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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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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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Modeling Plants vs Zombies Gameplay using Finite automata 3

Modeling Plants vs Zombies Gameplay using Finite automata

引用

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

作者： Archana Reddy, P. Divya Jyothi, G. Varshita, Velumury Akshitha, Chennupati Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Playing video games on personal computers is known as PC gaming. PC games are available in a variety of genres, offer both single-player and multiplayer options, and have spawned an international e-sports industry. Modding communities are active, digital distribution systems are essential, and hardware can be customized. Future developments in gaming, such as virtual reality, cloud gaming, and artificial intelligence, portend more innovation in the industry. The game, Plants vs Zombies is represented by Finite Automata and it is ruled by DFAs and NFAs which describe the actions and behaviours of both the plants and the zombies and the result of their actions. In the proposed work you can notice how the NFA will be converted to DFA and vice versa due to its handy functions. Overall, "Plants vs Zombies"is a fun and interactive way for players to explore computational thinking while learning about the complex ideas of NFA and DFA in Automata Theory. The advantages are Synchronize State Management, Consistent State Transitions, Easier Development, and Unambiguous Animation Staging. © 2024 IEEE.

关键词： Virtual reality

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos 15

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

引用

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

作者： Sree, V. V. V. Bhagya Bharathi Mohan, G. Prasanna Kumar, R. Rithani, M. Pallavi, Gundala Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn't much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain. © 2024

关键词： AMOSI Attention CNN LSTM MultiModal

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A Hybrid Approach of Spider Monkey Optimization with Taylor Series for Optimal Routing in Wireless Sensor Network 7

A Hybrid Approach of Spider Monkey Optimization with Taylor ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sindhuja, M. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350359299

Clustering that facilitates routing protocols has been created in recent years to cut down on energy usage and increase the lifespan of WSNs. The LEACH cluster is well-known among WSNs. The LEACH selects a few sensor nodes at random from each cluster. Cluster heads (CHs) are the aforementioned nodes. Cluster members are the sensor nodes that make up each CH. CHs hold up extra load to coordinate diverse tasks during the cluster. Energy usage and overall network performance can both suffer if the wrong CHs are selected for the LEACH. Therefore, a significant factor in extending the WSN's lifetime is selecting an appropriate CH and an efficient routing algorithm. This work introduces a new approach to CH selection and optimal routing in WSN, combining the Spider monkey optimization technique with a Taylor-based gravitational search algorithm. The CHs are selected, and cluster-based routing algorithms are developed using Spider Monkey Optimization (SMO). The sensor nodes are first organized into clusters, with the maximum competent node being the cluster head. From there, communication is sent across several nodes in a multi-hop fashion. Taylor dependent gravitational search method (TDGSM) is introduced which uses the taylor series along with gravitational search method to find the optimal routing. The suggested algorithm has achieved superior results when compared to the existing methods. © 2024 IEEE.

关键词： Sensor nodes

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Chatbot to chat with medical books using Retrieval-Augmented Generation Model 3

Chatbot to chat with medical books using Retrieval-Augmented...

引用

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

作者： Bhavani Peri, Sai Deepa Santhanalakshmi, S. Radha, Radha Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350364569

This research paper presents a novel AI tool aimed at revolutionizing medical education by addressing the specific needs of medical students, fostering effective learning, and managing stress. The implementation leverages cutting-edge technology known as Large Language Models (LLMs), with a focus on utilizing ChatGPT4, Streamlit, and Trulens. The digital assistant serves as a friendly and accurate resource, responding promptly to complex medical queries, thereby optimizing study time. The study meticulously examines the balance between technical innovation and practical benefits, emphasizing the project's goal of not only creating a sophisticated tool but also providing tangible assistance to medical *** report outlines the successful development and deployment of the AI-powered study buddy, showcasing its potential to significantly improve learning environments, particularly in demanding fields such as medicine. Notably, the project empha-sizes the importance of groundedness in its chatbot, achieving an impressive accuracy rate of approximately 84% for a single textbook. The integration of Streamlit, ChatGPT4, and Trulens contributes to creating a seamless and user-friendly experience for medical *** research underscores the broader implications of AI in education, demonstrating how advancements in technology can positively impact learning outcomes. By reducing the time spent on information retrieval, the AI study buddy empowers medical students to concentrate more on core learning objectives, ultimately facilitating their journey to becoming proficient and successful medical practitioners. © 2024 IEEE.

关键词： Medical education

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Securing Smart Home IoT devices using Hyperledger Fabric 15

Securing Smart Home IoT devices using Hyperledger Fabric

引用

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

作者： Pranesh, R. Lalith Guptha, B. Aswinkumar, K. Pramoth, B. Abirami, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid growth of Smart Home applications has broadened the scope for a wide range of security concerns. Blockchain technology, specifically Hyperledger Fabric, offers a promising solution. The proposed model explores its application, leveraging the Fablo toolkit to enhance the security of Smart Home IoT devices. The decentralized nature of Hyperledger Fabric, with features such as its consensus mechanism and chaincode, helps mitigate common security issues like unauthorized access and device tampering. The resultant tamperproof network, coupled with favorable metrics like latency and transaction throughput, underscores the robustness of the proposed model. Our model promises low latency and high transaction throughput. The metrics assist the model in securely and efficiently handling a large number of transactions without compromising on speed and security. © 2024 IEEE.

关键词： Smart homes

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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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Sea Surface Temperature Prediction using Sample Convolution and Interaction Network 15

Sea Surface Temperature Prediction using Sample Convolution ...

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

作者： Gayathri, P. Yalavarthi, Rahul Varma, Jayanth Santosh Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Predicting oceanic phenomena plays a very important role in ensuring maritime safety, managing coastal areas and preparing them for any disasters like cyclone or tsunami. There are several factors that contribute to these severe weather events among which Sea Surface Temperature(SST) is a crucial one. Warmer oceans can often lead to stronger storms or even frequent heavy downpours, highlighting the importance of accurately forecasting SST. This study explores the performance of a deep learning model called Sample Convolution and Interaction Network (SCINet) in forecasting SST. This model follows a decompose-downsample-convolve-interact architecture that helps in learning complex trends in the data. On comparing SCINet’s performance with well established time-series models like Long-Short Term Memory (LSTM) network, it was able to achieve almost 4% - 20% lower Mean Absolute Error (MAE) than LSTM on various testing points. This highlights SCINet’s potential effectiveness in time-series forecasting, contributing to the ongoing development and revolutionary advancements in deep learning models for time series prediction. ©2024 IEEE.

关键词： Long short-term memory

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Multimodal Lung Cancer Image Analysis 15

Multimodal Lung Cancer Image Analysis

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

作者： Pallithodiyil, Akshita Prasanth Srikrishna, Atyam Preetham Sai, Gutha Venkat Paripoorna, D. Sathiya, R.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in detecting lung cancer, often diagnosed at an advanced stage, complicating treatment efforts. The proposed model integrates CT (Computed Tomography) and PET (Positron-Emission Tomography) scans to enhance lung cancer prediction. CT scans offer detailed anatomical insights, while PET scans excel in identifying abnormal metabolic activity and precise tumour localization. This combined approach provides a comprehensive assessment, empowering healthcare professionals with improved diagnostic accuracy and better-informed treatment planning for lung cancer patients. © 2024 IEEE.

关键词： classification CNN (Convolutional Neural Network) CT Early Fusion NIfTI (Neuroimaging Informatics Technology Initiative) Non-small Cell Lung Cancer (NSCLC) PET ResNet Structural Similarity Index Measure (SSIM) SVM

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