Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2944
Probodh Narayan Gour, Mohd. Faheem Khan
Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN
{"title":"Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches","authors":"Probodh Narayan Gour, Mohd. Faheem Khan","doi":"10.17485/ijst/v17i7.2944","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2944","url":null,"abstract":"Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i8.3151
Mamta Patel, Mehul Shah
Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest Xrays (CXR), Lung diseases
研究目的本研究旨在利用深度学习开发一种稳健的医疗识别系统，用于从胸部 X 光图像中识别各种肺部疾病，包括 COVID-19、肺炎、肺不张和正常状态。重点是采用集合固定特征学习方法来提高诊断能力，从而开发出一种具有成本效益且可靠的诊断工具，以应对肺部疾病在全球的流行。研究方法该研究利用了包含 COVID-19 胸部放射影像的 Kaggle 数据集。对原始 X 光图像进行预处理，以增强对比度和去除噪音，同时通过近似错误重采样来解决数据集的不平衡问题。采用了包括两级和三级方法在内的集合学习技术，以利用单个基础学习器-VGG16、InceptionV3 和 MobileNetV2 的优势。该模型的性能使用准确率、召回率、精确度和 F1 分数等指标进行评估。为实现远程访问，使用 Python Gradio 开发了用户界面和共享网络链接。研究结果在两级集合中，基础学习者的特征被串联起来，并使用支持向量机进行分类。三级集合使用由三个机器学习分类器分类的串联特征，并采用多数投票系统进行最终预测。两级方法的准确率、精确度、召回率和 F1 分数均达到 93%。三级集合模型表现优异，在检测四种肺部疾病（即 COVID-19、肺炎、肺不张和正常状态）方面达到了 94% 的准确率。新颖性：这项研究展示了深度学习技术，尤其是集合学习，在增强从原始胸部 X 光图像检测肺部疾病方面的功效，为该领域做出了贡献。该模型采用三个经过修改的高效预训练网络进行自动特征提取，无需人工特征工程。所开发的模型可作为医疗保健专业人员的决策支持工具，尤其是在资源匮乏的环境中。关键词卷积神经网络（CNN）、深度学习（DL）、迁移学习（TL）、集合学习（EL）、固定特征提取、胸部 X 光片（CXR）、肺部疾病
{"title":"Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images","authors":"Mamta Patel, Mehul Shah","doi":"10.17485/ijst/v17i8.3151","DOIUrl":"https://doi.org/10.17485/ijst/v17i8.3151","url":null,"abstract":"Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest Xrays (CXR), Lung diseases","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"197 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140455667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2766
Ashwani Gupta, Utpal Sharma
Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi
{"title":"Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi","authors":"Ashwani Gupta, Utpal Sharma","doi":"10.17485/ijst/v17i7.2766","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2766","url":null,"abstract":"Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"494 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2497
R. O. Gayathri, R. Hemavathy
Objectives: To prove the fixed point theorems for non-self mappings using disconnected graphs. Method: Graph theoretical approach is adopted to prove the fixed point theorems for non-self mappings. In all the previous works, connected graphs were used for establishing the results, but it is demonstrated in this work that disconnected graphs are best suited, and this new approach simplifies the proofs to a greater extent. Findings: The fixed point theorems by Banach, Kannan, Chatterjea, and Bianchini are proved using the new methodology. Novelty: An important part of the results concerning fixed point theorems is proving the iterated sequence to be a Cauchy sequence, and this is amalgamated with the edge sequence of the disconnected graph. Subject Classification: 54H25, 47H10 Keywords: Non-self mapping, Iterated sequence, Disconnected graph, Edge sequence, Fixed point
{"title":"Fixed Point Theorems for Non-self Mappings Using Disconnected Graphs","authors":"R. O. Gayathri, R. Hemavathy","doi":"10.17485/ijst/v17i7.2497","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2497","url":null,"abstract":"Objectives: To prove the fixed point theorems for non-self mappings using disconnected graphs. Method: Graph theoretical approach is adopted to prove the fixed point theorems for non-self mappings. In all the previous works, connected graphs were used for establishing the results, but it is demonstrated in this work that disconnected graphs are best suited, and this new approach simplifies the proofs to a greater extent. Findings: The fixed point theorems by Banach, Kannan, Chatterjea, and Bianchini are proved using the new methodology. Novelty: An important part of the results concerning fixed point theorems is proving the iterated sequence to be a Cauchy sequence, and this is amalgamated with the edge sequence of the disconnected graph. Subject Classification: 54H25, 47H10 Keywords: Non-self mapping, Iterated sequence, Disconnected graph, Edge sequence, Fixed point","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"240 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2944
Probodh Narayan Gour, Mohd. Faheem Khan
Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN
{"title":"Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches","authors":"Probodh Narayan Gour, Mohd. Faheem Khan","doi":"10.17485/ijst/v17i7.2944","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2944","url":null,"abstract":"Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"180 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i8.2464
