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Addressing the Challenge of Deepfake Media: Robust Detection for Ensuring Authenticity 15

Addressing the Challenge of Deepfake Media: Robust Detection...

引用

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

作者： Juneja, Kanishka Lalithamani, N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

As fake content gets more sophisticated, there's a competition between making better fakes and better detectors. In an era where seeing is no longer believing, we must sharpen our commitment to authenticity in the face of technological deception. The study looks closely at how deepfake detection model works, including the different ways researchers have previously described and methods used to detect misleading videos and images that are spread very easily nowadays through various platforms. This research centers on a comparative analysis of prominent predefined models, namely CNN, VGG16, and Xception networks, in the realm of deepfake detection. Employing these models individually and by exploring different combinations of them, our methodology aims to unravel the distinctive strengths of each architecture. By evaluating their performance comprehensively, we seek to identify accuracy, precision and robustness of deepfake detection, making advancements in the ongoing process of safeguarding digital authenticity. By doing so, our research seeks to bridge the gap between theoretical model strengths and practical application, ensuring that the identified methods align with the demands of the digital world. © 2024 IEEE.

关键词： artificial intelligence Deep learning Deepfake DFDC Digital Forensics Neural Networks

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Emotion Recognition from Speech, Text, and Facial Expressions Using Meta-Learning

Emotion Recognition from Speech, Text, and Facial Expression...

引用

International Conference on Applied Soft computing and Communication Networks, ACN 2023

作者： Laalitya, Garapati Vaishnavi, Koduvayur Satyanarayana Rohith, T. Kavitha, C.R. Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819720033

This paper presents a meta-learning methodology for emotion detection in multimedia content. The proposed approach uses three distinct models: Siamese network, convolutional neural network (CNN), and recurrent neural network (RNN) to identify emotions in a self-produced database of 1200 video and audio files. The Siamese network compares input files with reference to emotions to determine the emotion of the input file, while CNN classifies the input files into several emotion categories. In cases where data is scarce or insufficient to tackle a particular problem, meta-learning becomes a powerful strategy for quickly adapting to new scenarios and training with a limited set of examples. To enhance performance across various tasks, it is crucial to train a well-refined meta-learning model on a range of learning tasks. The justification for incorporating meta-learning in this study for emotion recognition lies in its versatility and adaptability, as it enables effective adaptation to novel environments and challenges. The performance of the models is evaluated against traditional machine learning methods, and the results show that the meta-learning approach achieves state-of-the-art performance on the self-created dataset. The suggested meta-learning strategy has the potential to enhance the precision and adaptability of current emotion detection methods. Additionally, it can be expanded to various domains and applications, including speech recognition and sentiment analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Music Recommendation System based on Big Data using Pyspark 9

Music Recommendation System based on Big Data using Pyspark

引用

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

作者： Jain, Nikunj Sah, Nabin Kumar Hawari, Jawed Khare, Sangita Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the evolving landscape of digital music consumption, personalized song recommendation systems play a pivotal role in enhancing user engagement and satisfaction. This article presents an in-depth exploration and analysis of an innovative song recommendation framework designed to address the challenges of delivering tailored music experiences. Utilizing advanced machine learning techniques, especially K-Means Clustering Algorithm and data analytics, the proposed system aims to offer song suggestions according to user preferences. The proposed system introduces a hybrid song recommendation system that combines the two distinct approaches i.e. Content and Collaborative based filtering. Various algorithms were implemented for Content based filtering whereas Hadoop and Pyspark was used for implementation of Collaborative filtering. Wide research and Evaluations based on various metrics were considered. The research also draws the strengths and drawbacks or limitations of Content based Recommendation over Collaborative based Recommendation System. The results highlight the system's capability to overcome limitations eventually enhancing user experience by offering a better experience. © 2024 IEEE.

关键词： Recommender systems

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Exploring Sarcasm Detection: Leveraging Neural Network Models with BERT Embeddings 4

Exploring Sarcasm Detection: Leveraging Neural Network Model...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Rayvanth, N. Shreya Shree, S. Challa, VenkataHemant Kumar Reddy Sharma, Vishwash Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

Sarcasm is a form of verbal mockery, which poses a unique challenge for common people to understand, this also leads to difficulties in Natural Language Processing and sentiment analysis. This study deals with Sarcasm detection from textual data and a few machine learning and ensemble models were compared with the proposed Deep Learning architecture. BERT was used for the extraction of features and vectorization of textual data. The study utilizes curated datasets consisting of non-sarcastic and sarcastic samples sourced from Kaggle and Reddit platforms. The models will be evaluated using standard metrics. The best ML model accuracy achieved was 0.88 and an F1 score of 0.89 with a combination of Bert Embeddings and the SVM model. The proposed methodology using BERT embeddings a simple neural architecture achieved an accuracy of 0.92 on the test dataset with a F1-score of 0.91. It has a far lesser complexity than other well-known Deep learning architectures in use in other works. With the live streaming of The proposed Neural Network model achieved an accuracy of 0.94 and an F measure of 0.90 on the data streamed from Reddit. © 2024 IEEE.

关键词： Contrastive Learning

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FishNet Freshwater Fish Disease Detection using Deep Learning Techniques 2

FishNet Freshwater Fish Disease Detection using Deep Learnin...

