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Detection of Real and Manipulated Videos using the Transfer Learning Approach of Deep-Learning Models 5

Detection of Real and Manipulated Videos using the Transfer ...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Reddy, Bandi Rupendra Rup, Daka Chandra Rohith, Mathi Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Deepfake (manipulated images and videos) identification is critical in multimedia forensics, as it can spread misinformation, threaten privacy, and have legal implications. To address this problem, researchers and practitioners are developing deepfake detection techniques that use deep-learning models and computer vision algorithms to analyse the authenticity of images and videos. This study proposes a novel approach providing robust and scalable deepfake detection by using transfer learning in feature extraction and deep-learning models to train features extracted from the video dataset. The transfer-learning has been experimented with the ResNet, XceptionNet, and VGG16 models for feature extractions. Further, the deep-learning models Long short-term memory (LSTM) and Bidirectional long short-term memory (Bi-LSTM) have been experimented with to train the extracted features. To rigorously test the proposed approach, the data set under the experiment combined the three benchmark datasets - The Deepfake Detection Challenge (DFDC), Celeb-DF, and Face-Forensics++ datasets. The prediction rate achieved was 96 % and above on average by the proposed approach for the datasets under consideration for the experiments. The proposed approach performs better than the state-of-the-art techniques, gaining a higher accuracy of 96% and above. © 2024 IEEE.

关键词： Contrastive Learning

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AI-Enhanced Learning Assistant Platform 7

AI-Enhanced Learning Assistant Platform

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sumanth, N. Sai Priya, S. Vishnu Sankari, M. Kamatchi, K.S. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Sist Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. © 2024 IEEE.

关键词： Students

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A Personalized Visual Communication App for Children with Autism 3

A Personalized Visual Communication App for Children with Au...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Karki, Ankit Namitha, Kalakunnath Singh, Kunal Agrawal, Ronak Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350364569

Autism Spectrum Disorder (ASD) is a condition that impacts interaction, communication and behavior usually showing signs, around the age of two. This paper presents a novel mobile application developed using Flutter to help children with ASD improve cognitive and communication skills through personalized, AI-powered learning modules, interactive quizzes, and visual aids. The proposed app aims to empower individuals with autism, facilitate learning and social interactions and equip parents with tools for monitoring progress. = The application includes communication features, tailored educational modules and progress monitoring capabilities. It leverages Flutter for platform functionality ensuring accessibility on both Android and iOS devices. The user friendly interface, symbols, for communication and secure parent dashboard enhance usability. Initial assessments indicate increased user engagement improved communication skills and greater independence. With a technology that offers interactive forms of communication, personalized learning modules, and dynamic assessments to match a student's progress with their performance. This app is available for Android and iOS devices through Flutter, a technology that enables it to work in both platforms. The app is easily accessible by users due to its user-friendly layout, visual communication symbols, and secure parent dashboard. Preliminary evaluations indicate increased user participation, enhanced communication, and higher self-reliance. Compared to others apps, this one adopts a more broad approach, emphasizing communication, education, and life skills. © 2024 IEEE.

关键词： Vision aids

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Implementing Multinomial Naive Bayes for Employee Empowerment: A Transparent Platform for Task Management and Issue Resolution

Implementing Multinomial Naive Bayes for Employee Empowermen...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sudeesh Kumar, V. Mahendira Kumar, S. Kumar, S. Praveen Akyf Thanish, A. Rithani, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379716

