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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Boovaneswari, S. Palanivel, N. Nihilrs, Sri Madhavan, R. Daniyel, A. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350359688

A method called deepfake produces fake video and films with artificial or substituted faces. Deepfakes are turning into a worrying societal phenomenon because they may be used maliciously to spread harmful information, fabricate electronic convincing proof, make fake political news, and even participate in online harassment and fraud. These masks make it harder to distinguish facial features, making it more difficult to detect fake video. To detect the fake morphed video, we are using the deepfake algorithm to detect the morphed face in the video. Deepfake technology is not using to create the morphed video and also it is used to detect the video by using the *** a result, more sophisticated deepfake detection technology is required. Because the field does not yet have the necessary dataset for detection-model training, the research generates a real or fake video dataset with face masks The study attempts to improve accuracy by improving CNN topologies, detecting important visual signals, and comparing model performance against various morphing strategies. Real-world application of the effect of dataset size and diversity on training is investigated. By protecting multimedia material against changing digital tampering, this research helps combat the threat posed by deepfake technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploration of Quantum Computing in Multi-Die FPGA Routing Problems 9

Exploration of Quantum Computing in Multi-Die FPGA Routing P...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Tian, Haochang Zhang, Xu Wang, Chengkai Ji, Weiqing Kou, Mingyang Yao, Hailong School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798331509453

This paper delves into the innovative application of quantum computing to solve complex multi-die FPGA (FieldProgrammable Gate Array) routing challenges. Leveraging the Quantum Approximate Optimization Algorithm (QAOA), we propose a novel approach that integrates classical routing techniques with quantum computing methodologies. This integration specifically utilizes QAOA to enhance the efficiency and effectiveness of the routing process. This hybrid approach partitions the routing task into intra-FPGA and inter-FPGA components, optimizing each segment through the appropriate computational techniques. Extensive experiments conducted on quantum computing simulators, alongside trials on actual quantum hardware, demonstrate the theoretical potential of quantum computing in enhancing the efficiency of multi-die FPGA routing solutions. Our findings suggest that despite the current limitations of quantum hardware, such as noise and error rates, quantum computing holds promise for advancing EDA methodologies. This paper contributes to the field by pioneering the integration of quantum algorithms with EDA techniques, offering a novel perspective on future applications of quantum computing in complex routing problems. © 2024 IEEE.

关键词： Quantum computers

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Optimizing classifier performance using PCA-FLANN: a fast and reliable approach

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Iran Journal of computer science 2025年 1-14页

作者： Jena, Monalisa Behera, Ranjan Kumar Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science Yonsei University Seoul 03722 South Korea Department of Computer Science & Engineering Indian Institute of Information Technology Jharkhand Ranchi India Department of Computer Science Fakir Mohan University Odisha Balasore India

This work introduces PCA-FLANN, an innovative hybrid model combining principal component analysis (PCA) with functional link artificial neural network (FLANN) to achieve efficient non-linear dimensionality reduction and robust classification. The approach is motivated by the need to optimize feature selection, recognizing that not all features equally contribute to achieving optimal accuracy. PCA is employed to extract the most relevant features from datasets, retaining essential information while significantly reducing dimensionality, and FLANN processes these reduced features to handle non-linear correlations effectively. To assess its performance, PCA-FLANN is evaluated on six widely-used real-world datasets from the KEEL repository and compared against hybrid models such as PCA-RBFN, PCA-SVM, PCA-RF, PCA-MLP, and standalone FLANN. The results reveal that PCA-FLANN outperforms these models in accuracy, with improvements of 8.5%, 2.94%, 9.09%, 11.1%, and 9.69%, respectively. Additionally, the model demonstrates enhanced computational efficiency and reduced complexity, establishing PCA-FLANN as a powerful and precise solution for classification tasks involving dimensionality reduction. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Classification Feature extraction FLANN Neural network PCA

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Detection and Segregation of Plastic Waste using a Robotic Arm 15

