Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130566
Usha Badhera, Apoorva Verma, P. Nahar
Abstract In this paper literature was surveyed to find popular clustering techniques used by researchers in recent times to predict academic performance. We obtained a trend that the K-means algorithm is particularly popular among researchers because of its simplicity and scalability, and in other studies K-medoids algorithm was selected as it is less affected by outliers. On the basis of these observations these two clustering algorithms were implemented in Python, on student dataset of undergraduate students from a higher education institute. Two different clusters were obtained which segment students based on their academic performances in the previous two exams. The clusters obtained by have high accuracy score and K-medoids cluster centroids have taken exact values of marks obtained by students whereas K-means centroid value is a round off. The K-means clustering is also affected by the presence of outliers in the student dataset.
{"title":"Applicability of K-medoids and K-means algorithms for segmenting students based on their scholastic performance","authors":"Usha Badhera, Apoorva Verma, P. Nahar","doi":"10.1080/09720510.2022.2130566","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130566","url":null,"abstract":"Abstract In this paper literature was surveyed to find popular clustering techniques used by researchers in recent times to predict academic performance. We obtained a trend that the K-means algorithm is particularly popular among researchers because of its simplicity and scalability, and in other studies K-medoids algorithm was selected as it is less affected by outliers. On the basis of these observations these two clustering algorithms were implemented in Python, on student dataset of undergraduate students from a higher education institute. Two different clusters were obtained which segment students based on their academic performances in the previous two exams. The clusters obtained by have high accuracy score and K-medoids cluster centroids have taken exact values of marks obtained by students whereas K-means centroid value is a round off. The K-means clustering is also affected by the presence of outliers in the student dataset.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121067956","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130575
A. Goswami, Ity Patni, S. Choubey, Nishu Gupta
Abstract Investment in mutual funds can be made either through Lump Sum Investment Plan (LIP) or through Systematic Investment Plan (SIP). This study is an attempt to spread awareness amongst public/investors regarding the various advantages of SIP investments. The comparison has been done between LIP : and SIP investment option on selected equity funds of 8 Indian AMCs for a period of 3 years. Selected risk-return non regression measures are applied to assess the performance of selected equity funds. Further MCDM technique is applied to rank the SIP investment options of equity funds based on their performance evaluation results.
{"title":"A comparative study on SIP and LIP with reference to Indian mutual fund industry using non-regression performance evaluation ratios and single factor CAPM model","authors":"A. Goswami, Ity Patni, S. Choubey, Nishu Gupta","doi":"10.1080/09720510.2022.2130575","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130575","url":null,"abstract":"Abstract Investment in mutual funds can be made either through Lump Sum Investment Plan (LIP) or through Systematic Investment Plan (SIP). This study is an attempt to spread awareness amongst public/investors regarding the various advantages of SIP investments. The comparison has been done between LIP : and SIP investment option on selected equity funds of 8 Indian AMCs for a period of 3 years. Selected risk-return non regression measures are applied to assess the performance of selected equity funds. Further MCDM technique is applied to rank the SIP investment options of equity funds based on their performance evaluation results.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224267","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130580
Deepti Kiran, Ashish Sharma, Itisha Sharma
Abstract It is important for the service providers to understand the expectations of the customers to be more competitive in the market. The study aims to understand the customer preferences in the non-scheduled operators in the Indian market. A self-administered questionnaire was shared with 480 customers about their preferences and expectations from the non-scheduled operators. This study presents pioneering research in the field of charter industry in the Indian aviation sector. The results of this study provide a contribution towards a better understanding of the customer segment, so that the charter companies can mold their strategies to address the needs of the customers and be more profitable. This study also tries to understand the current satisfaction level of the clientele. The study also finds which initiatives would encourage customers to choose a non-scheduled operator. The author could not find any research done in the non-scheduled segment of the aviation industry and therefore, this study is one of its kind in the entire Indian sub-continent and will become a basis of future researches in this field.
