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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

共 21727 条记录，以下是411-420 订阅

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Advancing Ophthalmic Diagnostics: Employing Deep Learning for Precision Detection of Retinal Damage in OCT Images 2

Advancing Ophthalmic Diagnostics: Employing Deep Learning fo...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Rithani, M. Kumar, R. Prasanna Chatiyode, Veda Anusha, Janani Srinivasan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350373110

Retinal optical coherence tomography imaging is an important diagnostic tool for early detection and monitoring of various retinal disorders. This study aims to improve diagnostic accuracy by leveraging deep learning methods such as VGG16 and InceptionV3 models to classify retinal OCT images automatically, ultimately enhancing ocular diagnostics and clinical decision-making. Utilizing a dataset comprising approximately 85,000 high-resolution images across primary retinal condition categories including diabetic macular edema (DME), age-related macular degeneration (AMD), Choroidal Neovascularization (CNV), and healthy retinas, the efficacy of these deep learning models is assessed in this comprehensive study. Results demonstrate that VGG16 excels in CNV classification and InceptionV3 is proficient in identifying normal retinal conditions. Comparative analysis underscores their distinct strengths, with both models achieving an impressive overall accuracy of 92.76%. These findings demonstrate the potential of deep learning for automating OCT image interpretation and enhancing ocular diagnostics, thereby improving patient outcomes and clinical decision-making resulting in advanced healthcare practices. © 2024 IEEE.

关键词： Optical coherence tomography

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UAV Detection for Aerial Vehicles using YOLOv8 2

UAV Detection for Aerial Vehicles using YOLOv8

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sairam, D.V. Bhuvaneswari, R. Chokkalingam, S.P. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

In response to rising security concerns with drones, our research overcomes the shortcomings of current systems by developing a novel YOLOv8-based technique for UAV detection in. Thorough testing yields better results than YOLOv7, YOLOv7-UAV, and SSD in terms of metrics, indicating flexibility and accuracy in various airborne conditions. Our approach successfully detects UAVs in real-world trials using a drone equipped with a camera, highlighting its practical applicability. The research provides an effective detection methodology that makes a substantial contribution to the advancement of aviation security. Especially, our model shows remarkable accuracy with a mean average precision (mAP) of 99.5%. These results highlight the urgency of adopting sophisticated systems like YOLOv8 in order to defend against possible dangers and counter evolving UAV threats. This work achieves an accuracy of 99.5%, recall of 99.9% and an F1 Score of 90%. © 2024 IEEE.

关键词： Drone Detection Neural Networks UAV detection YOLOV8 You Only Look Once

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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...

引用

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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Advancing Supply Chain Management: Cloud-Based Demand Forecasting Solutions 3

Advancing Supply Chain Management: Cloud-Based Demand Foreca...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Spoorthi, M. Rao, Subhiksha Suresh Mishra, Sahil Rajagopal, Shinu M Prashanth, Mrudula Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354171

On-demand information that can be provided throughout all parts of the supply chain network and through cloud-based supply chain management system optimizes resource flow, enhances cooperation, and increases adaptability. The study delves into the amalgamation of AWS (Amazon Web Services) cloud solutions into demand projections for use in supply chain oversight. Inventory levels optimization, production planning streamlining and, thus, global supply chain efficiency tailoring all hinge on one factor: demand forecasting. Adopting AWS cloud services elicits a plethora of benefits;ranging from - but not limited to - scalability, flexibility and cost efficiency. AWS avails an array of instruments and provisions that enable entities to sift through copious historical data seamlessly. They can mix this with extrinsic determinants like market dynamism or even seasonal oscillations so as to arrive at those ever-accurate demand forecasts. On another front altogether is AWS' ability to handle storage, processing plus visualization capabilities for data silos integration towards seamless real-time updates delivery on forecast reports. © 2024 IEEE.

关键词： Amazon Forecast Cloud computing demand forecasting real-time supply chain management

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End-To-End Encrypted Agriculture Chatbot 8

End-To-End Encrypted Agriculture Chatbot

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Reddy, Pennabadi Devendra Reddy, K Satya Sampath Jayanth, P. Kavitha, C.R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

Agriculture sector is considered to be one of the most prominent sectors in the Indian economy. Farmers constantly face challenges such as a lack of information on best practices, disease detection, and market information. An End-to-End Agriculture chatbot can solve this problem by providing real-time information and support to farmers to enhance their productivity. This paper introduces a secure End-to-End Agriculture chatbot designed with strong encryption techniques to ensure data security. The proposed system will use the different asymmetric algorithm, a well-established public key cryptography method, to establish a safe and secure platform. Users sensitive data like soil reports and crop information will be encrypted using a widely available public key before sending it to the chatbot. After then, the chatbot uses its safe private key to decode the data. The asymmetric encryption method makes sure that the data being sent can only be accessed by authorized parties. © 2024 IEEE.

关键词： Encryption algorithms

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Graph-Based Approaches to Detect Network Anomalies and to Predict Attack Spread 15

Graph-Based Approaches to Detect Network Anomalies and to Pr...

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

作者： Rithuh Subhakkrith, S. Kumar, Y. Jagadish Harish, R. Dheeraj, B. Vishnu Vidhya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Real world applications of data structures is enormous. Queues, stacks, trees, and graphs are commonly applied and implemented across various fields. To improve the efficiency of such implementations, optimisation through the underlying data structure can be considered. computer networks inherently are based on graphs with communicating systems as nodes and communication channels as edges. Information flows between the communicating systems through the communication channel. To detect suspicious connections and information flow, an optimal graph structure is implemented by modelling the information flow as edges, and retaining the communicating systems as nodes. Graph analytics is performed to capture graphical relationships, and the final graph structure is formed with the features of the information flow split into node features and edge features. Then this graph structure is passed to Graph Neural Networks (GNN) that captures hidden relationships of the graph, and transforms these graph features in such a way that basic Machine Learning models like k-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest can train themselves and model into better anomalous connection classifiers. Eleven baseline models are trained using the traditional way, and using GNN transformed embeddings, and their performances are observed. Testing shows that all models show an improvement in the evaluatory scores. Finally, a logic is provided to build an attack sequence graph to track the route in which the attack spreads among infected systems. © 2024 IEEE.

关键词： Attack Sequence Graph Edge Classification Graph Analytics Graph Neural Networks Network Security

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Banana Freshness Classification: A Deep Learning Approach with VGG16 6th

Banana Freshness Classification: A Deep Learning Approach wi...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Patre, Falguni Vasant Arya, Aditya Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819774227

In the food sector, ensuring the safety and quality of banana products is crucial. The classification of bananas into "fresh banana" and "rotten banana" categories is the main objective of this study. The studied banana varieties are cavendish, lady fingers, and red bananas. We use the VGG16 deep convolutional neural network with a dataset of high-resolution banana images. Our method includes training-testing ratios, picture enhancement, and rigorous data preprocessing. The results indicate how well the VGG16 model performs in classifying freshness, with good recall, accuracy, precision, and F1-score. Additionally, the model successfully differentiates between cavendish, lady finger, and red bananas, highlighting its capacity to handle minute variations. This work expands the application of image classification to other fruit varieties by offering a dependable technique for quality control and automated evaluation of banana freshness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Food industry

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