D. Rajendiran, S. Karthikayini, K. Veeramuthu, N. Harikrishnan
Objectives: This research focuses on the determination of the natural radionuclides radium, thorium, and potassium in the twenty-six sediment samples collected at the sea, beach, and creek regions of Ennore Port. Methods: The activity concentrations of 226Ra, 232Th, and 40K were determined using gamma ray spectrometry with a high-purity germanium (HPGe) detector. Findings: The average activity concentrations of 226Ra, 232Th, and 40K were in the descending order of 40K (397.58 Bq kg-1) > 232Th (65.83 Bq kg-1) > 226Ra (18.28 Bq kg-1). The estimated average values of radiological parameters such as radium equivalent activity (143.04 Bq kg-1), absorbed dose rate (64.91 nGy h-1), annual effective dose equivalent (0.32 mSv y-1), and external hazard index (0.39) were lower than the respective world average values, reported by United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2000). Moreover, the representative level index and annual gonadal dose equivalent were slightly higher than the world average value. Hence, this research proved that the study area is radiologically safe for humans and the environment. Novelty: A location and sample collection-based novelty is approached to carried out the work. Sea sediments were also collected along with samples from creek and beach regions in order to examine the dispersion of natural radionuclides from land to marine environments. The samples from the beach and creek regions were collected using a Peterson grab sampler. Especially in the sea region, the samples were collected using a Van Veen grab sampler at a depth of 4 m and a distance of 10 m parallel to the shoreline. Keywords: Natural radioactivity, Sediment, Ennore, Gamma ray spectrometry, HPGe detector, Radiological parameters
{"title":"Gamma Ray Spectroscopy Analysis of Sediments of Coastal Areas in Ennore, Tamil Nadu","authors":"D. Rajendiran, S. Karthikayini, K. Veeramuthu, N. Harikrishnan","doi":"10.17485/ijst/v17i8.2464","DOIUrl":"https://doi.org/10.17485/ijst/v17i8.2464","url":null,"abstract":"Objectives: This research focuses on the determination of the natural radionuclides radium, thorium, and potassium in the twenty-six sediment samples collected at the sea, beach, and creek regions of Ennore Port. Methods: The activity concentrations of 226Ra, 232Th, and 40K were determined using gamma ray spectrometry with a high-purity germanium (HPGe) detector. Findings: The average activity concentrations of 226Ra, 232Th, and 40K were in the descending order of 40K (397.58 Bq kg-1) > 232Th (65.83 Bq kg-1) > 226Ra (18.28 Bq kg-1). The estimated average values of radiological parameters such as radium equivalent activity (143.04 Bq kg-1), absorbed dose rate (64.91 nGy h-1), annual effective dose equivalent (0.32 mSv y-1), and external hazard index (0.39) were lower than the respective world average values, reported by United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2000). Moreover, the representative level index and annual gonadal dose equivalent were slightly higher than the world average value. Hence, this research proved that the study area is radiologically safe for humans and the environment. Novelty: A location and sample collection-based novelty is approached to carried out the work. Sea sediments were also collected along with samples from creek and beach regions in order to examine the dispersion of natural radionuclides from land to marine environments. The samples from the beach and creek regions were collected using a Peterson grab sampler. Especially in the sea region, the samples were collected using a Van Veen grab sampler at a depth of 4 m and a distance of 10 m parallel to the shoreline. Keywords: Natural radioactivity, Sediment, Ennore, Gamma ray spectrometry, HPGe detector, Radiological parameters","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"34 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140455089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i8.3195
C. Kayelvizhi, A. Pushpam
