In the present study, quantitative model and methods that analyze dynamic system of communication flow have been developed in the domain of queuing theory under uncertain environment. The study approach supports model-based queue design as opposed to creative engineering. Some critical aspects and results of queue model and fuzzy set theory has been reviewed in brief and the application of bulk queue model to communication network under imprecise data has been discussed. We have assumed that the arrival pattern, service pattern as well covariance between incoming and transmitted packets all are fuzzy in nature. The system characteristics with defuzzification process have been explored on the basis of lower and upper bound at possibility level alpha and signed distance method. The validity of the results has been analyzed through numerical illustration and graphical study.
{"title":"Modeling of Communication Network with Queuing Theory under Fuzzy Environment","authors":"Himanshu Mittal, Naresh Sharma","doi":"10.17762/msea.v71i2.72","DOIUrl":"https://doi.org/10.17762/msea.v71i2.72","url":null,"abstract":"In the present study, quantitative model and methods that analyze dynamic system of communication flow have been developed in the domain of queuing theory under uncertain environment. The study approach supports model-based queue design as opposed to creative engineering. Some critical aspects and results of queue model and fuzzy set theory has been reviewed in brief and the application of bulk queue model to communication network under imprecise data has been discussed. We have assumed that the arrival pattern, service pattern as well covariance between incoming and transmitted packets all are fuzzy in nature. The system characteristics with defuzzification process have been explored on the basis of lower and upper bound at possibility level alpha and signed distance method. The validity of the results has been analyzed through numerical illustration and graphical study.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42479664","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}
The credibility of the news sources has hit a new low during the COVID-19. Hence, it is necessary to check for facts before trusting the news. Clustering is extremely important for analysing data, making predictions, and overcoming data abnormalities. So, in this work, the two most prominent clustering algorithms, K-Means and K-Medoids, are tested on a dataset, and K-Means outperforms k-Medoid. We now utilized supervised classification methods like Logistic Regression, K-Nearest Neighbours, and Support Vector classifier to train on the same news headlines we used for clustering with the 'Prediction' column, and then chosen the technique with the highest accuracy. The Support Vector Classifier had the maximum accuracy of 94.93 percent, according to the test. We have developed is a hybrid model consisting of an unsupervised K Means clustering algorithm and a supervised Support Vector classification algorithm. The K Means algorithm organizes the news headlines into clusters by capturing the usage of certain words and the support vector algorithm learns from those clusters to predict the categories into which the unseen news headlines belong to.
{"title":"Recognizing Fake Headlines Using Clustering Algorithms","authors":"Juthuka Arunadevi, A. Mary Sowjanya","doi":"10.17762/msea.v71i2.71","DOIUrl":"https://doi.org/10.17762/msea.v71i2.71","url":null,"abstract":"The credibility of the news sources has hit a new low during the COVID-19. Hence, it is necessary to check for facts before trusting the news. Clustering is extremely important for analysing data, making predictions, and overcoming data abnormalities. So, in this work, the two most prominent clustering algorithms, K-Means and K-Medoids, are tested on a dataset, and K-Means outperforms k-Medoid. We now utilized supervised classification methods like Logistic Regression, K-Nearest Neighbours, and Support Vector classifier to train on the same news headlines we used for clustering with the 'Prediction' column, and then chosen the technique with the highest accuracy. The Support Vector Classifier had the maximum accuracy of 94.93 percent, according to the test. We have developed is a hybrid model consisting of an unsupervised K Means clustering algorithm and a supervised Support Vector classification algorithm. The K Means algorithm organizes the news headlines into clusters by capturing the usage of certain words and the support vector algorithm learns from those clusters to predict the categories into which the unseen news headlines belong to.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43170321","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}
Typically, classification algorithms work poorly when confronted with unbalanced datasets, and the resulting effects are skewed against the majority class. As a result, an effective model is required to identify unbalanced data, particularly in the context of fraud detection. For these types of issues, the classifier's accuracy is not trusted because the cost of predicting a fraud sample as a non-fraud sample is extremely high. In general, imbalanced learning happens when some types of data distributions significantly outnumber other data distributions in the instance space. There is a need of technique such as under sampling or oversampling in order to learn from unbalanced datasets. A novel over sampling method has been suggested for learning from unbalanced datasets in this paper. The basic impression here is that a weighted distribution for diverse outnumbered class instances has been utilized depending on their degree of complexity to learn, with more pretended evidence leading to the outnumbered ones, being more troublesome to learn. As a result, with regard to data distributions, the suggested approach improves learning first by bringing down the bias familiarized using class difference, and then by pliantly conveying the classification judgement boundary toward challenging instances.
