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Athletic PathFinder: Motion Dynamics and Trajectory Mapping 15

Athletic PathFinder: Motion Dynamics and Trajectory Mapping

引用

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

作者： Kumar, Ala Datta Sai Akhilesh, N.V.S.K. Balaji, Ravipati Venkata Prabhas, Pendyala Gireesh Kumar, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

It is not outside the scope of this to say that the new paper was a pioneer work that revolutionizes our understanding of the rules of routine production. The new approach put forward by our research manages to quantify ball possession time out of each game for both teams. This helps us understand better how games actually develop. The study employs the latest tool: VAEP (Valuing Actions by Estimating Probabilities) framework, and also maps the field in a 2D plane. With this new method, it becomes feasible to measure quantitatively the actions of all players, thus enabling a purely objective appraisal of each individual's performance. Analytical powers are further boosted by the study by incorporating a leaderboard that rates players according to their performance indicators. This all-around approach not only makes the game strategies of teams open to view in a whole new way, but also singles out indeed decisive players who have a substantial effect on events. The Pioneer Athletic PathFinder is a breakthrough for sports analytics, providing a powerful means for coaches, players, and analysts to study and improve their game strategies. Four baseline classifiers were trained, achieving the following accuracies: XGBoost (9 9%), Random Forest (9 7%), Polynomial (9 2%), and Bagging (9 2%). © 2024 IEEE.

关键词： 2D Field Mapping Motion Dynamics team performance trajectory mapping VAEP Framework

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Graphsage-Based Named Entity Recognition for Malayalam

Graphsage-Based Named Entity Recognition for Malayalam

引用

International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Harikrishnan, M.K. Bindu, K.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819746491

The primary goal of a Named Entity Recognition (NER) system is to gather relevant information from the given text. NER in Malayalam text is challenging due to the agglutinative structure of the language. Due to the increasing presence of the Internet, more people use smartphones, leading to an increase in Malayalam search queries. A major portion of these searches contains Named Entities which makes it reasonable to study methods to identify them effectively. Words can have relations with other words in a sentence. If these relationships are considered as edges of a graph, the sentences could be processed as a graph instead of in a sequential manner. Graph Neural Networks (GNN) have been used for processing graphs in recent years. In this work, graphs for sentences in the Malayalam language are created using a simple nearest word method and processed in a GraphSAGE (Graph Sample and Aggregated)-based NER system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graph neural networks

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Malware Image Classification with Enhanced Feature Extraction Using VGG16 and Spatial Pyramid Pooling 8

Malware Image Classification with Enhanced Feature Extractio...

引用

8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Ryan, Collin Diviya, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331531539

In the present era, the rise in number and complexity of malware poses a major threat leading to the rise in the importance of smarter Malware detection systems to understand and detect modern malwares. Modern systems are often very slow and inefficient with outdated machine learning models and feature selection techniques. These inefficiencies are overcome by the use of a multiclass detection system with CNN application to automatically classify malware. The system represents malware binaries in image format and uses VGG16 based network architecture with Spatial Pyramid Pooling (SPP) and ReLU activation to efficiently retrieve multiscale features from images of variable dimensionality. The model also includes dropout regularization, categorical cross entropy and Adam optimizer for better performance, adaptability and generalization. This approach works with a well balanced dataset with extensive training and achieved an accuracy of 95.94% making it a reliable and scalable solution for real time threat detection in a dynamic environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing 802.11 MAC Protocol: A Design and Implementation Approach for Improved Throughput, Efficiency, QoS, and Reduced Latency in Low Contention Networks for Voice and Video Communication

Optimizing 802.11 MAC Protocol: A Design and Implementation ...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Rajesh, M. Sreevidya, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350309140

This study suggests modifications to the already existing IEEE 802.11 MAC protocol, by proposing the elimination of the back-off period following collisions and recommending the use of Distributed Inter-Frame Space (DIFS) for post-collision waiting. The revised protocol is validated using the Network Simulator which involves simulations with six nodes setup in a wireless medium. The Network Simulator will act as an important step in evaluating and analyzing the findings of this study. The primary objective of this research is to transform the detection of collision events by relying solely on the DIFS time instead of using the conventional back-off period along with DIFS time. Expected advantages include improved network speed, reduced contention, and optimized channel access. The insights from this study may further contribute to the ongoing development of the wireless communication protocols based on IEEE 802.11. © 2024 IEEE.

关键词： Internet protocols

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Temporal Fusion of Time-Distributed VGG-16 and LSTM for Precise Action Recognition in Video Sequences 5

Temporal Fusion of Time-Distributed VGG-16 and LSTM for Prec...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Athira, K.A. Divya Udayan, J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Autonomous systems play a vital role in creating an ambient environment, but for comprehensive human-robot interaction, the system must interpret and identify specific human behaviours. The demand for recognizing human activity from videos has significantly increased across diverse domains, such as medicine, intelligent video surveillance, human-robot interaction, and ambient-assisted environments. Deep learning has emerged as a pioneering technique for human activity recognition from videos due to its ability to learn features from input videos automatically. This research proposes a novel approach for recognizing activities in videos by leveraging the combined power of the VGG-16 convolutional neural network (CNN) and the Long Short-Term Memory (LSTM). To assess the efficiency of our suggested methodology, experiments are conducted on the widely used benchmark dataset UCF-50. The experimental results demonstrate that our VGG-16 and LSTM-based model outperforms state-of-the-art methods for activity recognition, achieving higher accuracy and robustness across various activity categories. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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AI-Driven Stress Analysis and Management: A Novel Approach Leveraging OpenAI and NoSQL Databases to Empower Students in Stress Management and Promote Overall Student Well-being and Academic Progression

AI-Driven Stress Analysis and Management: A Novel Approach L...

