configuration. For blockchain networ

关键词： Blockchain Consensus Algorithms Cryptography Immutable Ledger

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Intelligent Wearable Device for Visually Impaired Person to act as a Third Eye 4

Intelligent Wearable Device for Visually Impaired Person to ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Saranya, G. Tharun, S.V. Tamilvizhi, T. Surendran, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300093

People with limited vision, impaired sight, or visual impairment cannot see or recognize people, objects, words, or letters. Offer visually challenged people a camera-based detection system so they can read names of trained people, products, objects, and texts. With the aid of facial recognition technology, the suggested system will enable them to recognize a person and some of the items in front of them, including (Bottle, chair, person, cat, dog, plant). The printed text on books, magazines, and other printed materials can be read by employing optical character recognition technology. The proposed technology serves as a substitute for an artificial eye for persons who are visually impaired. It doesn't require any oversight from people. © 2023 IEEE.

关键词： Optical character recognition

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Design and construction of a portable e-nose system for human exhaled breath VOC analysis

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Materials Today: Proceedings 2022年 58卷 422-427页

作者： Binson, V.A. Akbar, Rizwana Thankachan, Nisha Thomas, Sania Department of Electronics Engineering Saintgits College of Engineering Kerala Kottayam686532 India Department of Computer Science & Engineering Saintgits College of Engineering Kerala Kottayam686532 India

This paper describes the design and fabrication of a system that can analyze the human exhaled breath volatile organic compounds (VOCs). The expelled breath contains several VOCs in addition to nitrogen, oxygen, carbon dioxide, and water vapor. Some of these VOCs are biomarkers for certain lung diseases like lung cancer, cystic fibrosis, chronic obstructive pulmonary disease (COPD), etc, and they can even identify smokers and non-smokers by variations of VOCs. The development of the electronic nose (e-nose) with five metal oxide semiconductor (MOS) gas sensors, control circuit, and processing circuit is explained in this paper. The system is tested and verified in the detection of breath VOCs. The clinical feasibility of the system is checked in a small pilot study involving 4 lung cancer patients, 4 chronic obstructive pulmonary disease patients, 4 asthma patients, and 4 healthy controls including smokers and non-smokers. The results of the study are also included, along with the conclusions drawn from the work. The e-nose system developed with TGS gas sensors is economical and uses a non-invasive method of breath sampling, and surely it will be a diagnostic tool for the detection of lung cancer, COPD, and other major respiratory diseases in near future. © 2022

关键词： Volatile organic compounds

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Decentralised Society Using Soulbound Tokens 3

Decentralised Society Using Soulbound Tokens

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Tejashwin, U. Kennith, S.J. Manivel, Rohit Devi, M. Thanga Subha Nirmala, M. New Horizon College of Engineering Department of Computer Science Bengaluru India

ISBN: (纸本)9798350302288

Today's world revolves around Web 3.0 technology to form a decentralized network as it addresses issues such as privacy, connection, and security that were at stake in Web 2.0. This paper proposes a system that can be used by Educational Institutions, Students, and Recruiting Organizations to provide an environment that can store and verify the credibility of students. The proposed system is built using the concept of Web 3.0 and utilizes technologies such as Soul Bound Tokens(SBTs) that underlie Block Chain technology. On successful implementation of the system, the results observed are;it provides a secure environment to store the certifications or data of students that cannot be forged, reproduced, or removed from the chain, also addresses concerns regarding the privacy of crucial data by proposing the off-chain data storing method which shall enhance the provenance of the student's data. Finally, this system emphasizes building an environment that stores students' data and processes them in a decentralized manner which shall resolve the concerns of Web 2.0. © 2023 IEEE.

关键词： Students

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Towards the Implementation of Traffic engineering in SDN: A Practical Approach 1

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Prabu, U. Geetha, V. Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India Department of Information Technology Pondicherry Engineering College Puducherry India

ISBN: (数字)9789819916245

ISBN: (纸本)9789819916238

The advancement of software-defined networks (SDNs) plays a major role in the next generation of networks. It has laid its root in the cloud, data center, and the Internet of things. SDN separates the data and control plane. Through this, it provides greater flexibility, cost-effective configuration, and improved functioning of advanced design of networks. Traffic engineering (TE) is one of the major strengths of software-defined networks. Its main goal is to manage the flows in the network, update topologies, analyze the traffic, and be fault tolerant. Apart from that, it performs load balancing, congestion control, traffic rerouting, and updating policies. This article is intended to provide a detailed description of the types of TE techniques, formulation of TE problems, different ways to implement TE in SDN, and different topologies to be considered for the performance evaluation of the algorithms. The overview of the above-mentioned things might be beneficial for beginners and future researchers to have a clear understanding and go ahead with the implementation of TE in SDN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges

AI Sovereignty in Autonomous Driving: Exploring Needs and Po...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Sinha, Koesha Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Department of Statistics and Data Science Bangalore India Techno International New Town Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350314144

With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. © 2023 IEEE.

关键词： Autonomous vehicles

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Discovery of Unknown Classes in Constrained Semi-Supervised Learning 2

Discovery of Unknown Classes in Constrained Semi-Supervised ...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Roopa, S. Nithya Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

Supervised classification models have proved their significance in research and are utilized in many key areas. However, there are some limitations for these methods. In supervised learning, adequate labelled training data are required for each class. The learned classifier can classify only the data belonging to classes for which training data is available and lacks the ability to handle the data of unseen classes. However, in real time scenarios, there may not be adequate training data for all the classes. There could also be scenarios where the classes not available in the training data appearing in the testing data. The learner is expected to have the capability to uncover the class labels for the data belonging to unseen classes. To handle this situation, methods under different learning paradigms are proposed. In this work, various semi supervised learning techniques that can handle hidden classes(clusters) are evolved. Variant of Semi Supervised EM Based-K means and Naïve Bayes are discussed. © 2023 IEEE.

关键词： Classification (of information)

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