{"title":"A Novel Technique to Defraud Credit Card by Handling Class Imbalance Problem Using Machine Learning","authors":"Kaneez Zainab, Namrata Dhanda, Syed Qamar Abbas","doi":"10.17762/msea.v71i2.69","DOIUrl":"https://doi.org/10.17762/msea.v71i2.69","url":null,"abstract":"Typically, classification algorithms work poorly when confronted with unbalanced datasets, and the resulting effects are skewed against the majority class. As a result, an effective model is required to identify unbalanced data, particularly in the context of fraud detection. For these types of issues, the classifier's accuracy is not trusted because the cost of predicting a fraud sample as a non-fraud sample is extremely high. In general, imbalanced learning happens when some types of data distributions significantly outnumber other data distributions in the instance space. There is a need of technique such as under sampling or oversampling in order to learn from unbalanced datasets. A novel over sampling method has been suggested for learning from unbalanced datasets in this paper. The basic impression here is that a weighted distribution for diverse outnumbered class instances has been utilized depending on their degree of complexity to learn, with more pretended evidence leading to the outnumbered ones, being more troublesome to learn. As a result, with regard to data distributions, the suggested approach improves learning first by bringing down the bias familiarized using class difference, and then by pliantly conveying the classification judgement boundary toward challenging instances.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43586117","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}
Purpose – The purpose of this paper is twofold. First, is to evaluate the impact of digital marketing on the Buying behaviour Practices of medical professionals; and second, is to investigate the success factors of Digital Marketing model. Methodology – The study employs a survey analysis for 547 Medical Professionals in Haryana, India, to determine the extent of their Purchasing Behaviour Practices via Digital Marketing through a standard structured questionnaire. The study employs the Step-wise Regression technique to identify the most relevant predictors of the Digital Marketing model. Findings – The Predictors are Buying Behaviour practices, User friendly, Previous Buying Behaviour, Success Factors and Hindrance Factors of Digital Marketing model. Further, these predictors have been summarized each with the demographic forces as Dependent Variables i.e. Gender, Marital Status and Highest Qualifications respectively. The overall results depict that all the Models are having a significance impact on the Buying Behaviour Practices of Medical Professionals and contribute to the Success of Digital Marketing. Originality – The success of digital marketing model for analyzing the buying behaviour practices of medical professionals with five predictors and there demographic factors as dependent variable has provided insights which add new knowledge to the extent of digital marketing techniques adopted by medical professionals and buying behaviour literature.
{"title":"Success of Digital Marketing Model: Analysis for Buying Behaviour Practices of Medical Professionals","authors":"Monika Pathak, Rahul Hakhu","doi":"10.17762/msea.v71i2.70","DOIUrl":"https://doi.org/10.17762/msea.v71i2.70","url":null,"abstract":"Purpose – The purpose of this paper is twofold. First, is to evaluate the impact of digital marketing on the Buying behaviour Practices of medical professionals; and second, is to investigate the success factors of Digital Marketing model. \u0000Methodology – The study employs a survey analysis for 547 Medical Professionals in Haryana, India, to determine the extent of their Purchasing Behaviour Practices via Digital Marketing through a standard structured questionnaire. The study employs the Step-wise Regression technique to identify the most relevant predictors of the Digital Marketing model. \u0000Findings – The Predictors are Buying Behaviour practices, User friendly, Previous Buying Behaviour, Success Factors and Hindrance Factors of Digital Marketing model. Further, these predictors have been summarized each with the demographic forces as Dependent Variables i.e. Gender, Marital Status and Highest Qualifications respectively. The overall results depict that all the Models are having a significance impact on the Buying Behaviour Practices of Medical Professionals and contribute to the Success of Digital Marketing. \u0000Originality – The success of digital marketing model for analyzing the buying behaviour practices of medical professionals with five predictors and there demographic factors as dependent variable has provided insights which add new knowledge to the extent of digital marketing techniques adopted by medical professionals and buying behaviour literature.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44881124","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}
The data mining process mainly deals with the estimation, prediction, pattern extraction, and classification in big databases. The presence of missing values in the dataset decreases the accuracy of data mining classifiers. Therefore it is necessary to deal with missing values in the dataset to achieve accurate results. To improve the quality of data and prediction accuracy in the classification process, the authors have proposed a new hybrid missing value prediction algorithm, KLR, by combining the KNN and linear regression approach. The proposed KLR algorithm has been used for class validation and missing values imputation. Wisconsin Breast Cancer Diagnostic Dataset of 569 instances with 32 attributes from the machine learning repository of UCI, Irvinewasused to conduct the study. The Pearson Coefficient Correlation method is used for feature selection.Data normalization is performed using Min-max scaling technique. The Scikit-learn library for machine learning in python is used to complete all the experiments as the experimental framework. The mean square error method is used to evaluate the performance of the model. The proposed KLR algorithm with 450 nearest neighbors out of 569 gives the lowest MSE ie 0.00188 and more accurately predicts the missing values as compared to the classic models.
