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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Evaluation of convolutional neural network models’ performance for estimating mango crop yield

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第1期9卷 1-18页

作者： Neethi, M.V. Raviraj, P. Department of Computer Science and Engineering-Data Science ATME College of Engineering Affiliated to Visvesvaraya Technological University (VTU) Belagavi India Department of Computer Science and Engineering GSSS Institute of Engineering and Technology for Women Affiliated to Visvesvaraya Technological University (VTU) Belagavi India

In agriculture, crop yield estimation is essential;producers, industrialists, and consumers all benefit from knowing the early yield. Manual mango counting typically involves the utilization of human labor. Experts visually examine each sample to complete the process, which is time-consuming, difficult, and lacks precision. For commercial mango production to produce high-quality fruits from the orchard to the consumer, a quick, non-destructive, and accurate variety classification is required. Because of its effectiveness in computer vision, a convolutional neural network—one of the deep learning techniques—was chosen for this investigation. For yield prediction, a total of eight popular mango cultivars were utilized. A comparison with previously trained models was used to assess the proposed model. The performance of the classifiers was evaluated using evaluation metrics such as accuracy, loss, area under the receiver operating characteristic curve score, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen’s Kappa performance measure. In terms of performance evaluation criteria, it was found that the proposed approach outperformed the pre-trained models. The suggested model achieved 98.85% accuracy in the test set, which had 800 images. This outcome demonstrates the tangible applicability of the proposed methodology for mango crop estimation. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Networks Deep Learning Early Detection Machine Learning

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Enhanced Predictive Modeling for Myocardial Infarction Risk Assessment through Multimodal Data Integration 3

Enhanced Predictive Modeling for Myocardial Infarction Risk ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Abdulkadar, S. Dharun, S. Kirthivashan, V. Janani, B. K.S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331509675

Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that makes use of multimodal data, including clinical, genetic, imaging, and lifestyle information. By utilizing sophisticated machine learning methods, specifically the XGBClassifier, the model combines various datasets to reveal intricate patterns, attaining exceptional sensitivity and specificity. Robust reliability testing, a comparison with current approaches, and the use of interpretability tools such as SHAP guarantee that the suggested model provides useful information for clinical decision-making. Addressing dataset biases and extending feature integration to improve generalizability are two potential future steps. © 2025 IEEE.

关键词： AI Model and Accuracy Myocardial infarction streamlit metrics Train XGBClassifier

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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Secure and Efficient Data Synchronization Techniques for Digital Twins in Fog-Edge Cloud Environments 7

Secure and Efficient Data Synchronization Techniques for Dig...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Hayvita, R.K. Nishath, A.G. Ramalakshmi, S. Krishnan, R. Santhana Essaki Muthu, A. Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

Data synchronization is a critical aspect in the deployment of Digital Twins within Fog-Edge Cloud environments, ensuring consistency and reliability across distributed systems. However, the security and efficiency of synchronization techniques remain significant challenges. The paper presents a novel algorithm named TwinCrypt, which is designed based on the combination of appropriate encryption algorithm called as Homomorphic Encryption and suitable computing mechanism scheme called as Secure Multi-Party Computation. The algorithm is aimed to achieve secure and efficient data synchronization for Digital Twins in Fog-Edge Cloud environments. The proposed algorithm aims to address vulnerabilities associated with data transmission and storage, offering robust protection against unauthorized access and data breaches. The performance effectiveness of TwinCrypt is analysed by conducting suitable simulations and the simulation results are compared with are conducted comparing it with the existing algorithms used for data synchronization viz. RSA Encryption, AES Encryption, and Zero-Knowledge Proofs in similar environments. Simulation metrics such as latency, throughput, and security overhead are employed to assess the performance of TwinCrypt against its counterparts. The results establish that TwinCrypt outperforms existing algorithms in both security and efficiency, showcasing its suitability for real- world deployment scenarios. © 2024 IEEE.

关键词： Fog computing

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Image Denoising Using AI with Entropy as Metric Analysis 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Venkateshwara Reddy, H. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

This paper introduces a novel denoising approach Image denoising is one of the fundamental challenges in the field of image processing and computer vision. Our main aim of the project is to get a complete noiseless image with high accuracy and less time. So, in our project we are proposing an effective denoising technique using RNN (Recurrent neural network) for fixed pattern noisy images which may reduce the usage of number of auto encoders. Here, we are passing images into the recurrent neural networks as pixel information in the form of a 3D coordinate system. RNN doesn’t migrate the information from one node to another node until it gets its basic requirements. As we are using a single auto encoder, it will reduce noise as well as time complexity. The statistical analysis is going to be observed by using the following metric considerations, namely SNR (Signal to noise ratio), PSNR (Peak signal to noise ratio), MSE (Mean square error) and Entropy. From this research work we are going to get a complete noiseless image. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Recurrent neural networks