{"title":"Understanding the customer preferences of non-scheduled operators in India","authors":"Deepti Kiran, Ashish Sharma, Itisha Sharma","doi":"10.1080/09720510.2022.2130580","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130580","url":null,"abstract":"Abstract It is important for the service providers to understand the expectations of the customers to be more competitive in the market. The study aims to understand the customer preferences in the non-scheduled operators in the Indian market. A self-administered questionnaire was shared with 480 customers about their preferences and expectations from the non-scheduled operators. This study presents pioneering research in the field of charter industry in the Indian aviation sector. The results of this study provide a contribution towards a better understanding of the customer segment, so that the charter companies can mold their strategies to address the needs of the customers and be more profitable. This study also tries to understand the current satisfaction level of the clientele. The study also finds which initiatives would encourage customers to choose a non-scheduled operator. The author could not find any research done in the non-scheduled segment of the aviation industry and therefore, this study is one of its kind in the entire Indian sub-continent and will become a basis of future researches in this field.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117160757","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130574
L. Vijayan, R. Venkatesh
Abstract The viewpoint of online shopping is increasing in India because of several factors such as increased Internet knowledge, perceived efficacy, ease of use, increased number of working women, entry from the worldwide and local partakers, and the increased prominence of online companies. The purpose of this study is to find the customers trust and loyalty on online shopping during festival sales in India where various factors have been discussed. Descriptive research was used in this study using structured questionnaires to collect data from the respondents. The survey was carried out online using Goggle forms due to the ongoing pandemic. Regression analysis was used for testing of hypothesis. The results showed that there is a strong relationship between advertising and trust & loyalty during online shopping festival sales and advertising is a major boost online platforms sales during festival sales. The study in particular focuses on festival sales of online shopping in India where it’s limited to a defined period of online shopping, also increased number of respondents would give a broader outcome of the study. Online shopping festival sales has been growing at a rapid pace in India, focus of this study was on trust and loyalty as a prime factor. Though various aspects of festival sales in online shopping have been studied the effect of advertising This research was conducted at VIT University as part of my Research Program. driving these sales and its impact on customer trust and loyalty has been the first of its kind to be tested.
{"title":"Impact of online shopping advertising on customer trust and loyalty during festival sales","authors":"L. Vijayan, R. Venkatesh","doi":"10.1080/09720510.2022.2130574","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130574","url":null,"abstract":"Abstract The viewpoint of online shopping is increasing in India because of several factors such as increased Internet knowledge, perceived efficacy, ease of use, increased number of working women, entry from the worldwide and local partakers, and the increased prominence of online companies. The purpose of this study is to find the customers trust and loyalty on online shopping during festival sales in India where various factors have been discussed. Descriptive research was used in this study using structured questionnaires to collect data from the respondents. The survey was carried out online using Goggle forms due to the ongoing pandemic. Regression analysis was used for testing of hypothesis. The results showed that there is a strong relationship between advertising and trust & loyalty during online shopping festival sales and advertising is a major boost online platforms sales during festival sales. The study in particular focuses on festival sales of online shopping in India where it’s limited to a defined period of online shopping, also increased number of respondents would give a broader outcome of the study. Online shopping festival sales has been growing at a rapid pace in India, focus of this study was on trust and loyalty as a prime factor. Though various aspects of festival sales in online shopping have been studied the effect of advertising This research was conducted at VIT University as part of my Research Program. driving these sales and its impact on customer trust and loyalty has been the first of its kind to be tested.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511260","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130570
K.Bharatha Devi, S. Ratnoo
Abstract Among the other problems in the learning process, student dropout is an acute problem that needs to be taken care of by the educationist and policymakers. This paper is based on 330 students admitted to the Jawahar Navodaya Vidyalaya (JNV) school in the 6th class in five successive batches. The dataset has ten attributes out of which eight variables are categorical, and two are numerical. The paper addresses the research question as to what factors are important vis-a-vis the dropout students. Further, we have applied a random forest classifier to predict the school dropouts after five years. The results show that performance in the 6th class, income, father’s education, and gender are factors that influence the school dropouts. The random forest classifier achieves 86 per cent accuracy, 41 percent sensitivity and 98 percent specificity. We need to take data from more schools to further generalize the study.
