Pub Date : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403071
Thanh-Nghi Doan
This article studies machine learning techniques and factors that affect tourism demand to develop a predictive model for tourism demand in the coming years. The model was developed using the back-propagation neural network approach and expert knowledge for analyzing factors affecting tourist satisfaction. The data used in the study were collected over a ten-year period and comprised information on the local economic and social situation, as well as specialized tourism data. In addition, survey results evaluating tourism in An Giang province in 2019 were included. The study results demonstrate that the developed model has successfully captured the underlying patterns in the An Giang tourism data, enabling the prediction of the necessary tourism indicators for the future. The model achieved a high level of accuracy with an RSME of 0.04. Furthermore, our approach showed several advantages when compared to other classical statistical methods. Based on our research findings, we proposed policies to support businesses, planning, and management units in forecasting and investing in the development of tourism in each specific locality more effectively.
{"title":"A BACK-PROPAGATION NEURAL NETWORK WITH DELAY AND SHIFT WINDOW FOR TOURISM DEMAND FORECASTING","authors":"Thanh-Nghi Doan","doi":"10.21817/indjcse/2023/v14i3/231403071","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403071","url":null,"abstract":"This article studies machine learning techniques and factors that affect tourism demand to develop a predictive model for tourism demand in the coming years. The model was developed using the back-propagation neural network approach and expert knowledge for analyzing factors affecting tourist satisfaction. The data used in the study were collected over a ten-year period and comprised information on the local economic and social situation, as well as specialized tourism data. In addition, survey results evaluating tourism in An Giang province in 2019 were included. The study results demonstrate that the developed model has successfully captured the underlying patterns in the An Giang tourism data, enabling the prediction of the necessary tourism indicators for the future. The model achieved a high level of accuracy with an RSME of 0.04. Furthermore, our approach showed several advantages when compared to other classical statistical methods. Based on our research findings, we proposed policies to support businesses, planning, and management units in forecasting and investing in the development of tourism in each specific locality more effectively.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47446261","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}
In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.
{"title":"IMPACT OF COVID-19 ON BREAST CANCER SCREENING PROGRAM (BCSP) IN INDIA","authors":"Sashikanta Prusty, Sujit Kumar Dash, Srikanta Patnaik, Sushree Gayatri Priyadarsini Prusty","doi":"10.21817/indjcse/2023/v14i2/231403132","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i2/231403132","url":null,"abstract":"In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135238561","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403065
Nyashadzashe Tamuka, K. Sibanda
Spectrum scarcity is a prevalent problem in wireless networks due to the strict allotment of the spectrum (frequency bands) to licensed users by network regulatory bodies. Such an operation implies that the unlicensed users (secondary wireless spectrum users) have to evacuate the spectrum when the primary wireless spectrum users (licensed users) are utilizing the frequency bands to avoid interference. Cognitive radio alleviates the spectrum shortage by detecting unoccupied frequency bands. This reduces the underutilization of frequency bands in wireless networks. There have been numerous related studies on spectrum sensing, however, few studies have conducted a bibliometric analysis on this subject. The goal of this study was to conduct a bibliometric analysis on the optimization of spectrum sensing. The PRISMA methodology was the basis for the bibliometric analysis to identify the limitations of the existing spectrum sensing techniques. The findings revealed that various machine learning or hybrid models outperformed the traditional techniques such as matched filter and energy detectors at the lowest signal to noise ratio (SNR). SNR is the ratio of the desired signal magnitude to the background noise magnitude. This study, therefore, recommends researchers propose alternative techniques to optimize (improve) spectrum sensing in wireless networks. More work should be done to develop models that optimize spectrum sensing at low SNR.