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Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution 31st

Pubic Symphysis-Fetal Head Segmentation Network Using BiForm...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Cai, Pengzhou Jiang, Lu Li, Yanxin Liu, Xiaojuan Lan, Libin College of Computer Science and Engineering Chongqing University of Technology Chongqing China College of Artificial Intelligence Chongqing University of Technology Chongqing China

ISBN: (纸本)9789819620630

Pubic symphysis-fetal head segmentation in transperineal ultrasound images plays a critical role for the assessment of fetal head descent and progression. Existing transformer segmentation methods based on sparse attention mechanism use handcrafted static patterns, which leads to great differences in terms of segmentation performance on specific datasets. To address this issue, we introduce a dynamic, query-aware sparse attention mechanism for ultrasound image segmentation. Specifically, we propose a novel method, named BRAU-Net to solve the pubic symphysis-fetal head segmentation task in this paper. The method adopts a U-Net-like encoder-decoder architecture with bi-level routing attention and skip connections, which effectively learns local-global semantic information. In addition, we propose an inverted bottleneck patch expanding (IBPE) module to reduce information loss while performing up-sampling operations. The proposed BRAU-Net is evaluated on FH-PS-AoP and HC18 datasets. The results demonstrate that our method could achieve excellent segmentation results. The code is available on GitHub. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic imaging

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Real-Time Soil Moisture Sensing Using Arduino for Automated Plant Irrigation System 7th

Real-Time Soil Moisture Sensing Using Arduino for Automated ...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Vijayalakshmi, R. Deepalakshmi, R. Madhavan, T.S. Siva Balasubramanian, G. Department of Computer Science and Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai India Velammal College of Engineering and Technology Tamil Nadu Madurai India

ISBN: (纸本)9783031699856

Irrigation constitutes a fundamental element of agriculture and food production. Outdated methods in both developing and underdeveloped nations result in significant water wastage during this crucial process. This project proposes an inventive method for automating the control of water release valves in micro irrigation networks with prediction of required humidity by weather condition using artificial intelligence (AI). This system seeks to close the gap between water supply and crop demand while also optimizing resource use and reducing environmental impact by using AI-powered soil moisture monitoring and decision-making processes. This project address the key factors like varying demand of water for crops based upon the growth stage, weather conditions and plant type. This reducing the usage by adopting the method of micro irrigation which reduces the usage of water by 70–80% than conventional methods of irrigation. This project also provides a user-friendly interface to efficiently control the watering of plants when needed by the user. © IFIP International Federation for Information Processing 2024

关键词： Decision making

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A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

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computers, Materials & Continua 2023年第2期74卷 3711-3726页

作者： Sugandhi Midha Sahil Verma Kavita Mohit Mittal Nz Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Chandigarh UniversityMohali140413India Chandigarh Engineering College Jhanjeri Mohali140307India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia FITTaylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the *** and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application *** integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care *** has improved the real-time monitoring of patients by medical ***’data get stored at the central server on the cloud from where it is available to medical practitioners in no *** centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’*** recent days,several schemes have been proposed to ensure the safety of patients’*** most of the techniques still lack the practical implementation and safety of *** this paper,a secure multi-factor authentication protocol using a hash function has been ***(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient *** Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for *** results prove that the proposed scheme ensures secure access to the database in terms of spoofing and *** comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

关键词： Multi-factor authentication hash function BAN logic SPANAVISPA

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A Comparative Study on Nature Inspired Optimization Algorithms to Solve Real World Problems- A Survey 5

A Comparative Study on Nature Inspired Optimization Algorith...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bharathi, A. Ponnila, P. Daniel Madan Raja, S. Karthiga, M. Kcg College of Technology Computer Science & Engineering Chennai India Bannari Amman Institute of Technology Information Technology Erode Sathyamangalam India Karunya Institute of Technology and Sciences School of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Computer Science and Engineering Erode Sathyamangalam India

ISBN: (纸本)9798331504403

Optimization plays a vital role in science and engineering. Nonlinear problems demand optimized solutions, traditional Algorithms fail to solvethese kinds of problems. A Recent trend is to use Nature Inspired optimization Algorithms due to their adaptability and effectiveness. Nature itself gives many optimized solutions for solving real world problems. The main objective of using optimization Algorithms is to minimize the cost, energy consumption and maximize the effectiveness, maintenance and execution. This paper reviews the various nature Inspired optimization Algorithms used to solve optimization problems. © 2024 IEEE.

关键词： Optimization algorithms

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