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DeepFake Detection Through Key video Frame Extraction using GAN 1

DeepFake Detection Through Key Video Frame Extraction using ...

引用

1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRs 2022

作者： Lalitha, s. sooda, Kavitha Department of Computer Science & Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9781665460842

The emergence of deepfake videos in recent years has made image falsification a real threat. A deepfake video uses deep learning technology to substitute a person's face, emotion, or speech with the face, emotion, or speech of another person. Finding such deceptive deepfake videos on social media is the first step in preventing them. A robust neural network-based technique to identify false videos is presented in this paper. An important video frame extraction approach is used to speed up the process of finding deep fake videos. A model made up of a convolutional neural network (CNN) and a classifier network consisting of GAN technology is provided. Resnet, Resnext50 and LsTM were passed over in favor of the Confusion Matrix when deciding which structure to pair with the classifier while detecting the fake video. The model is a method for detecting visual artefacts. The subsequent classifier network uses the feature vectors from the CNN module as this is the input to categorize the video whether it is fake or real one. The dataset is considered from DeepFake Detection Challenge to get the best model. The key goal is to get high accuracy without using a lot of data to train the model. In comparison to earlier efforts, the key video frame extraction method dramatically decreases computations by achieving 97.2% accuracy using the Deepfake Detection Challenge dataset. © 2022 IEEE

关键词： Convolutional neural networks

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COvID-19 Prediction Using Time series Models 4

COVID-19 Prediction Using Time Series Models

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Deepthi, A.R. Indiramma, M. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Real-time data has evolved to become an integral part of understanding events across different timelines. Machine Learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COvID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the sARs-Cov-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and vARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COvID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and vARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using voila. © 2022 IEEE.

关键词： COvID-19

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Temporal face feature progression with cycle GAN 1

Temporal face feature progression with cycle GAN

引用

1st International Conference on Artificial Intelligence, Computational Electronics and Communication system, AICECs 2021

作者： Ashish, Roy Prasad, b.G. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru India

The aging process creates significant changes in the appearances of people's faces. When compared to other causes of variation in face imaging, aging-related variation has specific distinct properties. Facial Aging variations, for example, is unique for each person;it occurs gradually and is significantly influenced by other characteristics including health, gender, and life-style. As a result, the proposed effort will use Generative Adversarial Networks to address these critical concerns (GANs). Generative Adversarial Networks (GAN's) is made up of a generator and a discriminator network. The generator model generates images that a discriminator model analyses to determine if they are real or fake. This paper provides a Temporal Face Feature Progressive framework with Cycle GAN, which maintains the initial appearance and identity in the elderly aspect of their facial structure. To address aging concerns, our goal is to transform an initial age category image into a targeted age with age progression. We show that our temporal face features progressive cycle GAN learns and transfers facial traits from the source group to the targeted group by training various images. The IMDb-WIKI Face dataset has been used to obtain the results for the same. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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Machine Learning Models for sentiment Analysis 10

Machine Learning Models for Sentiment Analysis

引用

10th International Conference on Advanced Computing and Communication systems, ICACCs 2024

作者： Muralidhar, Amrutha Lakkanna, Yathindra B. M. S College of Engineering Department of Computer Science and Engineering Bengaluru India National Institute of Fashion Technology Department of Fashion and Lifestyle Accessory Design Bengaluru India

ISBN: (纸本)9798350384369

In the competitive landscape of online fashion retail, understanding and responding to customer sentiments is crucial for success. Our study addresses the challenge faced by retailers in effectively analyzing diverse customer feedback data to enhance their understanding of customer sentiments. We evaluate the performance of five machine learning models using techniques like word cloud examination, sentiment distribution assessment, and Latent Dirichlet Allocation. Our findings indicate that svM and Logistic Regression emerge as the top-performing models, with svM demonstrating superior accuracy and overall performance compared to the others. by using these methods, online stores may improve consumer satisfaction and obtain a competitive advantage in the market. This research contributes to broader industry advancements by providing clear guidance on leveraging machine learning for sentiment analysis in the online fashion retail sector. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Coverage Efficiency through Intelligent Agent Deployment in Large-scale IoT Environment 2

Enhancing Coverage Efficiency through Intelligent Agent Depl...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： soni, Nainsi Panchal, Rajan M. Kumar, saurabh The LNM Institute of Information Technology Department of Computer Science and Engineering Rajasthan Jaipur India S.P.B. Patel Engineering College Department of Information Technology Gujarat Mehsana India