Developing a positive motivational culture among employees in today's changing workplace is achieved through effective communication and problem-solving. This abstract introduces an innovative Employee Empowerment Platform designed to enhance transparency and collaboration within the work-force. Using a text classification algorithm, this work presents an employee authorization system that classifies and analyzes problem reports submitted by employees. Using a multi-step approach to data preprocessing, the system extracts HTML tags, square brackets, and special characters from text data collected from problem reports. Count Vectorization and TF-IDF are two mathematical techniques used for transforming preprocessed text into features. At its heart, the platform uses a polynomial naive base classification to identify positive and negative emotions in problem reports and assign them to the appropriate category. In addition, the system uses data visualization techniques to provide a comprehensive understanding of the model's performance. Through accuracy plots and confusion matrices, you can study the prediction skill of the classifier. By establishing a clear process for finding work and facilitating problem classification, our employee empowerment program helps companies identify common concerns about work and fosters an exchange of ideas between employees creating a workplace environment that nurtures individual growth and collective success. The main objective of the platform is to increase enthusiasm and empowerment in the workplace by addressing challenges quickly. © 2024 IEEE.

关键词： Empowerment of personnel

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Implementation and Analysis of TCP CUBIC Congestion Control System in Mininet 9

Implementation and Analysis of TCP CUBIC Congestion Control ...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sunder, S Navin Akathya, Ve Ram Seshadri, Samarth Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the realm of data transfer dynamics, an intricate comprehension of congestion control is imperative for the seamless optimization of network performance. This study rigorously delves into the complexities, unveiling discernible disparities between sending rates and throughput. Emphasizing the critical role of congestion control, the research highlights escalating retransmissions and congestion as pronounced inefficiencies. The proposed methodology encompasses a thorough examination of packet loss, network utilization, and queuing delays, paired with the implementation of effective congestion control strategies. Post-implementation, the results demonstrate a significant enhancement in network performance, characterized by adaptive sending rates and minimized inefficiencies. This research underscores congestion control's pivotal role in optimizing performance, offering valuable insights for achieving an efficient and responsive network. © 2024 IEEE.

关键词： Data transfer

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Optimal Frame Extraction for Event Detection in Surveillance Videos 16

Optimal Frame Extraction for Event Detection in Surveillance...

引用

16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Ranganath, Venkataraman Padmavathi, S. Aarthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India

This paper addresses the efficient identification of candidate keyframes in long and untrimmed surveillance videos. Existing fixed-length segmentation methods for violence detection suffer from computational inefficiency and fragmented short-Term event repre-sentation. To overcome these limitations, we propose an Optimal Keyframe Extraction framework utilizing shot boundary detection to segment videos based on significant visual changes. Candidate Keyframe segments are extracted from the shots, focusing on potential anomalous segments. Keyframes are selected based on the highest motion changes, accurately representing short term dynamic events. This dynamic approach aims to minimize the processing time of long and untrimmed surveillance videos by reducing the number of segments required for analysis of the event. Results show that the complexity of analyzing the event ef-fectively can be done by extracting an optimal set of keyframes within 2-3% of total number frames for each video. This research improves violence detection by efficiently analyzing large video datasets and accurately identifying short-Term dynamic events, thereby enhancing public safety and security applications. © 2025 The Author(s).

关键词： Deep learning

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A Comparison of Explainable AI Models on Numeric and Graph-Structured Data 2

A Comparison of Explainable AI Models on Numeric and Graph-S...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Avinash, Alekh Harikumar, Akshay Nair, Adithya Pai, Sreejith Kumara Surendran, Simi George, Livya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

An exponential growth of interest in the healthcare IoT over the past few years has increased the adoption of AI. However, healthcare analytics demands highly accurate and reliable systems that can not afford even a small amount of vulnerability. The black-box nature of AI models raises significant concerns about the computations involved in these predictions, leading to a lack of trust in these models. Explainable Artificial Intelligence (XAI) seeks to resolve this problem by providing a human-understandable explanation for AI decisions, bringing transparency, trust, and fairness to these AI models. This paper evaluates the existing XAI models on numeric and graph-structured healthcare data. The LIME and SHAP models are first used to explain the predictions in the numerical dataset of fetal health prediction. Second, graph-structured data is analyzed using GNN Explainer and PG Explainer. This evaluation suggests that XAI model interpretations benefit healthcare professionals and patients to trust AI predictions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Design and Implementation of a Movie Rental Website Using Automata Theory 5

