Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988176
D. R. D. Varma, R. Priyanka
The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.
{"title":"Implementation and Performance Analysis of Novel Support Vector Machine Classifier for Detecting Eye Cancer Image in comparison with Decision Tree","authors":"D. R. D. Varma, R. Priyanka","doi":"10.1109/ICTACS56270.2022.9988176","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988176","url":null,"abstract":"The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131481299","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988489
Bhumika Gupta, N. Pokhriyal, K. K. Gola, Mridula
The detection of depression is a critical issue for human well-being. Previous research has shown us that online detection is successful in social media, allowing for proactive intervention for depressed users. It is a serious psychological disorder and it takes hold of more than 300 million people across the globe. A person who is depressed experience anxiety and low self-esteem in their everyday life, which affects their relationships with their family and friends, and can lead to various diseases and, in the most extreme scenario, suicide. With the rise of social media, the majority of individuals now use it to express their emotions, feelings, and thoughts. If a person's depression can be discovered early by analyzing their post, then essential efforts can be taken to save them from depression-related disorders or, in the best scenario, from suicide. The main goal of our work is to inspect Reddit user posts to see whether any factors suggest depression attitudes among relevant internet users. We use sentiment examination and Machine Learning (ML) techniques to train the ML model and assess the efficacy of our suggested strategy for this goal. A lexicon of phrases that are more common in depressed accounts is identified. In this study, we have combined Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) to build a hybrid model that can predict depression by evaluating user textual messages.
{"title":"Detecting Depression in Reddit Posts using Hybrid Deep Learning Model LSTM-CNN","authors":"Bhumika Gupta, N. Pokhriyal, K. K. Gola, Mridula","doi":"10.1109/ICTACS56270.2022.9988489","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988489","url":null,"abstract":"The detection of depression is a critical issue for human well-being. Previous research has shown us that online detection is successful in social media, allowing for proactive intervention for depressed users. It is a serious psychological disorder and it takes hold of more than 300 million people across the globe. A person who is depressed experience anxiety and low self-esteem in their everyday life, which affects their relationships with their family and friends, and can lead to various diseases and, in the most extreme scenario, suicide. With the rise of social media, the majority of individuals now use it to express their emotions, feelings, and thoughts. If a person's depression can be discovered early by analyzing their post, then essential efforts can be taken to save them from depression-related disorders or, in the best scenario, from suicide. The main goal of our work is to inspect Reddit user posts to see whether any factors suggest depression attitudes among relevant internet users. We use sentiment examination and Machine Learning (ML) techniques to train the ML model and assess the efficacy of our suggested strategy for this goal. A lexicon of phrases that are more common in depressed accounts is identified. In this study, we have combined Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) to build a hybrid model that can predict depression by evaluating user textual messages.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130048012","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987838
I. Muda, S. Madem, Shahriar Hasan, Sohel Ahmod, R. A. Kayande, Nilanjan Chakraborty
One of the most talked-about topics of the past few years, block chain technology has already influenced numerous industries and businesses, altering the lives of countless people in the process. While the features of block chain technologies have the potential to provide us with more trustworthy and convenient services, there are still significant security concerns that must be addressed. The primary objective of this work is to explain and convey the idea of block chain, its modern-day uses in the business sector, and the numerous dangers and security challenges associated with block chain technology. The widespread adoption of block chain technology has the potential to solve the intractable trust problems in a variety of industries.
{"title":"A Survey on Applications and Security Issues of Blockchain Technology in Business Sectors","authors":"I. Muda, S. Madem, Shahriar Hasan, Sohel Ahmod, R. A. Kayande, Nilanjan Chakraborty","doi":"10.1109/ICTACS56270.2022.9987838","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987838","url":null,"abstract":"One of the most talked-about topics of the past few years, block chain technology has already influenced numerous industries and businesses, altering the lives of countless people in the process. While the features of block chain technologies have the potential to provide us with more trustworthy and convenient services, there are still significant security concerns that must be addressed. The primary objective of this work is to explain and convey the idea of block chain, its modern-day uses in the business sector, and the numerous dangers and security challenges associated with block chain technology. The widespread adoption of block chain technology has the potential to solve the intractable trust problems in a variety of industries.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120842746","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988540
Susheel George Joseph, M. Ashraf, A. Srivastava, Bhasker Pant, A. Rana, Ankita Joshi
Potatoes are grown commercially in practically every country in the world. Unfortunately, the crop has been affected by a number of different diseases. In order for the gardener to take quick action, they need to have an understanding of the nature of the contamination. They had the notion that if they looked closely at the leaves, they would be able to learn more about the diseases that were plaguing their communities. Many different Convolutional Neural Network (CNN) models and Machine Learning (ML) methodologies have been created in order to provide assistance to farmers in the diagnosis of diseases affecting tomato crops. Deep Learning and Neural Networks are used in the construction of CNN models. This gives CNN models an advantage over other Machine Learning approaches, such as k-NN and Decision Trees. Because it must handle such a wide array of inputs, the notoriously challenging Pre-skilled CNN is notoriously tough to programme. However, it is capable of producing incredible works of art. An outline of a model for a convolutional neural network that is simpler to understand is provided here. It consists of a total of eight hidden levels. The suggested lightweight model beats both state-of-the-art machine learning approaches and pre-trained models in terms of accuracy when applied to the Plant Village dataset, which is available to the general public. The Plant Village dataset has 39 classes, and these classes collectively represent a large number of different plant species. There are ten different diseases that may infect tomato plants, all of which have the potential to inflict damage. While k-NN has the best accuracy (94.9%) among the classic machine learning methods, VGG16 performs exceptionally well among the trained models. After the picture improvement was finished, the images were pre-processed so that the effectiveness of the suggested CNN may be increased. To be more specific, we accomplished this by considering the width of the picture as a random variable and, as a result, altering the brightness of the image correspondingly. On data sets that have nothing to do with Plant Village, the suggested model achieves an outstanding accuracy of 98%.