Detection and Segregation of Plastic Waste using a Robotic A...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kangabam, Bikash Huidrom, Bhupendra Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim India

ISBN: (纸本)9798331300579

Plastic waste pollution has become one of the most significant environmental concerns in recent years. The accumulation of plastic waste has been observed in various locations worldwide, leading to environmental damage and health hazards. In this paper, a system is proposed for the detection and segregation of plastic waste using a Robotic arm manipulator based on YOLOv5 and Raspberry Pi. YOLOv5 is an advanced object detection algorithm that can identify plastic waste in images. The Raspberry Pi is a low-cost, small-sized computer that can be used as the central processing unit for the proposed system. The system includes a camera module that captures images of the waste material in real time, and an ultrasonic sensor to measure distance of the object. It detects and identifies the plastic waste in the image. Then, using the 3-D coordinate values calculated from the data gathered from the camera and ultrasonic sensor, plastic waste is separated using a robotic arm by applying inverse kinematics, controlled by the Raspberry Pi. The proposed system's performance was evaluated using various custom datasets, and the results show that it can effectively detect and segregate plastic waste from other types of waste materials. © Grenze Scientific Society, 2024.

关键词： Inverse kinematics

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Deep Neural Network Based on Sparse Auto-Encoder for Road Extraction 19th

Deep Neural Network Based on Sparse Auto-Encoder for Road Ex...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liu, Sheng Chang, Shuxiao Cao, Ting Li, Xinyue Department of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9783031651250

Road extraction from aerial image has realistic significance for GIS data updating. In view of the complexity challenging for acquiring road information, this paper proposes supervised model that combines Convolutional Neural Network (CNN) with Sparse Auto-Encoder (SAE) to cope with the road extraction task. First, the road features are extracted from the amount of non-annotated data using SAE model that aim to train the road features using CNN principle with implementing convolution and pooling to reduce model complexity. Second, the encoder network completes the operation, and after the deep pooling and deconvolution operations, the intermediate features are extracted by the decoder network and sampled back to the input image of the same size on the map. Third, the soft-max classifier categorizes images into roads and non-roads. Finally, the experiments verify that the proposed method outperforms the traditional methods and could achieve the satisfy result. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolutional neural networks

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Machine Learning-Powered Traffic Sign Tracking: A Step-by-Step Framework for Recognition 2

Machine Learning-Powered Traffic Sign Tracking: A Step-by-St...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Rkhi Krishna, C.R. Pallavi, N.R. Computer Science Engineering Department BGS Institute of Technology Adichunchanagiri University India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the biggest challenges in machine vision is finding and identifying items in natural scenes. When real-time processing capabilities are needed, this difficulty is exacerbated. Intelligent transport systems rely heavily on identifying and detecting traffic signs through video technology. While a great deal of research has shown that several state-of-the-art approaches' "most notably, deep learning methods' "perform admirably on public datasets, these methods have significant processing needs. In contrast to previous studies, we provide a computational framework based on machine learning for the detection, tracking, and recognition of traffic signs using video captured from a moving vehicle for Indian Traffic Sign boards. The main advantages of our suggested system are: 1. Improving traffic sign detection performance in challenging lighting scenarios. 2. Using content analysis and important information recognition, a region of interest (ROI) extraction approach was created to improve the accuracy of visual inspection using YOLO. 3. The DNN method is used to enhance tracking performance, which includes the traffic lights, and produce a more stable classification result. The model underwent testing using various epoch values, revealing an accuracy increase to 96.8% after 150 epochs. Additionally, a comparison was made with input based on images, yielding an accuracy of 95.4% also after 150 epochs. © 2024 IEEE.