Objectives: The main objectives of this work are to solve Neutral Delay Differential Equations (NDDEs) using Galerkin weighted residual method based on successive integration technique and to obtain the Estimation of Error using Residual function. Methods: The Galerkin weighted residual method based on successive integration technique is proposed to obtain approximate solutions of the NDDEs. In this study, the most widely used classical orthogonal polynomials, namely, the Bernoulli polynomials, the Chebyshev polynomials, the Hermite polynomials, and the Fibonacci polynomials are considered. Findings: Numerical examples of linear and nonlinear NDDEs have been considered to demonstrate the efficiency and accuracy of the method. Approximate solutions obtained by the proposed method are well comparable with exact solutions. Novelty: From the results it is observed that the accuracy of the numerical solutions by the proposed method increases as N increases. The proposed method is very effective, simple, and suitable for solving the linear and nonlinear NDDEs in real-world problems. Keywords: Galerkin Weighted Residual method, Polynomials, Hermite, Bernoulli, Chebyshev, Fibonacci, Successive integration technique, Neutral Delay Differential Equations
{"title":"Solving Neutral Delay Differential Equations Using Galerkin Weighted Residual Method Based on Successive Integration Technique and its Residual Error Correction","authors":"C. Kayelvizhi, A. Pushpam","doi":"10.17485/ijst/v17i8.3195","DOIUrl":"https://doi.org/10.17485/ijst/v17i8.3195","url":null,"abstract":"Objectives: The main objectives of this work are to solve Neutral Delay Differential Equations (NDDEs) using Galerkin weighted residual method based on successive integration technique and to obtain the Estimation of Error using Residual function. Methods: The Galerkin weighted residual method based on successive integration technique is proposed to obtain approximate solutions of the NDDEs. In this study, the most widely used classical orthogonal polynomials, namely, the Bernoulli polynomials, the Chebyshev polynomials, the Hermite polynomials, and the Fibonacci polynomials are considered. Findings: Numerical examples of linear and nonlinear NDDEs have been considered to demonstrate the efficiency and accuracy of the method. Approximate solutions obtained by the proposed method are well comparable with exact solutions. Novelty: From the results it is observed that the accuracy of the numerical solutions by the proposed method increases as N increases. The proposed method is very effective, simple, and suitable for solving the linear and nonlinear NDDEs in real-world problems. Keywords: Galerkin Weighted Residual method, Polynomials, Hermite, Bernoulli, Chebyshev, Fibonacci, Successive integration technique, Neutral Delay Differential Equations","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"373 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140455563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2497
R. O. Gayathri, R. Hemavathy
Objectives: To prove the fixed point theorems for non-self mappings using disconnected graphs. Method: Graph theoretical approach is adopted to prove the fixed point theorems for non-self mappings. In all the previous works, connected graphs were used for establishing the results, but it is demonstrated in this work that disconnected graphs are best suited, and this new approach simplifies the proofs to a greater extent. Findings: The fixed point theorems by Banach, Kannan, Chatterjea, and Bianchini are proved using the new methodology. Novelty: An important part of the results concerning fixed point theorems is proving the iterated sequence to be a Cauchy sequence, and this is amalgamated with the edge sequence of the disconnected graph. Subject Classification: 54H25, 47H10 Keywords: Non-self mapping, Iterated sequence, Disconnected graph, Edge sequence, Fixed point
{"title":"Fixed Point Theorems for Non-self Mappings Using Disconnected Graphs","authors":"R. O. Gayathri, R. Hemavathy","doi":"10.17485/ijst/v17i7.2497","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2497","url":null,"abstract":"Objectives: To prove the fixed point theorems for non-self mappings using disconnected graphs. Method: Graph theoretical approach is adopted to prove the fixed point theorems for non-self mappings. In all the previous works, connected graphs were used for establishing the results, but it is demonstrated in this work that disconnected graphs are best suited, and this new approach simplifies the proofs to a greater extent. Findings: The fixed point theorems by Banach, Kannan, Chatterjea, and Bianchini are proved using the new methodology. Novelty: An important part of the results concerning fixed point theorems is proving the iterated sequence to be a Cauchy sequence, and this is amalgamated with the edge sequence of the disconnected graph. Subject Classification: 54H25, 47H10 Keywords: Non-self mapping, Iterated sequence, Disconnected graph, Edge sequence, Fixed point","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"63 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2748
A. Mehta, Kajal S Patel
