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5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Simulation of an Intelligent Traffic Management System Using Vehicular Networks 3

Design and Simulation of an Intelligent Traffic Management S...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tapan Datta, P. Lalith Kumar, D. Tanoj, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In this study, the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the IEEE 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks. © 2025 IEEE.

关键词： Highway administration

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Cross-Language Code Mapping with Transformer Encoder-Decoder Model 8th

Cross-Language Code Mapping with Transformer Encoder-Decode...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Naik, M. V. Deepak Jayaraman, Swaminathan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819721467

In the dynamic field of software development, knowledge of multiple programming languages is increasingly valued. To address the demand for cross-language systems, our research investigates code translation and proposes a new approach using transformer models to map cross-language codes. We draw inspiration from the success of transformer-based neural networks in natural language processing and apply these models to the complex world of programming languages. A central hypothesis is that transformers can effectively translate code between languages, acting as a bridge between programming paradigms. We develop a custom transformer encoder-decoder model for program-to-program translation that is initialized with a dataset containing C++ programs and corresponding codes in Haxe, Kotlin, Python, and Java. The cooperation between the encoder and the decoder allows a smooth translation of the code, which shows considerable adaptability. Through fine-tuning with data from other programming languages like Kotlin and Python, our model extends its translation capabilities. Enabling Haxe as an intermediate language further facilitates translation to target languages like Java. This adaptability, based on a transformer-based architecture, effectively meets the challenges of an ever-evolving programming environment. It should be noted that our approach differs from large language models in terms of programming complexity and capturing OOP design patterns. In addition to translation, our research paves a way for code visualization using tools such as Java interactive visualization environment. "Cross-Language Code Mapping with Transformer Models" marks a significant advance towards automatic code translation in object-oriented programming, enriching developer tool kits and promoting collaboration across language boundaries in software engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Program translators

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Explaining Sentiments in Indian Legal Judgments with LIME and SHAP 8th

Explaining Sentiments in Indian Legal Judgments with LIME an...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Babu, T. Hemanth Kiran, V. Gopi Bhat, Suhas S. Prabhakar, Priyanka Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

This paper presents a comparative study on sentiment analysis applied to legal texts, specifically legal opinions, and judgments. This paper uses embeddings such as T5, XLM Roberta and BERT, Roberta, and Legal BERT to convert the textual data into pre-trained language model word representation, to make it easier for ML algorithms to determine the sentiment (positive, negative, or neutral) of legal documents. The dataset was converted into embeddings, and various models, including KNN, ANN, SVM, random forest, XG boost, logistic regression, and decision tree, were employed for sentiment analysis. Each model was subjected to hyperparameter tuning using Grid Search CV, resulting in the highest accuracy of 66.66% with SVM on T5 embeddings. This research provides valuable insights into the emotional degrees within legal texts, demonstrating the effectiveness of our sentiment analysis approach. © IFIP International Federation for Information Processing 2025.

关键词： Embeddings

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Path Planning for Autonomous Drones Using Proximal Policy Approximating Agent

Path Planning for Autonomous Drones Using Proximal Policy Ap...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Varshini Devi, I. Pravine Mukesh, R. Paayas, P. Varsha, P. Supritha, R. PrasannaKumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819795062

The advent of autonomous drones promises transformative applications across many industries, necessitating the ability to plan long-range paths automatically. The purpose of this paper is to explore the synergistic effect of Markov Decision Processes (MDPs) and deep reinforcement learning (DRL) in the context of autonomous drone racing. A simulated racing track is used to demonstrate how autonomous drones can optimize path planning using the Proximal Policy Optimization algorithm (PPO). In our study, we present an agent that efficiently navigates strategic checkpoints, is adaptable, and generalizes its skills to similar situations. It demonstrates the potential of DRL and PPO to advance autonomous drone path planning, paving the way for practical implementation in a wide range of industries. With drones reshaping industries, this work contributes to the exciting future of autonomous systems. The agent covered an average distance of 19.6 m per episode, showcasing its ability to navigate the test environment effectively. In a notable feat, the agent exhibited the capability to pass through a maximum of 9 holes in the test environment without encountering any collisions. This indicates a high level of adaptability and strategic decision-making, even in the face of increased environmental complexity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Markov processes

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A Hybrid Feature Selection Model for Early Prediction of Metabolic Syndrome 4

