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Enhanced Classification of Exoplanets Using Machine Learning: Integrating Feature Selection, Ensemble Algorithms, and Explainable AI Insights

Enhanced Classification of Exoplanets Using Machine Learning...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Ishvarya G Kavya Sree Kammari Sharanya Vanraj Thambi Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research delves into employing machine learning (ML) algorithms for classifying exoplanets, focusing on enhancing classification accuracy through feature selection. By utilizing data from the KOI planet candidates, the study addresses the challenge of handling large datasets by employing feature selection techniques like the chi-square method, improving the classification process’s effectiveness. Principal Component Analysis (PCA) was employed for data visualization, aiding in understanding the underlying structure and relationships within the exoplanet dataset. In the implementation phase, the study initially employed standalone classifier models such as KNN, SVM, Random Forest, Naive Bayes, Decision trees and CNN. Following this, ensemble algorithms including XGBoost, GBM, CatBoost, and AdaBoost were utilized to enhance classification performance further. Among these, Random Forest emerged as the top performer, achieving an F-measure of 0.95, surpassing the others. Additionally, the study incorporates explainable AI (XAI) techniques to interpret the importance of individual features in the models’ predictions, shedding light on crucial features essential for accurately classifying KOI planets. These insights serve as a foundation for developing more advanced and dependable ML models for exoplanet classification, facilitating future research endeavors that may incorporate additional datasets and features.

关键词： Machine learning algorithms Planets Extrasolar planets Predictive models Nearest neighbor methods Feature extraction Prediction algorithms Classification algorithms Random forests Principal component analysis

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Enhancing Parkinson's Disease Diagnosis using Speech Analysis:A Feature Subset Selection Approach with LIME and SHAP

Enhancing Parkinson's Disease Diagnosis using Speech Analysi...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Sai Santhoshi Haneesha Samudrala Jaishitha Thambi Sai Rishisri Vadluri Anand Mahalingam Peeta Basa Pati Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Parkinson’s disease (PD) is a progressive neurological disorder that affects movement, posture, hand writing and speech. This disease often manifests speech related symptoms, including reduced vocal intensity, articulatory imprecision, making speech analysis a potential diagnostic tool. This paper focus on speech analysis of individuals to diagnose PD using different classifiers such as Logistic Regression (LR), Gradient Boosting classifier (GBC), Random Forest (RF), SVM, Decision Tree (DT), AdaBoost. SHAP and LIME are implemented toevaluate each feature’s significance. It’s observed that the optimal subset with 7 important features, identified with SHAP, an accuracy of 98% is attained.

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A Speech2Text based Recognizer and Probabilistic Parser Application for TeluguSentences

A Speech2Text based Recognizer and Probabilistic Parser Appl...

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International Conference on computing and Networking Technology (ICCNT)

作者： Surisetty Hima Varshini Gottimukkala Sarayu Varma Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Telugu, the largest Dravidian language, is one of the Indian languages that is predominantly spoken in Andhra Pradesh, Telangana and a few parts of southern India. The proposed work presents a speech-to-text based recognizer cum parser for some simple sentences in Telugu which are commonly used in day-to-day conversations. The Probabilistic Cocke-Younger-Kasami (P-CYK) algorithm has been employed in order to recognize these simple sentences and generate the corresponding probabilistic parse for these sentences. These parses will be useful in gaining a better understanding about the grammatical structure of a given sentence. At present, there are not many linguistic processing methodologies which are integrated with speech-based inputs for regional languages like Telugu. Hence, an effort has been made in order to efficiently use speech as the form of input and obtain the parse for some basic sentences with the corresponding probability of that parse. The probabilistic parser that has been developed in this work takes speech in Telugu as the input and identifies both grammatically correct and incorrect sentences and produce the probabilistic parse of the grammatically correct sentences. This system was then assessed by making use of the metrics like accuracy score, precision score and recall score. Also, a user interface was developed using Streamlit for the same.

关键词： Measurement Accuracy Oral communication User interfaces Linguistics Probabilistic logic Speech to text

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Vehicle Detection in Unmanned Aerial Imagery Through Advance You Only Look Once Architectures

Vehicle Detection in Unmanned Aerial Imagery Through Advance...

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International Conference on computing and Networking Technology (ICCNT)

作者： U Nishitha V Lokesh Tata Kaushik Rimjhim Padam Singh Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The detection of vehicles by unmanned aerial vehicles (UAVs) is gaining substantial attention in the field of traffic control. In aerial images, vehicles have a unique perspective and take up less pixels than other objects in the broader data set.. This study uses the YOLOv8 (You Only Look Once version 8) deep learning architecture to propose an enhanced method of vehicle recognition in aerial photos. This work goes beyond the conventional YOLOv8 implementation by introducing new methods for data augmentation to enhance the efficiency and the model’s resiliency. To build a diverse and enriched dataset, the suggested methodology makes use of cutting-edge techniques such image cropping, contrast correction, and brightness improvement. Training the YOLOv8 model on this enhanced dataset produces encouraging results; on a different test set with ground truth annotations, a noteworthy achievement of ${7 0 \%}$ accuracy in vehicle detection was made. This accomplishment highlights the effectiveness of our strategy and shows a notable increase in accuracy over earlier approaches. The study demonstrates the potential of YOLOv8 in tackling the difficulties of aerial vehicle detection and highlights the crucial role that data augmentation plays in improving model performance and resilience.