{"title":"Hybrid Missing Value Imputation Algorithm- KLR","authors":"Deepti Sharma, Rajneesh Kumar, Anurag Jain","doi":"10.17762/msea.v71i2.67","DOIUrl":"https://doi.org/10.17762/msea.v71i2.67","url":null,"abstract":"The data mining process mainly deals with the estimation, prediction, pattern extraction, and classification in big databases. The presence of missing values in the dataset decreases the accuracy of data mining classifiers. Therefore it is necessary to deal with missing values in the dataset to achieve accurate results. To improve the quality of data and prediction accuracy in the classification process, the authors have proposed a new hybrid missing value prediction algorithm, KLR, by combining the KNN and linear regression approach. The proposed KLR algorithm has been used for class validation and missing values imputation. Wisconsin Breast Cancer Diagnostic Dataset of 569 instances with 32 attributes from the machine learning repository of UCI, Irvinewasused to conduct the study. The Pearson Coefficient Correlation method is used for feature selection.Data normalization is performed using Min-max scaling technique. The Scikit-learn library for machine learning in python is used to complete all the experiments as the experimental framework. The mean square error method is used to evaluate the performance of the model. The proposed KLR algorithm with 450 nearest neighbors out of 569 gives the lowest MSE ie 0.00188 and more accurately predicts the missing values as compared to the classic models.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49367412","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}
The Internet of Things (IoT) has grabbed the attention of the scientific community in recent years. IoT is a recently formed technology that will be the future of the web, allowing distinct everyday objects to communicate with each other without any human interaction. It is one of the most hotly debated fields in both academia and industry for current and future study areas. IoT security and privacy issues have proven to be critical objectives. This paper includes IoT models, schemes, and implementation issues related to different IoT technologies and devices. It focuses on security challenges of IoT communications such as privacy, authentication, integrity of data, and service availability, mostly in hardware aspects. Attacks and modern vulnerabilities, as well as countermeasures, are taken into account. Various IoT security models are described along with security challenges.
{"title":"Security in Internet of Things (IoT): Challenges and Models","authors":"Navdeep Lata, Dr. Raman Kumar","doi":"10.17762/msea.v71i2.68","DOIUrl":"https://doi.org/10.17762/msea.v71i2.68","url":null,"abstract":"The Internet of Things (IoT) has grabbed the attention of the scientific community in recent years. IoT is a recently formed technology that will be the future of the web, allowing distinct everyday objects to communicate with each other without any human interaction. It is one of the most hotly debated fields in both academia and industry for current and future study areas. IoT security and privacy issues have proven to be critical objectives. This paper includes IoT models, schemes, and implementation issues related to different IoT technologies and devices. It focuses on security challenges of IoT communications such as privacy, authentication, integrity of data, and service availability, mostly in hardware aspects. Attacks and modern vulnerabilities, as well as countermeasures, are taken into account. Various IoT security models are described along with security challenges.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43989604","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}
Wireless networks are complicated to put together, and the more people use them over time, the more complicated they become. Wireless networks are made up of many different types of technology, which means they have vulnerabilities. One vulnerability is that they are easily spoofed or impersonated by Sybil attacks. In a Sybil attack, the attacker disguises themselves as someone else and generates various identities to have access to the system. This type of attack is typically accomplished by creating multiple fake user accounts. The attacker then uses these fake accounts to promote their content or ideas, vote for their own content or ideas, and/or harass other users. Since wireless networks are very resource-constrained, it is vital to develop more efficient and lightweight trustworthy security mechanisms to identify & track Sybil attacks as these are a major concern for the stability or security of the network. There are some security schemes for prevention against Sybil attacks, like cryptography, privacy-preserving solutions and lightweight authentication. Cryptography and privacy-preserving techniques require key management and additional infrastructure overhead, which makes them difficult to establish and maintain in a limited resource environment. The lightweight trusted system detects & avoids single node and multi-node attacks under different conditions. In this paper, a survey is conducted on various techniques for the detection of Sybil attacks.