{"title":"CNN-based Early Blight and Late Blight Disease Detection on Potato Leaves","authors":"Susheel George Joseph, M. Ashraf, A. Srivastava, Bhasker Pant, A. Rana, Ankita Joshi","doi":"10.1109/ICTACS56270.2022.9988540","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988540","url":null,"abstract":"Potatoes are grown commercially in practically every country in the world. Unfortunately, the crop has been affected by a number of different diseases. In order for the gardener to take quick action, they need to have an understanding of the nature of the contamination. They had the notion that if they looked closely at the leaves, they would be able to learn more about the diseases that were plaguing their communities. Many different Convolutional Neural Network (CNN) models and Machine Learning (ML) methodologies have been created in order to provide assistance to farmers in the diagnosis of diseases affecting tomato crops. Deep Learning and Neural Networks are used in the construction of CNN models. This gives CNN models an advantage over other Machine Learning approaches, such as k-NN and Decision Trees. Because it must handle such a wide array of inputs, the notoriously challenging Pre-skilled CNN is notoriously tough to programme. However, it is capable of producing incredible works of art. An outline of a model for a convolutional neural network that is simpler to understand is provided here. It consists of a total of eight hidden levels. The suggested lightweight model beats both state-of-the-art machine learning approaches and pre-trained models in terms of accuracy when applied to the Plant Village dataset, which is available to the general public. The Plant Village dataset has 39 classes, and these classes collectively represent a large number of different plant species. There are ten different diseases that may infect tomato plants, all of which have the potential to inflict damage. While k-NN has the best accuracy (94.9%) among the classic machine learning methods, VGG16 performs exceptionally well among the trained models. After the picture improvement was finished, the images were pre-processed so that the effectiveness of the suggested CNN may be increased. To be more specific, we accomplished this by considering the width of the picture as a random variable and, as a result, altering the brightness of the image correspondingly. On data sets that have nothing to do with Plant Village, the suggested model achieves an outstanding accuracy of 98%.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122298050","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988462
Apeksha Koul, Yogesh Kumar, Anish Gupta
Bladder cancer is currently the most frequent and worst cancer in the United States. Over the last several decades, bladder cancer detection and therapy breakthroughs have significantly reduced its mortality. Cystoscopy treatment has been considered useful for detecting and treating bladder cancer (BCa), but it is also prone to certain complications. Hence, this study has explored numerous research methodologies for identifying and diagnosing bladder cancer using AI techniques such as machine learning and deep learning models. The paper also emphasizes the accomplishments and challenges of researchers in this field. The assessment of the various techniques has also been compared to draw some conclusions.
{"title":"A Study on Bladder Cancer Detection using AI-based Learning Techniques","authors":"Apeksha Koul, Yogesh Kumar, Anish Gupta","doi":"10.1109/ICTACS56270.2022.9988462","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988462","url":null,"abstract":"Bladder cancer is currently the most frequent and worst cancer in the United States. Over the last several decades, bladder cancer detection and therapy breakthroughs have significantly reduced its mortality. Cystoscopy treatment has been considered useful for detecting and treating bladder cancer (BCa), but it is also prone to certain complications. Hence, this study has explored numerous research methodologies for identifying and diagnosing bladder cancer using AI techniques such as machine learning and deep learning models. The paper also emphasizes the accomplishments and challenges of researchers in this field. The assessment of the various techniques has also been compared to draw some conclusions.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127223099","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987971
H. S, S. Raman, Pitty Sanjay, S. Latha, P. Muthu, S. Dhanalakshmi
On comparing diseases that cause major mortality, skin lesions are frequently considered of as minor players in the worldwide league of illness. Melanoma and Melanocytic nevus are skin cancers that have a high fatality rate. In the early stages of skin lesions, accurate classification can help doctors save a patient's life. Even when dermatologists utilize photos to diagnose, specialists' correct diagnosis rates are believed to be 75–84 percent. The purpose of this study is to use machine learning to pre-classify skin lesions as Melanoma or Melanocytic nevus, and to build a decision support system to assist doctors and differential diagnosticians in making better decisions.