Objectives: Dengue fever, a mosquito-borne viral disease, is particularly prevalent in tropical regions like India. Gujarat State is also one of them. Forecasting outbreaks of diseases such as dengue can prove important for public health management. The purpose of this study is to predict dengue cases in ten districts of Gujarat using the LSTM machine learning model. And if people are aware of this from the beginning, the spread of dengue can be prevented. Methods: This approach uses LSTM models to predict dengue cases using a total of 10 years (2010 to 2019) of data. From this data, data from 2010 to 2016 is used for training and data from 2017 to 2019 is used for testing. To predict dengue cases, population density, average temperature, average humidity, monthly rainfall, dengue cases with lag of one, two and twelve months. Findings: The LSTM model was applied with different parameter configurations, showing the following results: The root mean square error value is 0.04, and the R-squared (R2) score is 0.84. Many machine learning methods, like ANN, linear regression, random forest, etc., have been used to predict dengue cases in different states and countries. LSTM model gives the best results in terms of accuracy. Previously reported dengue cases, population density, and total monthly rainfall proved to be the most effective predictors of dengue in the state of Gujarat. Novelty: Models have been developed to predict dengue outbreaks in many other countries and states. The LSTM model is developed for the first time in this study for the state of Gujarat. 84% accuracy is obtained from the model. This model has been prepared by collecting environmental data and registered dengue cases in Gujarat state. Keywords: Dengue Cases Predictions, Artificial Intelligence in Healthcare, LSTM Algorithm, Disease Outbreaks, Public Health Management
{"title":"LSTM-based Forecasting of Dengue Cases in Gujarat: A Machine Learning Approach","authors":"A. Mehta, Kajal S Patel","doi":"10.17485/ijst/v17i7.2748","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2748","url":null,"abstract":"Objectives: Dengue fever, a mosquito-borne viral disease, is particularly prevalent in tropical regions like India. Gujarat State is also one of them. Forecasting outbreaks of diseases such as dengue can prove important for public health management. The purpose of this study is to predict dengue cases in ten districts of Gujarat using the LSTM machine learning model. And if people are aware of this from the beginning, the spread of dengue can be prevented. Methods: This approach uses LSTM models to predict dengue cases using a total of 10 years (2010 to 2019) of data. From this data, data from 2010 to 2016 is used for training and data from 2017 to 2019 is used for testing. To predict dengue cases, population density, average temperature, average humidity, monthly rainfall, dengue cases with lag of one, two and twelve months. Findings: The LSTM model was applied with different parameter configurations, showing the following results: The root mean square error value is 0.04, and the R-squared (R2) score is 0.84. Many machine learning methods, like ANN, linear regression, random forest, etc., have been used to predict dengue cases in different states and countries. LSTM model gives the best results in terms of accuracy. Previously reported dengue cases, population density, and total monthly rainfall proved to be the most effective predictors of dengue in the state of Gujarat. Novelty: Models have been developed to predict dengue outbreaks in many other countries and states. The LSTM model is developed for the first time in this study for the state of Gujarat. 84% accuracy is obtained from the model. This model has been prepared by collecting environmental data and registered dengue cases in Gujarat state. Keywords: Dengue Cases Predictions, Artificial Intelligence in Healthcare, LSTM Algorithm, Disease Outbreaks, Public Health Management","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"426 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.17485/ijst/v17i7.2670
Rinkal Shah, Jyoti Pareek