引用

2024 International Conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Reddy, B. Akhileswar Reddy, G. Someswara Lokesh, K. Sreevidya, B. Rajesh, M. Amrita School Of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350306446

Now a days, students are under a lot of stress due to social issues and schoolwork. Additionally, they use smart devices excessively, which negatively impacts their mental health. an online application that allows users to actively interact with the system to get recommendations and insights specific to their level of stress. This ground-breaking fusion establishes the foundation for an approachable and proactive stress-reduction solution. This project aims to produce a novel stress analysis tool by integrating NoSQL databases with OpenAI's superior natural language processing, specifically the GPT-3.5 model. Real-time stress analysis is made possible by the system's ability to collect extensive stress-related data from users by utilizing OpenAI's superior language understanding skills. Scalability is enhanced by the incorporation of NoSQL databases, which guarantee effective unstructured data retrieval and storage. © 2024 IEEE.

关键词： Stress analysis

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Enhancing Computation Offloading In Wireless-Powered Mobile-Edge computing Networks With Deep Reinforcement Learning For Online Optimization 4

Enhancing Computation Offloading In Wireless-Powered Mobile-...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, R. Prasanna Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sai, Gudivada Dhyana Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Edge computing at the mobile frontier, enhanced by the integration of wireless energy, represents a cutting-edge strategy to boost processing performance in networks with limited energy resources, such as wireless sensor networks and the Internet of Things (IoT). This study investigates a mobile edge computing (MEC) framework powered by wireless energy, employing a dual-mode offloading scheme. In this paradigm, tasks from wireless devices (WD) may either be processed on-device or entirely shifted to an MEC server. In this approach, tasks from a wireless device (WD) are either processed locally or completely transferred to an MEC server. The objective is to create an online algorithm that can adjust task offloading and wireless resource allocation adaptively according to the variable conditions of the wireless channel. Conventional numerical optimization methods are inadequate due to the swift variations within the channel's coherence time. Our aim is to develop an algorithm that operates online and can dynamically adjust both offloading and resource distribution in response to the fluctuating state of the wireless channel. Traditional numerical optimization approaches fall short because they cannot swiftly adapt to the rapid changes in the channel's coherence. The solution we propose is a framework based on Deep Reinforcement Learning for Online Offloading that utilizes deep neural networks to incrementally learn from offloading decisions, thereby circumventing the need for complex combinatorial optimization. This leads to a significant reduction in the computational load, particularly in expansive networks. We've further enhanced this system with a method that enables real-time modification of the DROO algorithm's parameters. Our experiments demonstrate that this novel algorithm nearly achieves optimal efficiency and significantly reduces computation times - by more than ten times relative to existing techniques. For example, in a network with 30 users, DROO achiev

关键词： access points component Mobile-edge computing radio-frequency reinforcement learning resource allocation Wireless Devices WPT

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Emotion Recognition Using Meta-Learning based on Facial Expressions 5

Emotion Recognition Using Meta-Learning based on Facial Expr...

引用

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

作者： Prashanth, V.J. Sri Naga Jathin, P. Gopinath, Suraj Udith, S. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications. © 2024 IEEE.

关键词： Contrastive Learning

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Integrated Framework for Accurate Change Detection and Deforestation Identification in Satellite Imagery 2

Integrated Framework for Accurate Change Detection and Defor...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Prabu, M. Preetham, Simhachalam Sanju Tu, Devakrishnan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

The escalation of deforestation, particularly in the urban periphery, underscores the urgency for advanced frameworks in change detection. This research addresses this imperative by introducing a comprehensive solution for precise identification of deforestation and change detection in satellite imagery. Our proposed approach encompasses two key frameworks: one focused on change detection and the other on deforestation identification. A Modified U-Net for change detection, emphasizing pixel-wise alterations in satellite imagery. Simultaneously, a VGG Net model is employed to identify deforested areas, focusing on distinguishing relevant features. The Modified U-Net architecture leads with an exceptional 99.25% accuracy, excelling in detecting nuanced alterations, especially in urban landscapes. Integration of the VGG Net model further amplifies accuracy, achieving a commendable 91.87% in pinpointing deforested areas. This dual proficiency showcases our framework's adaptability, adeptly handling diverse environmental monitoring challenges. Emphasizing robustness and effectiveness, our framework emerges as a pivotal tool in remote sensing, making significant contributions to environmental conservation and land-use monitoring. © 2024 IEEE.

关键词： Environmental monitoring

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SecureNet: A PySpark-Based Approach to Enhanced Network Intrusion Detection Using Machine Learning and Feature engineering 4

SecureNet: A PySpark-Based Approach to Enhanced Network Intr...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Reddy, B. Akhileswar Reddy, G. Someswara Lokesh, K. Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

Cyber-attacks are increasing day to day in this world of AI and Internet. A ML model to detect the network attack based on the network parameters is of great use. The proposed model uses a new and complete method to make intrusion detection in computer networks more accurate. Using PySpark's shared computing power, machine learning rules are put in the network data. It helps to make a strong first step of sorting things out. To optimize and streamline the feature space, two distinct techniques are employed sequentially;Chi-Square Selector for selecting important features and Principal Component Analysis to reduce features. These methods are used one after the other to improve how well machine learning models work. This makes them more accurate and flexible for finding unauthorized attacks on computer systems. It adds important value to the world of cybersecurity and Big Data analysis. From the experiments. The model which reported the best performance is Multi-Layer Perceptron with 0.993 accuracy. © 2024 IEEE.

关键词： Network intrusion

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