{"title":"A Survey: Detection and Mitigation Techniques of Sybil in the Networks","authors":"Meena Bharti, Dr Shaveta Rani, Dr Paramjeet Singh","doi":"10.17762/msea.v71i2.66","DOIUrl":"https://doi.org/10.17762/msea.v71i2.66","url":null,"abstract":"Wireless networks are complicated to put together, and the more people use them over time, the more complicated they become. Wireless networks are made up of many different types of technology, which means they have vulnerabilities. One vulnerability is that they are easily spoofed or impersonated by Sybil attacks. In a Sybil attack, the attacker disguises themselves as someone else and generates various identities to have access to the system. This type of attack is typically accomplished by creating multiple fake user accounts. The attacker then uses these fake accounts to promote their content or ideas, vote for their own content or ideas, and/or harass other users. Since wireless networks are very resource-constrained, it is vital to develop more efficient and lightweight trustworthy security mechanisms to identify & track Sybil attacks as these are a major concern for the stability or security of the network. There are some security schemes for prevention against Sybil attacks, like cryptography, privacy-preserving solutions and lightweight authentication. Cryptography and privacy-preserving techniques require key management and additional infrastructure overhead, which makes them difficult to establish and maintain in a limited resource environment. The lightweight trusted system detects & avoids single node and multi-node attacks under different conditions. In this paper, a survey is conducted on various techniques for the detection of Sybil attacks.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47489424","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}
Computer systems have made it possible to transfer human life from the real world to virtual reality. This process has been accelerated by the Covid-19 virus. Cybercriminals have also switched from a real-life to a virtual one. Online, committing a crime is far easier than in real life. Cybercriminals often use malicious software (malware), to launch cyber-attacks. Apart from this polymorphic and metamorphic malware are used that use obfuscation techniques to create new malware variants. To effectively battle new malware types, you'll need to employ creative approaches that depart from the conventional. Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants. Deep learning (DL), which differs from typical machine learning methods, might be a potential approach to the challenge of identifying all varieties of malware. In the present study, an IVMCT framework is introduced which classifies malware using transfer learning. For this purpose, the MalImg dataset is used which is based on grayscale images converted from binaries of malware. The comparison of IVMCT is done with existing techniques which shows that our technique is better than existing techniques.
{"title":"IVMCT: Image Visualization based Multiclass Malware Classification using Transfer Learning","authors":"M. Raman Kumar","doi":"10.17762/msea.v71i2.65","DOIUrl":"https://doi.org/10.17762/msea.v71i2.65","url":null,"abstract":"Computer systems have made it possible to transfer human life from the real world to virtual reality. This process has been accelerated by the Covid-19 virus. Cybercriminals have also switched from a real-life to a virtual one. Online, committing a crime is far easier than in real life. Cybercriminals often use malicious software (malware), to launch cyber-attacks. Apart from this polymorphic and metamorphic malware are used that use obfuscation techniques to create new malware variants. To effectively battle new malware types, you'll need to employ creative approaches that depart from the conventional. Traditionally signature-based techniques are used with machine learning algorithms to detect malware that is unable to catch its variants. Deep learning (DL), which differs from typical machine learning methods, might be a potential approach to the challenge of identifying all varieties of malware. In the present study, an IVMCT framework is introduced which classifies malware using transfer learning. For this purpose, the MalImg dataset is used which is based on grayscale images converted from binaries of malware. The comparison of IVMCT is done with existing techniques which shows that our technique is better than existing techniques.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45616044","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 : 2022-03-06DOI: 10.17762/msea.v71i2.2195
J. Kandpal
Chronic diseases kill many Humans in all over the world. Monitor risk factors including physical exercise to manage these illnesses. Wearables like Fitbit can track and give health data to help users make decisions. Most wearables marketing targets the young, active, and most populous racial groups. Wearable electronics can revolutionize healthcare by continuously monitoring health factors. Sensor technology, data processing, and communication protocols have made wearable gadgets useful for healthcare monitoring and diagnosis. This article discusses sensors, data processing, and communication protocols used in wearable electronics to revolutionize healthcare monitoring and diagnosis. A side-by-side table compares each method's pros and cons. The topic covers wearable electronics processing for healthcare monitoring and diagnosis. A block architecture and graphic explain healthcare monitoring and diagnosis using wearable electronics. Wearable electronics adoption is often hampered by concerns regarding data privacy and security, data reliability, and healthcare system compatibility. Wearable electronics are revolutionizing medicine in numerous ways, from monitoring chronic illnesses to giving emergency treatment. Wearable tech could develop into artificial intelligence, machine learning, augmented reality, virtual reality, cutting-edge sensors, telemedicine, 5G networks, nanotechnology, and blockchain. Finally, wearable electronics research could improve patient outcomes and quality of life, transforming healthcare.