Objectives: To develop a deep learning method using camera images that can effectively detect the preliminary phase of oral cancer, which has a high rate of morbidity and mortality and is a significant public health concern. If left untreated, it can result in severe deformities and negatively affect the patient's quality of life, both physically and mentally. Early detection is crucial owing to the rapid spread of the disease, where biopsy is the only option left. Therefore, it is essential to identify malignancies swiftly to prevent disease progression non-invasively. Methods: Three different scenarios are used in this study to analyze samples: CNN architecture, stratified K-fold validation, and transfer learning. For automated disease identification on binary datasets (normal vs. ulcer) and multiclass datasets (normal vs. ulcer vs. Leukoplakia), camera images are pre-processed with data augmentation. As a feature extractor in the model, transfer learning is used with pre-defined networks such as VGG19, InceptionNET, EfficientNET, and MobileNET weights. Findings: Using the proposed CNN architecture, the F1 score for image classification was 78% and 74% for photos showing hygienic mouths or ulcers, and 83%, 87%, and 84% for images showing normal mouths, ulcers, and leukoplakia. Using stratified 3-fold validation, the results were improved to 97%, and an EfficientNET achieves the highest results in a binary F1 score of 98% and a classification with multiple classes F1 scores of 98%, 87%, and 91%, respectively. Novelty: Previous studies have mostly concentrated on differentiating oral potentially malignant diseases (OPMD) from oral squamous cell carcinoma (OSCC) or on discriminating between cancerous and non-cancerous tissues. The objective is to diagnose patients with non-invasive procedures to classify ulcers, healthy mouths, or precancerous type "Leukoplakia" without requiring them to visit a doctor. Keywords: CNN, Transfer Learning, Oral Cancer, Ulcer, Leukoplakia, Stratified K-fold validation
目的开发一种利用摄像头图像的深度学习方法，该方法可有效检测口腔癌的初期阶段，口腔癌的发病率和死亡率都很高，是一个重大的公共卫生问题。如果不及时治疗，口腔癌会导致严重畸形，并对患者的身心生活质量造成负面影响。由于疾病传播迅速，活检是唯一的选择，因此早期发现至关重要。因此，必须迅速识别恶性肿瘤，以非侵入性的方式防止疾病恶化。方法：本研究采用了三种不同的方案来分析样本：CNN 架构、分层 K 折验证和迁移学习。为了在二元数据集（正常 vs. 溃疡）和多类数据集（正常 vs. 溃疡 vs. 白斑病）上自动识别疾病，对摄像头图像进行了数据增强预处理。作为模型中的特征提取器，迁移学习使用了预先定义的网络，如 VGG19、InceptionNET、EfficientNET 和 MobileNET 权重。研究结果使用提出的 CNN 架构，对显示卫生口腔或溃疡的照片进行图像分类的 F1 分数分别为 78% 和 74%，对显示正常口腔、溃疡和白斑病的图像进行分类的 F1 分数分别为 83%、87% 和 84%。通过分层 3 倍验证，结果提高到 97%，EfficientNET 的二元 F1 得分达到 98%，多类分类 F1 得分分别为 98%、87% 和 91%，取得了最高成绩。新颖性：以往的研究大多集中于区分口腔潜在恶性疾病（OPMD）和口腔鳞状细胞癌（OSCC），或区分癌组织和非癌组织。本研究的目的是通过非侵入性程序对患者进行诊断，对溃疡、健康口腔或癌前病变类型 "白斑病 "进行分类，而无需患者就医。关键词CNN、迁移学习、口腔癌、溃疡、白斑病、分层 K 倍验证
{"title":"Non-invasive Primary Screening of Oral Lesions into Binary and Multi Class using Convolutional Neural Network, Stratified K-fold Validation and Transfer Learning","authors":"Rinkal Shah, Jyoti Pareek","doi":"10.17485/ijst/v17i7.2670","DOIUrl":"https://doi.org/10.17485/ijst/v17i7.2670","url":null,"abstract":"Objectives: To develop a deep learning method using camera images that can effectively detect the preliminary phase of oral cancer, which has a high rate of morbidity and mortality and is a significant public health concern. If left untreated, it can result in severe deformities and negatively affect the patient's quality of life, both physically and mentally. Early detection is crucial owing to the rapid spread of the disease, where biopsy is the only option left. Therefore, it is essential to identify malignancies swiftly to prevent disease progression non-invasively. Methods: Three different scenarios are used in this study to analyze samples: CNN architecture, stratified K-fold validation, and transfer learning. For automated disease identification on binary datasets (normal vs. ulcer) and multiclass datasets (normal vs. ulcer vs. Leukoplakia), camera images are pre-processed with data augmentation. As a feature extractor in the model, transfer learning is used with pre-defined networks such as VGG19, InceptionNET, EfficientNET, and MobileNET weights. Findings: Using the proposed CNN architecture, the F1 score for image classification was 78% and 74% for photos showing hygienic mouths or ulcers, and 83%, 87%, and 84% for images showing normal mouths, ulcers, and leukoplakia. Using stratified 3-fold validation, the results were improved to 97%, and an EfficientNET achieves the highest results in a binary F1 score of 98% and a classification with multiple classes F1 scores of 98%, 87%, and 91%, respectively. Novelty: Previous studies have mostly concentrated on differentiating oral potentially malignant diseases (OPMD) from oral squamous cell carcinoma (OSCC) or on discriminating between cancerous and non-cancerous tissues. The objective is to diagnose patients with non-invasive procedures to classify ulcers, healthy mouths, or precancerous type \"Leukoplakia\" without requiring them to visit a doctor. Keywords: CNN, Transfer Learning, Oral Cancer, Ulcer, Leukoplakia, Stratified K-fold validation","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"649 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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