{"title":"A BIBLIOMETRIC ANALYSIS ON SPECTRUM SENSING IN WIRELESS NETWORKS","authors":"Nyashadzashe Tamuka, K. Sibanda","doi":"10.21817/indjcse/2023/v14i3/231403065","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403065","url":null,"abstract":"Spectrum scarcity is a prevalent problem in wireless networks due to the strict allotment of the spectrum (frequency bands) to licensed users by network regulatory bodies. Such an operation implies that the unlicensed users (secondary wireless spectrum users) have to evacuate the spectrum when the primary wireless spectrum users (licensed users) are utilizing the frequency bands to avoid interference. Cognitive radio alleviates the spectrum shortage by detecting unoccupied frequency bands. This reduces the underutilization of frequency bands in wireless networks. There have been numerous related studies on spectrum sensing, however, few studies have conducted a bibliometric analysis on this subject. The goal of this study was to conduct a bibliometric analysis on the optimization of spectrum sensing. The PRISMA methodology was the basis for the bibliometric analysis to identify the limitations of the existing spectrum sensing techniques. The findings revealed that various machine learning or hybrid models outperformed the traditional techniques such as matched filter and energy detectors at the lowest signal to noise ratio (SNR). SNR is the ratio of the desired signal magnitude to the background noise magnitude. This study, therefore, recommends researchers propose alternative techniques to optimize (improve) spectrum sensing in wireless networks. More work should be done to develop models that optimize spectrum sensing at low SNR.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49369706","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403137
Susan Waleed Mohammed Al-Bayati, R. Asgarnezhad, Karrar Ali Mohsin Alhameedawi
It enables people to communicate with computers by using their brains. Electroencephalography (EEG) data are often used to quantify this sort of activity. A general time series problem for recognizing human cognitive states is eye state classification. Knowing human cognitive states can be quite useful for therapeutic applications in our daily life. Analyses that are both subject-dependent and independent are used to classify the current ocular states. In subject-dependent classification, the model is trained using data from a subject. Subject-specific categorization, however, is exempt from this requirement. There are issues with the EEG data because of noise and muscle activity. This study suggested a categorization approach that employs a separate pre-processing stage. In this context, the basis classifiers and the most significant studies are compared to the ensemble techniques used in the classification step. A publicly accessible EEG eye state dataset from UCI is used for evaluation. The results are 96.99%.
{"title":"A META-FRAMEWORK USING ENSEMBLES FOR EEG DIAGNOSIS","authors":"Susan Waleed Mohammed Al-Bayati, R. Asgarnezhad, Karrar Ali Mohsin Alhameedawi","doi":"10.21817/indjcse/2023/v14i3/231403137","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403137","url":null,"abstract":"It enables people to communicate with computers by using their brains. Electroencephalography (EEG) data are often used to quantify this sort of activity. A general time series problem for recognizing human cognitive states is eye state classification. Knowing human cognitive states can be quite useful for therapeutic applications in our daily life. Analyses that are both subject-dependent and independent are used to classify the current ocular states. In subject-dependent classification, the model is trained using data from a subject. Subject-specific categorization, however, is exempt from this requirement. There are issues with the EEG data because of noise and muscle activity. This study suggested a categorization approach that employs a separate pre-processing stage. In this context, the basis classifiers and the most significant studies are compared to the ensemble techniques used in the classification step. A publicly accessible EEG eye state dataset from UCI is used for evaluation. The results are 96.99%.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47219281","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403132
Sashikanta Prusty, S. Dash, S. Patnaik, Sushree Gayatri Priyadarsini Prusty
In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.
{"title":"IMPACT OF COVID-19 ON BREAST CANCER SCREENING PROGRAM (BCSP) IN INDIA","authors":"Sashikanta Prusty, S. Dash, S. Patnaik, Sushree Gayatri Priyadarsini Prusty","doi":"10.21817/indjcse/2023/v14i3/231403132","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403132","url":null,"abstract":"In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48050604","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403029
R. Nurhasanah, A. Buono, W. Kusuma
{"title":"COMBINING SIGNAL TO NOISE RATIO AND UNDERSAMPLING IN SINGLE NUCLEOTIDE POLYMORPHISMS IDENTIFICATION","authors":"R. Nurhasanah, A. Buono, W. Kusuma","doi":"10.21817/indjcse/2023/v14i3/231403029","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403029","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48398429","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403055
N. Eze, Ifeoma Onodugo, Stella Osondu, Akuchinyere Chilaka, F. Nwosu, Ekwutosi Ozioma Chukwu, Emmanuel Chekwube Eze
The study analyzes the applicability and political use of Twitter using sentiments and content (textual) analysis with the purpose of examining the pattern of online communications among Nigerian voters during the run up to the 2023 Nigerian General Elections (NGE23) to make prediction for winners. Naive Bayes, Support Vector Machine, and Random Forest were utilized to determine sentiment analysis for English tweets, while ICT specialists were employed to determine content analysis for the three key Nigerian languages – Igbo, Hausa
{"title":"PERCEPTION MINING AND SENTIMENT ANALYSIS OF POLITICAL SOCIALIZATION AMONG TWITTER USERS IN THE 2023 NIGERIA GENERAL ELECTION","authors":"N. Eze, Ifeoma Onodugo, Stella Osondu, Akuchinyere Chilaka, F. Nwosu, Ekwutosi Ozioma Chukwu, Emmanuel Chekwube Eze","doi":"10.21817/indjcse/2023/v14i3/231403055","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403055","url":null,"abstract":"The study analyzes the applicability and political use of Twitter using sentiments and content (textual) analysis with the purpose of examining the pattern of online communications among Nigerian voters during the run up to the 2023 Nigerian General Elections (NGE23) to make prediction for winners. Naive Bayes, Support Vector Machine, and Random Forest were utilized to determine sentiment analysis for English tweets, while ICT specialists were employed to determine content analysis for the three key Nigerian languages – Igbo, Hausa","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49475412","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403030
D. N, G. N
Sign language is a way of communication that helps people exchange information by using hand and arm gestures, commonly used by individuals who have difficulty hearing. However, sign language isn’t universal, because impaired individuals from different countries use their corresponding sign languages. Using sign language, it allows us to communicate with impaired individuals including our loved ones, students in mainstream/deaf schools/colleges, locals and company owners, etc. Studies say learning sign language makes it simpler for a person to grasp lip-reading along with their native language. Most research has been done on Sign Language Translation/Recognition; different sign languages are translated into a common spoken language. However, the inverse is less, meaning limited research has been done on converting spoken languages to sign languages. Focusing on this matter, this study aims to translate speech/text into Indian Sign Language using the basics of Natural Language Processing.