A Hybrid Feature Selection Model for Early Prediction of Met...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sreepadh, A.S. Sheshu, P. Reshiha, R.G. Kumar, T Vinay Jeyakumar, Gurusamy Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Metabolic Syndrome (MetS) is a medical condition that increases the risk of cardiovascular diseases, stroke, and type 2 diseases. Some of the symptoms of MetS are high blood pressure, elevated blood sugar, excess body fat, and abnormal cholesterol levels. All these symptoms are interconnected, which has made early diagnosis of MetS critical. In current medical strategies, there is no personalized medication which leads to unfavorable health outcomes and further increased risks. This paper aims to address these challenges by identifying optimal feature subsets required for the early prediction of MetS. To get the optimal features a hybrid model is proposed in this paper. The proposed model utilizes both the filter method and the wrapper method. Firstly, features are selected using the correlation coefficient (CC) method which is a filter method, and then the selected features are further refined using genetic algorithm (GA) which is a wrapper method. This hybrid model increases the prediction accuracy and reduces computational cost. The methodology of the proposed model, its experimental setup, the results obtained, and the inferences gained are presented in this paper. © 2025 IEEE.

关键词： Blood pressure

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Keyframe Extraction Approaches for Intelligent Video Analysis 4

Keyframe Extraction Approaches for Intelligent Video Analysi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sabarivasan, N. Thangavel, Senthil Kumar Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Real-time violence detection is essential for protecting the safety and security of people, especially in college campuses that are dynamic and have crowds. Manual surveillance systems are popular but inefficient as they require human attention all the time and are not capable of quickly identifying and responding to violent *** the exsisting violence detection model takes quite a time for processing the entire video. The proposed model extracts features as keyframes from the video using MobileNet V3 and K-medoids clustering. The redundancy of video data can be avoided and significant violent activities identified by isolating and analyzing critical frames from video data. This enhances the efficiency and responsiveness of surveillance systems. This work primarily contributes a customized violent activities dataset and implementing keyframe extraction algorithm for optimizing the usage of computational resources, and incorporating lightweight algorithms for seamless deployment with existing CCTV networks. The experimental results clearly demonstrate the ability of this approach to detect keyframes, thereby offering a scalable and practical solution. © 2025 IEEE.

关键词： Security systems

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework 3

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Garg, Shail Gayatri, Yerukola Surya Kausthub, A. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid. © 2025 IEEE.

关键词： Smart power grids

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HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multi-Agent Adversarial Games

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 465-479页

作者： Li, Wei Hu, Boling Song, Aiguo Huang, Kaizhu Southeast University School of Instrument Science and Engineering Jiangsu Nanjing210096 China Duke Kunshan University Department of Electrical and Computer Engineering Jiangsu Kunshan215316 China

In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiveness and slow convergence in complex wargame environments. Inspired by how human commanders make decisions, this paper proposes a novel method named Fully Integrating Hierarchical Decision-Making and Tactical Knowledge (HDMTK). This method comprises an upper reinforcement learning module and a lower multi-agent reinforcement learning module. To enable agents to efficiently learn the cooperative strategy, in HDMTK, we separate the whole task into explainable subtasks, and design their corresponding subgoals for shaping the online rewards upon tactical knowledge. Experimental results on the wargame simulation platform 'MiaoSuan' show that, compared to the advanced multi-agent reinforcement learning methods, HDMTK exhibits superior performance and faster convergence in the complex scenarios. © 2016 IEEE.

关键词： Adversarial machine learning

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IoT-based Environmental Monitoring System 3

IoT-based Environmental Monitoring System

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This project meets the current important demand for the constant monitoring of environmental conditions because climate change and human activities affect ecosystems. Temperature and humidity are measured using the DHT11/DHT22 sensor connected to the ESP8266 microcontroller and the GPS module is used to obtaining the location information. In contrast to current approaches targeting the communication data or energy consumption only, the contaminants of this system are unified, location-based, and central. It provides immediate data transfer to the cloud through a comfortable low power interface for usage in farming, transportation, and more for environmental monitoring. The findings show that all the environmental conditions and location change can be monitored by the IoT-based system which use ESP32/Arduino microcontroller for sensing data by a Bluetooth or Wi-Fi through cloud platform or server and it gives alert message to web interface or mobile application when temperature increases or decreases more than threshold values. This practical system is especially valuable for those areas of interest that require a constant check, such as environmental control or the logistics and health safety of the transported goods or passengers. © 2025 IEEE.

关键词： Environmental monitoring

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