{"title":"Predicting student dropouts using random forest","authors":"K.Bharatha Devi, S. Ratnoo","doi":"10.1080/09720510.2022.2130570","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130570","url":null,"abstract":"Abstract Among the other problems in the learning process, student dropout is an acute problem that needs to be taken care of by the educationist and policymakers. This paper is based on 330 students admitted to the Jawahar Navodaya Vidyalaya (JNV) school in the 6th class in five successive batches. The dataset has ten attributes out of which eight variables are categorical, and two are numerical. The paper addresses the research question as to what factors are important vis-a-vis the dropout students. Further, we have applied a random forest classifier to predict the school dropouts after five years. The results show that performance in the 6th class, income, father’s education, and gender are factors that influence the school dropouts. The random forest classifier achieves 86 per cent accuracy, 41 percent sensitivity and 98 percent specificity. We need to take data from more schools to further generalize the study.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128287367","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130568
Shailee Bhatia, Shelly Sachdeva, Puneet Goswami
Abstract Air pollution is a vital issue that affects day-to-day lives. It is observed that throughout the world, there is an instant need to overcome the monster of pollution. According to statistics, most of the polluted cities in the world are in India. This poses a serious need of the hour for the Indian scientists, engineers, and authorities as a whole to fight and reduce it as much as possible. The time has come when one needs to plan their outside activities on pollution levels and air quality status. Air Quality Index (AQI) varies daily; hence it is difficult to predict future trends for the same. The current study proposed a machine learning-based model that uses sensors, past/present pollutants concentration data, and satellite data to predict air pollution in the regions in India. We emphasize the fact that other than measurable pollutants (PM10, PM2.5, NO2, etc.); meteorological data like wind, temperature, and fire are also important factors in determining pollution. The model uses Long Short-Term Memory, which is the state-of-the-art technique used for time series prediction. The model could predict the concentration of the pollutants and calculate the AQI for the areas where data was available for the near future. The Root Mean Square Error on test data is 54. The results are quite promising and future model can be made, taking this as a base model. An inexpensive prediction technique can greatly help the administration in mitigating pollution.
{"title":"Air pollution prediction and hotspot detection using machine learning","authors":"Shailee Bhatia, Shelly Sachdeva, Puneet Goswami","doi":"10.1080/09720510.2022.2130568","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130568","url":null,"abstract":"Abstract Air pollution is a vital issue that affects day-to-day lives. It is observed that throughout the world, there is an instant need to overcome the monster of pollution. According to statistics, most of the polluted cities in the world are in India. This poses a serious need of the hour for the Indian scientists, engineers, and authorities as a whole to fight and reduce it as much as possible. The time has come when one needs to plan their outside activities on pollution levels and air quality status. Air Quality Index (AQI) varies daily; hence it is difficult to predict future trends for the same. The current study proposed a machine learning-based model that uses sensors, past/present pollutants concentration data, and satellite data to predict air pollution in the regions in India. We emphasize the fact that other than measurable pollutants (PM10, PM2.5, NO2, etc.); meteorological data like wind, temperature, and fire are also important factors in determining pollution. The model uses Long Short-Term Memory, which is the state-of-the-art technique used for time series prediction. The model could predict the concentration of the pollutants and calculate the AQI for the areas where data was available for the near future. The Root Mean Square Error on test data is 54. The results are quite promising and future model can be made, taking this as a base model. An inexpensive prediction technique can greatly help the administration in mitigating pollution.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114199614","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130565
A. Saxena, Arun Sharma, Mohd Majid
Abstract In this work optimal autonomous controlling of frequency and voltage deviation of the power system network has been presented. The frequency deviations and the voltage fluctuations are assessed with conventional and genetic algorithm method. Initial model of power system network has been developed which is based on Tie line power flow. There were several unknown parameters observed in the objective functions of conventional tie line power method. These unknown parameters were trained with genetic algorithm. The genetic algorithm consist of three major steps: reproduction, crossover, and mutations. The suitable value of frequency and voltage deviations are obtained for various loading conditions. But due loading conditions, high value of transient or peak overshoot and settling time were attained for frequency and voltage variations. It is observed that optimal minimum value of peak overshoot and settling time for frequency and voltage deviations are attained with genetic algorithm in comparison to conventional methods.