{"title":"SPEECH/TEXT TO INDIAN SIGN LANGUAGE USING NATURAL LANGUAGE PROCESSING","authors":"D. N, G. N","doi":"10.21817/indjcse/2023/v14i3/231403030","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403030","url":null,"abstract":"Sign language is a way of communication that helps people exchange information by using hand and arm gestures, commonly used by individuals who have difficulty hearing. However, sign language isn’t universal, because impaired individuals from different countries use their corresponding sign languages. Using sign language, it allows us to communicate with impaired individuals including our loved ones, students in mainstream/deaf schools/colleges, locals and company owners, etc. Studies say learning sign language makes it simpler for a person to grasp lip-reading along with their native language. Most research has been done on Sign Language Translation/Recognition; different sign languages are translated into a common spoken language. However, the inverse is less, meaning limited research has been done on converting spoken languages to sign languages. Focusing on this matter, this study aims to translate speech/text into Indian Sign Language using the basics of Natural Language Processing.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41980238","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403057
Mohamed Abd-Elnabi I. I. Gabr, Ahmed Z. Badr, Hani M. K. Mahdi
{"title":"EXPLORING STRATEGIES FOR MEASURING SEMANTIC SIMILARITY IN SHORT ARABIC TEXTS","authors":"Mohamed Abd-Elnabi I. I. Gabr, Ahmed Z. Badr, Hani M. K. Mahdi","doi":"10.21817/indjcse/2023/v14i3/231403057","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403057","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48556775","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 : 2023-06-20DOI: 10.21817/indjcse/2023/v14i3/231403090
Nada A. Rasheed, Osama Mohammed Qasim, Ajay Kumar Barla
Feature selection, the process of representing an object in the least dimensions, is one of the most important and difficult steps in pattern recognition. Therefore, meticulous selection of important features for classification is required. In this study, we propose a method based on Multidimensional Scaling (MDS) to reduce the dimensions of ancient ceramic fragment features. This method focuses on selecting the most important features based on the density of the grayscale image and texture. Finally, we use the Euclidean distance equation to classify objects into similar groups. With a database containing more than 300 images, the experiment achieved an impressive 90% success rate in accurately categorizing fragments as either similar or non-similar. These results demonstrate the effectiveness and promise of the proposed approach for image classification tasks, emphasizing the potential of statistical methods and image processing techniques for addressing complex computer vision challenges.
{"title":"SELECTING THE IMPORTANT FEATURES TO CLASSIFY THE ARCHAEOLOGICAL FRAGMENTS BY USING STATISTICAL TOOLS","authors":"Nada A. Rasheed, Osama Mohammed Qasim, Ajay Kumar Barla","doi":"10.21817/indjcse/2023/v14i3/231403090","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v14i3/231403090","url":null,"abstract":"Feature selection, the process of representing an object in the least dimensions, is one of the most important and difficult steps in pattern recognition. Therefore, meticulous selection of important features for classification is required. In this study, we propose a method based on Multidimensional Scaling (MDS) to reduce the dimensions of ancient ceramic fragment features. This method focuses on selecting the most important features based on the density of the grayscale image and texture. Finally, we use the Euclidean distance equation to classify objects into similar groups. With a database containing more than 300 images, the experiment achieved an impressive 90% success rate in accurately categorizing fragments as either similar or non-similar. These results demonstrate the effectiveness and promise of the proposed approach for image classification tasks, emphasizing the potential of statistical methods and image processing techniques for addressing complex computer vision challenges.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44190804","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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