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Leveraging Machine Learning to Predict Obesity Trends: Insights from Lifestyle and Biometric Data

Leveraging Machine Learning to Predict Obesity Trends: Insig...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Adapala, Rishi Manikanta Kancharla, Yaswanth Shaik, Baba Ameer Jyotsna, C. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bangalore India

ISBN: (纸本)9798331512965

This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obesity trends given lifestyle and biometric data from a large obesity dataset. Our methodology starts with data preprocessing that includes transforming categorical variables into numerical variables and standardizes the numerical variables into such a form so that machine learning algos can work with them properly. Supervised machine learning models, such as tree based methods, neural networks and various ensemble techniques are applied, and evaluated. For determining the most effective predictive approach, these models are evaluated by several performance metrics such as accuracy, precision, recall, and F1 score;the best model is chosen based on these metrics. In addition, the study includes explainable AI technique (SHAP - SHapley Additive exPlanations;LIME - Local Interpretable Model-Agnostic Explanations) to make the results more interpretable. The results from these methods provide insight into how different lifestyle and biometric factors contribute to the prediction of obesity;such as physical activity, diet patterns, and body mass index. The results from this work demonstrate not only how machine learning can improve prediction accuracy but also how these methods can provide actionable insights into the many contributing factors to obesity. © 2025 IEEE.

关键词： Self-supervised learning

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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...

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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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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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Speech Emotion Recognition Using Machine Learning 5th

Speech Emotion Recognition Using Machine Learning

引用

5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Veeramaneni, Asritha Samitha, V. Charitha, Talluri Shriya, Sagi Singh, Tripty Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

Speech emotion recognition (SER) is a technique for accurately determining a person’s emotion from their speech. Enhancing communication between people and machines. Even though emotions are arbitrary, it can be hard to annotate audio and it’s still hard to gauge someone’s emotional state. It now plays a crucial part in technological research as new technologies for human machine or medical applications are developed. It is obvious that surprise and rage feelings are more accurately predicted by our model than other emotions, It is understandable given how significantly the pitch, speed, and other qualities of the audio files for these emotions differ from those of other audio files. The system can capture and analyze various acoustic elements contained in voice signals using the SVM-based architecture and enabling the identification and categorization of various moods. Mel frequency cepstral coefficients (MFCCs), pitch, energy, and spectral features are some of these features, and they all work together to reveal important details about the emotional content that is present in speech. The deployed models attain a remarkable accuracy of 80% in identifying various emotional states from speech data after extensive training and optimization. This level of accuracy shows how well the suggested method works in properly classifying emotions based on auditory characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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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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2D Wound Image Segmentation Using Deep Learning Algorithms 2

2D Wound Image Segmentation Using Deep Learning Algorithms

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sivakumar, Soubraylu Abhiram, V.S.V. Rama, P. Kallakrinda, Vikram Kumar, T. Rajesh Saravanan, P. Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331541217

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks;they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of 92.3%. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future. © 2024 IEEE.

关键词： Image segmentation

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A Blockchain Based Decentralized Application System for Vanet FDIA Detection 3rd

A Blockchain Based Decentralized Application System for Vane...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Preeti, Grover Sanjeev Kumar, Prasad School of Computing Science and Engineering Galgotias University Uttar Pradesh NCR Greater Noida India

ISBN: (纸本)9789819726707

The growing prominence of intelligent transportation systems (ITS) has led to increased research on vehicular ad hoc networks (VANETs). Blockchain technology offers promising solutions for ensuring data security, integrity, and immutability within VANETs. However, VANETs remain vulnerable to false data injection attacks (FDIAs), which can significantly disrupt network operations and endanger safety. This paper proposes a novel approach using a blockchain-based decentralized application system (Dapp) for FDIA detection in VANETs. The Dapp leverages smart contracts to manage crucial aspects like data provenance, reputation, and consensus mechanisms. It uses POS consensus algorithm to guarantee data consistency and fault tolerance even in the presence of malicious nodes. To manage the vehicular data, users can access the system via Dapp, an Ethereum—distributed application. By integration of blockchain-based IPFS-Dapps system and AI/ML/big data model with blockchain are aim of the research article for providing the solutions for false data injection attacks (FDIA). We collect the data through Radio Channels, Smart Sensors, Smart Antennas and RSU and than uploaded using Dapp and than we classified nodes from smart contract and finally send it to Ethereum. We used a machine learning SVR algorithm, i.e., vehicular network-based support vector regression (VBSVR) to identify an attacker node. We have written the pseudocode for using IPFS with Ethereum, proposed research methodology includes integration of blockchain and Dapps with smart contract, integration of AI/ML/big data models with blockchain, and integration of ML Oracle under blockchain that are ideal for detection of false injection attacks nodes, and same is logged in IPFS Decentralized Server. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethereum

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Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with Random Forest and kNN for Strategic Job Allocation Insights

Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with ...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： George, Angelina Beena, B.M. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Data centers are vital to modern computing but contribute significantly to carbon emissions due to their high energy consumption. To address this, machine learning models can optimize resource allocation by analyzing sensor metrics. In this study, the Random Forest Regressor was chosen for its ability to capture sensor behavior, even showing negative R2 values, aligning with observed patterns. Meanwhile, k-Nearest Neighbors (kNN) emerged as the best model for learning from the same data, despite irregularities, with R2 values ranging between 0.6 and 1. This analysis supports sustainable data center management by enhancing energy efficiency and minimizing environmental impact. Future work will explore the integration of these models into simulation environments. © 2025 IEEE.

关键词： Carbon sequestration

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Advanced Financial Sentiment Analysis using FinBERT to Explore Sentiment Dynamics 3

Advanced Financial Sentiment Analysis using FinBERT to Explo...

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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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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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