{"title":"A robust controlling and management of load with minimum frequency and voltage deviation in network employing genetic algorithm","authors":"A. Saxena, Arun Sharma, Mohd Majid","doi":"10.1080/09720510.2022.2130565","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130565","url":null,"abstract":"Abstract In this work optimal autonomous controlling of frequency and voltage deviation of the power system network has been presented. The frequency deviations and the voltage fluctuations are assessed with conventional and genetic algorithm method. Initial model of power system network has been developed which is based on Tie line power flow. There were several unknown parameters observed in the objective functions of conventional tie line power method. These unknown parameters were trained with genetic algorithm. The genetic algorithm consist of three major steps: reproduction, crossover, and mutations. The suitable value of frequency and voltage deviations are obtained for various loading conditions. But due loading conditions, high value of transient or peak overshoot and settling time were attained for frequency and voltage variations. It is observed that optimal minimum value of peak overshoot and settling time for frequency and voltage deviations are attained with genetic algorithm in comparison to conventional methods.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129865119","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130564
R. Maheshwari, V. Kapoor
Abstract The proposed work studies the volatility pattern of NSE (National Stock exchange) stock market at its opening price for a period of ten years (2008-2017). In financial market, the most widely used measure is volatility, which shows the dispersion of stock market returns over a period. In general, the volatility measure the risk associated with the stock market; if the volatility is high, the risk is higher and vice versa. This can help an investor to differentiate between low risk and high risk stock indexes and to invest sensibly. In this paper we build a model for getting the volatility of stock market return based in NSE ten years value. We have calculated daily, monthly and yearly volatility and concluded that Year wise has the highest risk associated. Then we build the GARCH model to predict the volatility based on the historic value of NSE data. In this way in the proposed work, we have devised a way to predict the volatility of NSE using GARCH model.
{"title":"Estimating the volatility of stock price index for Indian market using GARCH model","authors":"R. Maheshwari, V. Kapoor","doi":"10.1080/09720510.2022.2130564","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130564","url":null,"abstract":"Abstract The proposed work studies the volatility pattern of NSE (National Stock exchange) stock market at its opening price for a period of ten years (2008-2017). In financial market, the most widely used measure is volatility, which shows the dispersion of stock market returns over a period. In general, the volatility measure the risk associated with the stock market; if the volatility is high, the risk is higher and vice versa. This can help an investor to differentiate between low risk and high risk stock indexes and to invest sensibly. In this paper we build a model for getting the volatility of stock market return based in NSE ten years value. We have calculated daily, monthly and yearly volatility and concluded that Year wise has the highest risk associated. Then we build the GARCH model to predict the volatility based on the historic value of NSE data. In this way in the proposed work, we have devised a way to predict the volatility of NSE using GARCH model.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128922592","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 : 2022-10-03DOI: 10.1080/09720510.2022.2130573
G. Saini, Shaurya Gupta
Abstract These times of pandemic influence remote working and understanding of the new normal. This new normal helps in reaching out the importance of an individualistic approach with the psychological contribution in procuring sustainable thinking. The benefaction of the individual and community helps in understanding these difficult times by accessing some psychological variables such as individual potential, stimulant drivers, identity traits and emotional health. These variables show their involvement in forming an individualistic approach. An individualistic approach will help in developing sustainable thinking which contributes to using the limited resources to the fullest in Covid-19 times. It can be concluded that individuals with high individual potential and stimulant drivers will promote an individualistic approach which promotes sustainable thinking. Emotional health and identity traits help in a flourishing individualistic approach which promotes sustainable thinking. The futuristic approach of the study throws light on the execution of cognitive styles in the individualistic approach which can be altered by individual potential, stimulant drivers, identity traits and emotional health accentuating the approach to stimulating sustainable thinking.
{"title":"Design of framework for cognitive styles in formulating individualistic approach","authors":"G. Saini, Shaurya Gupta","doi":"10.1080/09720510.2022.2130573","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130573","url":null,"abstract":"Abstract These times of pandemic influence remote working and understanding of the new normal. This new normal helps in reaching out the importance of an individualistic approach with the psychological contribution in procuring sustainable thinking. The benefaction of the individual and community helps in understanding these difficult times by accessing some psychological variables such as individual potential, stimulant drivers, identity traits and emotional health. These variables show their involvement in forming an individualistic approach. An individualistic approach will help in developing sustainable thinking which contributes to using the limited resources to the fullest in Covid-19 times. It can be concluded that individuals with high individual potential and stimulant drivers will promote an individualistic approach which promotes sustainable thinking. Emotional health and identity traits help in a flourishing individualistic approach which promotes sustainable thinking. The futuristic approach of the study throws light on the execution of cognitive styles in the individualistic approach which can be altered by individual potential, stimulant drivers, identity traits and emotional health accentuating the approach to stimulating sustainable thinking.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133635416","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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