{"title":"Exploring the Potential of Wearable Electronics for Healthcare Monitoring and Diagnosis","authors":"J. Kandpal","doi":"10.17762/msea.v71i2.2195","DOIUrl":"https://doi.org/10.17762/msea.v71i2.2195","url":null,"abstract":"Chronic diseases kill many Humans in all over the world. Monitor risk factors including physical exercise to manage these illnesses. Wearables like Fitbit can track and give health data to help users make decisions. Most wearables marketing targets the young, active, and most populous racial groups. Wearable electronics can revolutionize healthcare by continuously monitoring health factors. Sensor technology, data processing, and communication protocols have made wearable gadgets useful for healthcare monitoring and diagnosis. This article discusses sensors, data processing, and communication protocols used in wearable electronics to revolutionize healthcare monitoring and diagnosis. A side-by-side table compares each method's pros and cons. The topic covers wearable electronics processing for healthcare monitoring and diagnosis. A block architecture and graphic explain healthcare monitoring and diagnosis using wearable electronics. Wearable electronics adoption is often hampered by concerns regarding data privacy and security, data reliability, and healthcare system compatibility. Wearable electronics are revolutionizing medicine in numerous ways, from monitoring chronic illnesses to giving emergency treatment. Wearable tech could develop into artificial intelligence, machine learning, augmented reality, virtual reality, cutting-edge sensors, telemedicine, 5G networks, nanotechnology, and blockchain. Finally, wearable electronics research could improve patient outcomes and quality of life, transforming healthcare.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43869966","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 : 2022-03-06DOI: 10.17762/msea.v71i2.2139
Ritiksha Danu
Conventionally, the success of a wastewater treatment plant is evaluated by the quantity of chemical oxygen demand (COD), dissolved organic matter (BOD), total suspended solids (TSS), and other consequences of wastewater treatment that are removed during the treatment process and thereafter. Environmental engineers consider a number of parameters at the plant's discharge, including pH, NH4-N, NTotal, fecal coliform, and others. The traditional approach to performance assessment fails because it does not directly compare the effluent's distribution of these characteristics throughout the output stage of the process to the standards or specification limits issued by the Central Pollution Control Board (CPCB). To fill this knowledge gap, we propose and implement the probability-based Process Capability Indices (PCIs) and Multi - variate Process Flow Indices (MPCIs) in this research. These indices measure the effectiveness of a wastewater treatment process by contrasting the observed results with the predicted ones. PCIs have been widely used as a baseline against which manufacturing processes may be evaluated and tweaked to increase efficiency. The focus of this effort is on PCIs and MPCIs from the standpoint of environmental engineers, with the hope that their use would grow. Using capacity indicators accurately measures the effectiveness of the process of wastewater treatment, which is essential for reducing pollution and permitting the reuse of treated water. This study provides an analysis of the treating wastewater process's capacity by applying appropriate capability indices, using additional information acquired from case studies via literature research. Findings suggest that appropriate capacity indices may allow for more precise assessments of sewage treatment system performance than are presently possible.
{"title":"An Innovative Condition Assessment Method for Wastewater Treatment Facilities to Promote Long-Term Sustainability in Management and Operations","authors":"Ritiksha Danu","doi":"10.17762/msea.v71i2.2139","DOIUrl":"https://doi.org/10.17762/msea.v71i2.2139","url":null,"abstract":"Conventionally, the success of a wastewater treatment plant is evaluated by the quantity of chemical oxygen demand (COD), dissolved organic matter (BOD), total suspended solids (TSS), and other consequences of wastewater treatment that are removed during the treatment process and thereafter. Environmental engineers consider a number of parameters at the plant's discharge, including pH, NH4-N, NTotal, fecal coliform, and others. The traditional approach to performance assessment fails because it does not directly compare the effluent's distribution of these characteristics throughout the output stage of the process to the standards or specification limits issued by the Central Pollution Control Board (CPCB). To fill this knowledge gap, we propose and implement the probability-based Process Capability Indices (PCIs) and Multi - variate Process Flow Indices (MPCIs) in this research. These indices measure the effectiveness of a wastewater treatment process by contrasting the observed results with the predicted ones. PCIs have been widely used as a baseline against which manufacturing processes may be evaluated and tweaked to increase efficiency. The focus of this effort is on PCIs and MPCIs from the standpoint of environmental engineers, with the hope that their use would grow. Using capacity indicators accurately measures the effectiveness of the process of wastewater treatment, which is essential for reducing pollution and permitting the reuse of treated water. This study provides an analysis of the treating wastewater process's capacity by applying appropriate capability indices, using additional information acquired from case studies via literature research. Findings suggest that appropriate capacity indices may allow for more precise assessments of sewage treatment system performance than are presently possible.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49087327","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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