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

作者： Sanjay Kumaar, A. Vishnu Vignesh, A. Deepak, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350371314

Fish diseases pose significant challenges to aquaculture, causing economic losses and environmental issues. Early and accurate detection is important for effective management and prevention of diseases. This study proposes using three deep convolutional neural network architectures: Inception, VGG16, and a new CNN model called Fishnet. The efficacy of the models is assessed in two scenarios: using transfer learning and without it. The new FishNet model outperforms the pre-trained VGG16 network, demonstrating a 2% improvement in accuracy for disease detection in freshwater fishes. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-Driven Low-Resolution Thermal Image Reconstruction for Enhanced CWSI Analysis 5

Deep Learning-Driven Low-Resolution Thermal Image Reconstruc...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： George, Anzen T. Deepak, Devithaa M. Kunnath, Gayatri Pradeep Devi, Gowri Parvathy Sarath, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

The timely detection of drought stress in chili plants is a prerequisite for the healthy life of the crop and maximum production. This study proposes a new method to address the problem with the help of deep learning for the purpose of effectively identifying drought resistance in chili plants and focus on the detail analysis of plant health. We intend to use deep learning frameworks to design Convolutional Neural Network (CNN) models to upscale the low-resolution thermal images of chili plants to their high-resolution images. CWSI is computed based on the generated images, further employed to evaluate drought stress in chili plants. A real-world dataset of chili plants under various water stress circumstances will be gathered in order to accomplish this. The Crop Water Stress Index(CWSI) can be computed using the high-resolution thermal images generated via the CNN models. Compared to conventional visual inspection techniques, CWSI offers a more objective and accurate assessment by providing a quantitative measure of a plant's water stress level. Our method seeks to greatly increase the accuracy of drought resistance evaluation in chili plants by enhancing the integrity and visual clarity of low-resolution thermal images. Improved accuracy and effectiveness in agricultural monitoring and resource management strategies will follow from this. Ultimately, it could empower farmers to make data-driven decisions about irrigation techniques, maximizing water use effectiveness and guaranteeing the productivity and long-term health of their chili crops. © 2024 IEEE.

关键词： CNN CWSI ESPCN RDN SRCNN

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Emotion Recognition from Speech using Telugu Language and Deep Learning Models 1

Emotion Recognition from Speech using Telugu Language and De...

引用

1st IEEE International Conference on Communications and computer science, InCCCS 2024

作者： Aditya, S. Satwik, Vaka R. Krishna, Vamsi V. Vimal, C. Palaniswamy, Suja Pati, Peeta Basa Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350358858

This research involves the use of deep learning techniques to detect emotions, in Telugu speech signals, a research domain that has received little attention. Amrita speech emotion dataset-1 (ASED-1) consisting of 550 signals representing five categories. Surprise, anger, happiness, neutrality, and sadness. We explore how well various deep learning models can recognize emotions from speech. Our approach involves training models such as Convolutional Neural Networks (CNNs) Long Short-Term Memory (LSTM) networks and Multi-Layer Perceptron (MLPs) on a portion of the data and evaluating their performance on a test set. The MLP model achieved an accuracy rate of 84% although this was constrained by the size of the dataset. The CNN model showed results with decreasing trends in both training and validation loss indicating learning and generalization abilities. Similarly, the AlexNet architecture demonstrated a decline in both training and validation loss suggesting a fit without overfitting. Additionally, we included a machine learning classifier at the layer. Observed different accuracies across various models. The RNN model achieved an encouraging accuracy rate on the test data showing potential for emotion classification tasks but also room for improvement, through complex architectures or larger datasets. This research paper adds to the knowledge, in the field of emotion recognition, in Telugu speech by emphasizing the difficulties and possibilities of using deep learning techniques in this domain. © 2024 IEEE.

关键词： Emotion Recognition

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Speech to Action for Assisting Deaf Communities 8th

Speech to Action for Assisting Deaf Communities

引用

8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Krishna, Vunnam Vamsi Naini, Dheeraj Reddy Reddy, Vaka Satwik Sakri, Aditya Rajesh Radha, D. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819766802

In response to the changing world we live in, this groundbreaking project aims to redefine how people interact within the deaf and hard of hearing communities. The main goal is to develop a system that can understand and translate sign language while also preserving the aspect of emotion. By leveraging the power of technologies like speech recognition, emotional analysis, and deep learning techniques like CNN, LSTM an extensive system has been designed to adapt and respond in real time. This ensures that translated sign language maintains its subtleties for an enriching communication experience. Extensive testing of the system has been conducted as a part of the feedback loop by gathering information using the application. The results have confirmed its accuracy in translating languages and capturing emotions leading to empathetic interactions within the community. This work represents an advancement in how the hearing impaired interact within the communities, highlighting the importance of inclusivity, in advancements. It signifies a milestone toward envisioning a future where the captivating essence of communication effortlessly transcends through expressive sign language ultimately improving user experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Sudoku Solver Algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： Optimization algorithms

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Tri-Model Synergy: Redefining Depression Detection through the Convergence of ANN, CNN, and SVM 5

Tri-Model Synergy: Redefining Depression Detection through t...

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

作者： George, Angelina Baniya, Achal Jose, Alphonsa Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Depression being a universal mental health challenge that affects millions globally, necessitating reliable detection methods in order to aid in timely intervention. Like physical health, mental health is not to be ignored. This study presents a novel hybrid model that combines Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) to analyze and detect depression in individuals. The hybrid model achieved a training accuracy of 95.6% and a test accuracy of 91.2%, demonstrating its robustness and reliability. The total training time was recorded at 3.01 seconds, with ANN contributing 1.11 seconds and CNN contributing 1.9 seconds. Furthermore, the model's performance metrics included an F1-Score of 0.91 and 0.91 for detecting the classes of absence and presence of depression respectively, highlighting its effectiveness in correctly identifying depressive states. This hybrid approach provides a comprehensive analysis by leveraging the strengths of each individual model, paving the way for more reliable and scalable mental health assessment tools. © 2024 IEEE.

关键词： Convolutional neural networks

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