关键词： Traffic signs

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Evaluation of Classifiers Based on Neural Network for Handwritten Digit Recognition

Evaluation of Classifiers Based on Neural Network for Handwr...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Sehta, Neha Saraf, Krupi Vinchi, Aditya Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Handwritten Digit Recognition (HDR) refers to the computer' s ability to identify and categorize hand written digits from various sources into 10 predetermined categories, ranging from 0 to 9. The issue of digit recognition is significant and crucial. Due to variations in size, thickness, location, and orientation of manually written digits, several challenges need to be addressed in order to evaluate the problem of recognizing handwritten digits. The diversity and variety in the composition techniques of different persons also impact the pattern and appearance of the digits. It refers to the approach of recognizing and organizing written numbers. It offers a diverse array of uses, such as automated bank checks, postal addresses, and tax paperwork, among others. The objective of this research is to employ a classification system to accurately identify handwritten numerals. The consequences of the most widely employed deep learning algorithms, such as multilayered perceptron network, in conjunction with convolution neural networks, are being discussed. Comparison of five neural network classifier shows that accuracy up to 99% can be achieved only in few epochs of training with default configuration of hyper-parameters. © 2024 IEEE.

关键词： Deep learning

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Targeted Influence Maximization Using Centrality and Diminishing Weight

Targeted Influence Maximization Using Centrality and Diminis...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Baruah, Khanjan Changmai Tripathi, Rohit Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350350821

Social Networks are an integral part of our daily life. These networks are tools for sharing information among different users. In such a network, the most influential users become the source for disseminating information over the network. Hence, social network analysis was done to understand and identify the importance of a node in information diffusion. These influential nodes help to achieve maximum influence throughout the network. However, in certain cases, only a subset of nodes are needed to be influenced. This is called targeted influence maximization. In this paper, we use different centrality measures and diminishing weights to identify influential nodes and study the influence of these nodes on the target nodes. Our simulation shows that the seed selection scheme using centrality and diminishing weights activates more target nodes in a network than the existing schemes. © 2024 IEEE.

关键词： Centrality Measures Independent Cascade Model Social Network Analysis Target Influence Maximization

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Identification and Analysis of Neural Disorders Based on Hyperactivity and Spectrum Disorder with Functional MRI 8th

Identification and Analysis of Neural Disorders Based on Hyp...

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Kodukula, Sai Siddharth Varanasi, Usha Bala Boddikura, Soumya Talluri, Pavani Kalepu, Bala Sai Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India

ISBN: (纸本)9783031511660

A collection of conditions known as neurodevelopmental disorders affect a number of factors, including mood, learning capacity, self-control, and memory. The most frequent and prominent are attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The current diagnosis methods are time-consuming, human-mediated, and unreliable. This study focuses on the prediction of ADHD and ASD, and a comparative analysis is done using neural networks, support vector machine, gradient boosting, and logistic regression models to determine which performs well for the resting-state functional MRI (fMRI) data. During this procedure, time-series signals were collected from the brain region’s voxels and utilized to create functional connectivity characteristics. Our models were evaluated on various metrics, and we observed that a multi-layer perceptron model performs well. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Brain

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IMMULINK-Blockchain assurance in vaccine cold chain 4

IMMULINK-Blockchain assurance in vaccine cold chain

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Parthiban, M. Charu Dharshana, R. Sandhya, K.B. Goswami, Swathi Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

The pharmaceutical industry's supply chain management faces critical challenges, including tampering of vaccine details and a lack of transparency from regulatory authorities to end customers. These issues compromise the safety, traceability, and dependability of vaccine distribution. To address these challenges, we propose a comprehensive framework leveraging blockchain technology, smart contracts, and IoT sensors [12]. This solution integrates IoT sensors with smart contracts to monitor the cold chain process, ensuring vaccines are stored at proper temperatures and conditions. This integration with the Ethereum blockchain, utilizing the Solidity programming language, creates an immutable, transparent, and secure supply chain. We deployed our framework on Remix IDE, demonstrating its functionality with multiple nodes. The proposed system not only enhances the supply chain's dependability and traceability but also includes a feedback mechanism from customers, further improving the process's overall *** testing and deployment have shown promising results in improving the overall performance, security, and efficiency of the supply chain. This framework offers a robust solution for maintaining vaccine integrity and building trust among stakeholders in the pharmaceutical supply chain. © 2024 IEEE.

关键词： Efficiency

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