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Cluster Based Classification of Question Independent C Codes 8th

Cluster Based Classification of Question Independent C Codes

引用

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

作者： Vijjapu, Aditya Meti, Aditya H. Rao, Aniruddh Balakrishnan, Roshni M. Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

In the field of software development, ensuring the accuracy and quality of code remains a paramount concern. The task of precisely classifying code as correct or incorrect poses inherent challenges. This research introduces a groundbreaking approach that uses clusters constructed from code embeddings generated by CodeBERT to effectively classify code into distinct clusters representing correctness or incorrectness. The model’s ability to grasp intricacies in code semantics and structure leads to a significant reduction in debugging efforts. Consequently, this approach contributes to an overall increase in the reliability and robustness of software systems. CATBOOST algorithm consistently demonstrated high performance with an accuracy of 84% during the experimentation. © IFIP International Federation for Information Processing 2025.

关键词： Software reliability

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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...

引用

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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MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm 3rd

MRI-Based Brain Tumour Detection and Classification Using Ra...

引用

3rd International Conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Jareena Begum, D. Chokkalingam, S.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783540

A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image segmentation technique and a classifier. One such classifier is the Random Forest Algorithm. The Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT) are also employed. The created method is put to the test using a dataset available on the Kaggle platform. The acquired results were approximately 97.68% accurate. A confusion matrix and a comparison of the developed method to other research in the literature are included in the investigation to facilitate the implementation of the proposed strategy in image testing. This Research shows that the offered system is superior to others in terms of detection rate, false positive rate, and recall. Results from this study showed that the developed method was effective at detecting brain tumours due to its high levels of accuracy, precision, and recall. According to the proposed strategy, it is essential to provide professionals who diagnose brain tumours with access to such technology. © The Author(s) 2025.

关键词： Image segmentation

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IoT-based Environmental Monitoring System 3

IoT-based Environmental Monitoring System

引用

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

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

引用

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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Advanced Financial Sentiment Analysis using FinBERT to Explore Sentiment Dynamics 3

Advanced Financial Sentiment Analysis using FinBERT to Explo...

引用

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

作者： Baghavathi Priya, S. Kumar, Madhav Nitheesh Prakash, J.D. Krithika, N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331527549

This research investigates the incorporation of sophisticated Natural Language Processing (NLP) methods in the intricate field of finance. In particular, it utilizes FinBERT, a tailored language model created to understand the complex subtleties of financial text data, in conjunction with assessments of DistilBERT and Bidirectional Encoder Representations from Transformers (BERT). The main goal is to create a strong preprocessing framework that improves the interpretative ability of models for sentiment classification designed for financial uses while providing an in-depth evaluation of the comparative performance of the models. Through extensive empirical validation, our approach demonstrates that FinBERT outperforms the other models in accurately capturing subtle sentiment variations and financial terminology. The comparative study underlines the compromises between computational efficiency and precision, with DistilBERT providing a streamlined option. This research emphasizes FinBERT’s transformative capabilities in analyzing financial sentiment and simultaneously delivering essential insights into the comparative strengths and weaknesses of other approaches, presenting a comprehensive viewpoint to inform decision-making within the financial sector. Upon comprehensive analysis of the methods used in this paper, it is shown that FinBERT is best at capturing hidden features within the data and able to work well with unseen data also with an average accuracy of 89.6% over all the 3 classes that are present in the dataset. The suggested framework has the potential to greatly improve predictive analytics and risk management approaches, allowing for more informed, precise, and prompt financial decisions in intricate market conditions. © 2025 IEEE.

关键词： Predictive analytics

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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...

引用

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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Path Planning for Autonomous Drones Using Proximal Policy Approximating Agent

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

引用

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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Efficient Detection of Acute Lymphoblastic Leukemia in Microscopic Blood Cell Images 12th

Efficient Detection of Acute Lymphoblastic Leukemia in Mic...

引用

12th International Conference on Big Data and Artificial Intelligence, BDA 2024

作者： Pavani, Bhupathi Vishva Thanmai, Gaddam Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031818202

Acute Lymphoblastic Leukemia (ALL), a type of blood cell cancer, rapidly progresses until it affects the white blood cells, compromising the human body’s ability to fight infections. Untreated Acute Lymphoblastic Leukemia (ALL) can quickly infiltrate vital organs, leading to life-threatening situations and its early treatment is essential to prevent the destructive spread and improve the survival chances. Recent deep-learning techniques help in timely diagnosis of the fatal disease with Convolutional Neural Networks (CNN) detecting Acute Lymphoblastic Leukemia (ALL) in patients by automatically analyzing microscopic blood cell images. Hence, this paper proposes a novel CNN-based approach employing the fine-tuned EfficientNet-B0 model enhanced using an optimally placed self-attention layer with HardSwish activation function. Experimentational analysis has also been done by employing self-attention layers at different architectural positions and with recent activation functions to develop an efficient and optimal model architecture. Several other CNN models such as MobileNet, EfficientNetB0, DenseNet, NASNet, InceptionV3, and Xception have also been trained to classifying blood cell images into normal and ALL classes for comparison purposes. The proposed model outperformed all other techniques with a huge margin of 5% and had the highest accuracy of 86.85%, precision of 87%, recall of 87%, and F1-Score of 87%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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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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