ISBN: (纸本)9798350371314

The demand for efficient coverage in large-scale environments has pushed up the development of Internet of Things technologies. Effective deployment is more than plainly physically deploying devices. It necessitates a thorough awareness of the environment, which includes determining the best placements for both devices and their connectivity. This study examines the use of intelligent agents to enhance coverage efficiency over broad landscapes by autonomously regulating coverage gaps. A strategic framework is presented in this paper for optimizing connectivity and reachability across vast regions by deploying intelligent agents, and this study focuses on attaining good coverage with an optimum number of agents, even in harsh situations. The experiments with different scenarios demonstrate the potential outcomes and efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Internet of things

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Image Processing Techniques for Detecting steel Defects with MATLAb and Machine Learning 1

Image Processing Techniques for Detecting Steel Defects with...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, v. Mary, P. Arockia Ramanan, s.v. Kumar, N.M.G. Kumar, Pullela svvsr Abdullah, Ibrahim Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350367720

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAb. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects' recognition compared to other techniques. Thus, the' findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production. © 2024 IEEE.

关键词： Image enhancement

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Explainable AI for Crop disease detection 4

Explainable AI for Crop disease detection

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Rakesh, s. Indiramma, M. B.M.S College of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9781665474368

Agriculture is a major contributor to the Nation's economy since it is the most important component. It is common for plants to suffer from various kinds of diseases, which may be influenced by extreme climatic conditions and pests. This further lowers the quality of the harvest. In order to calculate the overall crop yield and productivity, early detection and accurate diagnosis of plant diseases are critical, which can significantly increase productivity and yield. Farmers have difficulty identifying diseases in plant leaves. Diagnosing diseases through traditional methods requires extensive field experience & expertise. The advancement of technology has allowed numerous strategies to be developed to identify plant diseases using Artificial Intelligence and Deep Learning. To detect and classify various crop diseases, we propose two models, Inception v3 and ResNet-9, which are deployed on Plant village Dataset and New Plant Disease Dataset. In addition to Deep Learning models, Explainable AI (XAI) tools such as LIME and Grad-CAM have been utilized for understanding the black-box nature of Deep Learning models. © 2022 IEEE.

关键词： Lime

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EEG signal processing and identification of P300 signals using deep learning 4

EEG Signal processing and identification of P300 signals usi...

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Mamtha, K. Indiramma, M. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

An Electroencephalography (EEG)-based brain-computer interface (bCI) is a system that helps in the process of direct communication between humans and computers using brain activity or biosignals. With the help of bCI, it is possible to notice events related to cognitive, motor, and sensory activities particular to that event, and this response is seen as a P300 peak in EEG signals. This event-related potential of the EEG signals is usually used to build speller systems. The input of commands or texts to a computer by thought can be done with the help of a communication tool P300 speller. Therefore, to build a faster speller system, fundamentally a correct identification and classification of P300 responses are required. The main objective of this paper is to explore the use of the deep learning models and to identify the P300 waves which help in visualizing the P300 signals from the given input EEG signals. © 2022 IEEE.

关键词： brain computer interface

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Applying a Hybrid Deep Learning Framework to Efficient stock Price Prediction 4

Applying a Hybrid Deep Learning Framework to Efficient Stock...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Khatri, Eti Ramesh babu, Ch. Nandhini, I. Reddy, sowmya Kethi Yoges, G. Reddy, Pundru Chandra shaker Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India Department of Computer Science and Engineering Hyderabad India V.S.B.Engineering College Department of Information Technology Karur India School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad India Maharaja Engineering College Avinashi India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350352931

It is very difficult to make reliable financial predictions using stock market data due to its inherent volatility and non-linearity, especially in the period following a pandemic. In response to these difficulties, this study builds a hybrid framework using deep learning to forecast technology stock prices, financial trends, and risk quantification. An increasing number of individuals have started investing in the stock market due to the fast growth of the economy in the past few years. A novel stock market prediction based on deep learning (sMP-DL) is presented in this research as a method for predicting stock market values. This study forecasted the stock market closing price using a bidirectional gated recurrent unit (biGRU) in conjunction with long short-term memory (LsTM). When tested against other systems currently in use, the results demonstrated that the suggested system performed admirably. since the values of R2, RMsE, MDA, MAE, and 0.8, respectively, are 3.4, 6.7, and 3.8. © 2024 IEEE.

关键词： Long short-term memory

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barcode Recognition using sequential and Parallel Approach

Barcode Recognition using Sequential and Parallel Approach

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOvA 2024

作者： Kusur, Chidanand s. shirdhonkar, M.s. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G.H. College of Engineering and Technology Karnataka Vijayapur586103 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9798331505134

In a computer vision and image processing, the barcode detection and recognition systems play very important role for the interpretation of the encoded information for the system automation and inventory management. Hence, we proposed an enhanced approach to barcode recognition from video frames by extending our previous work focused on barcode detection in static images i.e. for the detection and recognition of barcodes from the videos of racks of books. The proposed system showed that, the parallel approach is time efficient in comparison with sequential approach. © 2024 IEEE.

关键词： Inventory control

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