关键词： YOLO Accuracy Traffic control User interfaces Autonomous aerial vehicles Data augmentation Real-time systems Data models Vehicle dynamics Resilience

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Extrapolative Preservation Management of Medical Equipment through IoT 1

Extrapolative Preservation Management of Medical Equipment t...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tripathi, Rajeev Mishra, Vinay Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gupta, Ashulekha School of Management Sciences Department of Computer Application and Sciences Lucknow India School of Computer Application Babu Banarasi das University Lucknow India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations. © 2023 IEEE.

关键词： Failure Management Internet of things IoT Medical Equipment Maintenance Predictive Maintenance Sensors

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Contrastive Representation Learning for Multimodal Single-Cell RNA-Seq Data Integration

Contrastive Representation Learning for Multimodal Single-Ce...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Vishnu Vardhan Kosuru Sree Virajitha Ramaraju Sri Harshitha Anatatmula T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

MultiVI, a model for multimodal single-cell data generation, alongside an SSL contrastive learning pipeline is the foundation of this research. The combination approach promotes better learning of the structural and contextual representations of the single cell data and improve its downstream tasks like cell typing and the identification of the relationship to other variables such as genes. Here, the algorithm applies the contrastive loss function into the MultiVI training loop, which multitasking performance shows the value of the approach for forecasting one modality from another. The results evidence reveals a lot about cellular differentiation and regulation of gene expression. This work combines MultiVI, which is a generative model for multi-modal analysis of single-cell data, and a self-supervised contrastive learning approach. It helps to make precise prognoses and to explain the processes of cellular differentiation and gene regulation. The study falls under one of the biggest challenges in the biology of single cells and it stands a good chance to change how cellular biology, diagnostics of diseases included will be done.

关键词： Proteins Deep learning Training Biological system modeling DNA Contrastive learning Stem cells Regulation Data models Gene expression

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Design and Implementation of a Movie Rental Website Using Automata Theory

Design and Implementation of a Movie Rental Website Using Au...

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Global Conference for Advancement in Technology (GCAT)

作者： Abhinav Saluja Amitesh Panda Suhas Kesavan Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This paper proposes a novel approach to designing a movie rental website using concepts from push down automata theory. The objective is to create a user-friendly platform for browsing, selecting, and renting movies while leveraging the computational power of push down automata (PDA) to manage the underlying logic efficiently. The system architecture employs push down automata to model various states and transitions in the movie rental process. Users can interact with the website through a GUI, where they can search for movies, view their details and begin rental transactions. Behind the scenes, push down automata handles the validation of user inputs and enforces business rules such as rental duration and payment processing. Push down automata is utilized to represent the different states of a movie’s availability, including available for rent, items in cart, currently rented, and returned. Transitions between these states are triggered by user actions such as renting a movie, returning it, or extending the rental period. By modeling these states and transitions with push down automata, the system ensures consistency and correctness in the rental process. Overall, this project demonstrates the practical application of push down automata theory in the development of a movie rental website, offering a scale-able and efficient solution for managing movie rental operations while providing a seamless user experience.

关键词： Computational modeling Automata Systems architecture Motion pictures User experience Computational efficiency Logic Graphical user interfaces Business

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Recommendation System using Inference-based Graph Learning - Modeling and Analysis 2

Recommendation System using Inference-based Graph Learning -...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Bhaskaran, S. Hariharan, S. Veeramanickam, M.R.M. Bharathiraja, N. Pradeepa, K. Marappan, Raja Sastra Deemed University School of Computing Thanjavur India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India St. Michael College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665474160

Recently, the development of recommendation systems has been an emerging area where the target is the learner's preferences, and styles should be learned from past interactions to predict the ratings and recommendations of the new items. The significant critiques and challenges are some of the datasets are sparse and the bias in the ratings and popularity of the items that conventional recommenders can't efficiently process. This research constructs the graph model to merge the items and user interaction to enhance user representations. The new inference operator is designed to remove the bias factors and to draw inferences. The users and items are represented with high quality by constructing the semantic space that connects the users and the item's characteristics. The proposed model is simulated on real-world datasets, proving that the model produces significant outcomes better than existing methods. © 2022 IEEE.

关键词： Recommender systems

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Attention-Guided Deep Learning Framework For Movement Quality Assessment 48

Attention-Guided Deep Learning Framework For Movement Qualit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kanade, Aditya Sharma, Mansi Muniyandi, Manivannan Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India Indian Institute of Technology Madras Department of Applied Mechanics India

ISBN: (纸本)9781728163277

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores. © 2023 IEEE.

关键词： attention mechanism deep learning Movement Quality Assessment performance score generation transformer

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Integrated Linear Regression and Random Forest Framework for E-Commerce Price Prediction of Pre-owned Vehicle 1

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2nd International Conference on Trends in Electronics and Health Informatics, TEHI 2022

作者： Mishra, Amit Kumar Mallik, Saurav Sharma, Viney Paliwal, Shweta Ray, Kanad Department of Computer Science and Engineering Jain University Karnataka Bangalore India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA United States Department of Computer Science and Engineering Anand Engineering College Agra India School of Computing DIT University UK Dehradun India Department of Physics Electronics and Communication Amity University RJ Jaipur India

ISBN: (数字)9789819919161

ISBN: (纸本)9789819919154

The E-Commerce industry has taken the world in its stride. It has been growing at an exponential rate. This paper presents a machine learning-based model to predict the price of a used vehicle for selling and buying purposes. The model proposes two algorithms: Linear Regression and Random Forest Regression and draws a comparison between the two on the basis of standard performance measures. The uniqueness of this work is that it is not only capable to predict the price of a used vehicle but the model can also be extended with minimal effort for any kind of product across various spheres of the E-commerce industry. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic commerce

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