Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies

Agarwal, Nidhi; Jain, Amita; Gupta, Ayush; Tayal, Devendra K.

doi:10.1007/978-3-030-95711-7_33

Cited by 14 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study by delved into the application of the XGBoost machine learning model for detecting exoplanets in distant galaxies. While seemingly unrelated to PD, this research showcases the versatility of machine learning models, which can potentially be adapted to various domains, including medical diagnosis [2]. Agarwal This approach offers a holistic view of disease assessment [12].…”

Section: Literature Surveymentioning

confidence: 99%

“…OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1][2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3][4].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition. OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4]. RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. . CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

show abstract

Section: Literature Surveymentioning

confidence: 99%

mentioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, a number of researchers have looked into how machine learning algorithms may be used to increase the precision of transit and radial velocity approaches. For instance, researchers in [6][7] employed machine learning to enhance the radial velocity method's exoplanet discovery. The scientists properly predicted the presence of exoplanets by modelling the signals from exoplanets and stars using a machine learning approach known as Gaussian process regression.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

Singh,

Agarwal,

Yadav

2024

EAI Endorsed Trans IoT

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: Exoplanet exploration outside of our solar system has recently attracted attention among astronomers worldwide. The accuracy of the currently used detection techniques, such as the transit and radial velocity approaches is constrained. Researchers have suggested utilizing machine learning techniques to create a prediction model to increase the identification of exoplanets beyond our milky way galaxy. OBJECTIVES: The novel method proposed in this research paper builds a prediction model using a dataset of known exoplanets and their characteristics, such as size, distance from the parent star, and orbital period. The model is then trained using this data based on machine learning methods that Support Vector Machines and Random Forests. METHODS: A different dataset of recognized exoplanets is used to assess the model’s accuracy, and the findings are compared with in comparison to accuracy rates of the transit and radial velocity approaches. RESULTS: The prediction model created in this work successfully predicts the presence of exoplanets in the test data-set with an accuracy rate of over 90 percent. CONCLUSION: This discovery shows the promise and confidence of machine learning techniques for exoplanet detection.

show abstract

“…learning is that branch of AI (Artificial Intelligence) that lets computers in learning from various raw pieces of information, see repeats, and generate judgements[7][8][9]. Machine learning can evaluate huge datasets of various other domains in addition to retinal images like hunting exoplanets, various other prediction and classification-based systems[10- 19].…”

mentioning

confidence: 99%

Diabetic Retinopathy Eye Disease Detection Using Machine Learning

Dahiya,

Agarwal,

Singh

et al. 2024

EAI Endorsed Trans IoT

Self Cite

View full text Add to dashboard Cite

INTRODUCTION: Diabetic retinopathy is the name given to diabetes problems that harm the eyes. Its root cause is damage to the blood capillaries in the tissue that is light-sensitive in the rear of the eye. Over time, having excessive blood sugar may cause to the tiny blood capillaries that nourish the retina to become blocked, severing the retina's blood circulation. As a result, the eye tries to develop new blood arteries. OBJECTIVES: The objective of this research is to analyse and compare various algorithms based on their performance and efficiency in predicting Diabetic Retinopathy. METHODS: To achieve this, an experimental model was developed to predict Diabetic Retinopathy at early stage. RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of various algorithm selection and its impact on the overall performance of models. CONCLUSION: The findings of this study contribute to the understanding of multiple algorithm selection and its impact on the overall performance of models’ accuracy. By applying these algorithms, we can predict disease at early stage such that it can be cured efficiently before it goes worse.

show abstract

Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies

Cited by 14 publications

References 9 publications

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

A Novel Methodology for Hunting Exoplanets in Space Using Machine Learning

Diabetic Retinopathy Eye Disease Detection Using Machine Learning

Contact Info

Product

Resources

About