Design and Implementation of a Movie Rental Website Using Au...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Saluja, Abhinav Panda, Amitesh Kesavan, Suhas Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This paper proposes a novel approach to designing a movie rental website using concepts from push down automata theory. The objective is to create a user-friendly platform for browsing, selecting, and renting movies while leveraging the computational power of push down automata (PDA) to manage the underlying logic efficiently. The system architecture employs push down automata to model various states and transitions in the movie rental process. Users can interact with the website through a GUI, where they can search for movies, view their details and begin rental transactions. Behind the scenes, push down automata handles the validation of user inputs and enforces business rules such as rental duration and payment processing. Push down automata is utilized to represent the different states of a movie's availability, including available for rent, items in cart, currently rented, and returned. Transitions between these states are triggered by user actions such as renting a movie, returning it, or extending the rental period. By modeling these states and transitions with push down automata, the system ensures consistency and correctness in the rental process. Overall, this project demonstrates the practical application of push down automata theory in the development of a movie rental website, offering a scale-able and efficient solution for managing movie rental operations while providing a seamless user experience. © 2024 IEEE.

关键词： Web Design

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Smart Traffic Flow Optimization with IoT and Machine Learning 15

Smart Traffic Flow Optimization with IoT and Machine Learnin...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Khanna, Manju Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Traffic signal optimization is crucial for the effective traffic management within cities with the primary goals being the reduction of bottleneck occurrences, avoidance of the extension of travelling time, and improvement of overall city street network function. Historically used approaches can hardly be adjusted to fluctuating traffic situation. Hence, a new methodology involving the use of machine learning-based traffic signal control with the assistance of the IR (Infrared) /UV (Ultraviolet) sensors for traffic flow sensing in real-Time environment is introduced in this study. The UV sensors make congestion distance measurements in real time which are assisted by IR sensors for traffic information acquisition. Historic data with data gathered from the traffic sensors are used to train the machine learning model to predict traffic. The strategy employs the use of heuristic search methodologies in the determination of signal timings with reference to congestion information that has been forecasted as well as congestion information that is being experienced. This paper discusses the fact that machine learning-based approaches, supported by sensor technology, can be used for effective traffic management systems that modify with the help of data. The success of this traffic management system is proved by its ability to minimize the MSE (Mean Squared Error) of congestion predictions to an average of 0.69. © 2024 IEEE.

关键词： Information management

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Consumer Behaviour Analysis for Customer Segmentation and Purchase Prediction 10

Consumer Behaviour Analysis for Customer Segmentation and Pu...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Gopakumar, Amritha Shine, Aathira Ajim, Amina Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350384369

E-commerce, short for electronic commerce, is the practice of placing orders online or negotiating a sale's terms and price. Although e-commerce is often associated with selling products and services to consumers, business-to-business (B2B) transactions also make use of it. To market their goods, many smaller consumer enterprises rely only on online sales. Nowadays, people are choosing to make purchases online as online transaction mechanisms and shopping platforms develop. Nevertheless, given that both clients and Merchants are unable to interact with clients face-to-face, there is a limited understanding of consumer demands. Due to this, businesses may struggle to gauge nuanced cues such as body language and personal preferences. Limited direct engagement can hinder the ability to address specific customer needs and preferences, potentially resulting in misinterpretation or overlooking crucial details. In turn, businesses may face difficulties in tailoring their products or services to meet the nuanced expectations of their customers, emphasizing the importance of alternative strategies such as detailed customer feedback mechanisms and data analytics to bridge this gap. The online system logs user activity and gathers data on consumer behaviour, allowing for the prediction of consumer purchasing patterns. Therefore, to improve the shopping experience of people, our study aims to use historical sales data to analyze consumer behaviour and do customer segmentation using RFM (Recency, Frequency, and Monetary) based K-means clustering and purchase prediction using various ML models to improve sales, out of which the combination of Logistic Regression and XGBoost performed the best. © 2024 IEEE.

关键词： Sales

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