{"title":"Skin Lesion Classification using Machine Learning Algorithm for Differential Diagnosis","authors":"H. S, S. Raman, Pitty Sanjay, S. Latha, P. Muthu, S. Dhanalakshmi","doi":"10.1109/ICTACS56270.2022.9987971","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987971","url":null,"abstract":"On comparing diseases that cause major mortality, skin lesions are frequently considered of as minor players in the worldwide league of illness. Melanoma and Melanocytic nevus are skin cancers that have a high fatality rate. In the early stages of skin lesions, accurate classification can help doctors save a patient's life. Even when dermatologists utilize photos to diagnose, specialists' correct diagnosis rates are believed to be 75–84 percent. The purpose of this study is to use machine learning to pre-classify skin lesions as Melanoma or Melanocytic nevus, and to build a decision support system to assist doctors and differential diagnosticians in making better decisions.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125952776","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988553
T. Aruna, P. Naresh, A. Rajeshwari, M. Hussan, K. G. Guptha
The sheer quantity of big information has created tremendous prospects for forecasts and study. Infographic is a normal sight in everyday life. Several trend lines explain the pragmatic approach to weather assessment using interactive media. Because it was Previously unable to evaluate huge data, visual analytic tools have made plotting the system quality. For a good knowledge of the conditions, maps are used. Graphing trends like The precipitation of India have been presented with the maximum, minimum, and medium precipitation in the U.s Districts. The precipitation trend in India's provinces and territories was correctly illustrated in this article. The recurring sequence highlights extremely dry areas.
{"title":"Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques","authors":"T. Aruna, P. Naresh, A. Rajeshwari, M. Hussan, K. G. Guptha","doi":"10.1109/ICTACS56270.2022.9988553","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988553","url":null,"abstract":"The sheer quantity of big information has created tremendous prospects for forecasts and study. Infographic is a normal sight in everyday life. Several trend lines explain the pragmatic approach to weather assessment using interactive media. Because it was Previously unable to evaluate huge data, visual analytic tools have made plotting the system quality. For a good knowledge of the conditions, maps are used. Graphing trends like The precipitation of India have been presented with the maximum, minimum, and medium precipitation in the U.s Districts. The precipitation trend in India's provinces and territories was correctly illustrated in this article. The recurring sequence highlights extremely dry areas.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126426533","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988712
D. R. D. Varma, R. Priyanka
The novel performance analysis of prewitt algorithm for iris monitoring in comparison with the sobel to improve the Signal to Noise Ratio (SNR) for improving strength of the signal using. Materials and Methods: The 40 samples were collected using the g power clinical calculator. G1 as the prewitt algorithm with 20 samples and g2 as the sobel algorithm with 20 samples. 80% of power is prescribed for pretest and the acceptable error of 0.05 were used to identify the number of samples. Results: The prewitt algorithm has achieved the predominant performance accuracy of 94.0% when compared to the sobel algorithm with 87.85% of accuracy. The prewitt algorithm has the implication of ($mathrm{p} < 0.05$) with the sobel algorithm. Conclusion: The prewitt algorithm is implified greater accuracy when compared with the sobel algorithm.
{"title":"Implementation and Analysis of Novel Iris Monitoring System using Prewitt Algorithm in comparing with Sobel Algorithms by Signal-to-Noise Ratio","authors":"D. R. D. Varma, R. Priyanka","doi":"10.1109/ICTACS56270.2022.9988712","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988712","url":null,"abstract":"The novel performance analysis of prewitt algorithm for iris monitoring in comparison with the sobel to improve the Signal to Noise Ratio (SNR) for improving strength of the signal using. Materials and Methods: The 40 samples were collected using the g power clinical calculator. G1 as the prewitt algorithm with 20 samples and g2 as the sobel algorithm with 20 samples. 80% of power is prescribed for pretest and the acceptable error of 0.05 were used to identify the number of samples. Results: The prewitt algorithm has achieved the predominant performance accuracy of 94.0% when compared to the sobel algorithm with 87.85% of accuracy. The prewitt algorithm has the implication of ($mathrm{p} < 0.05$) with the sobel algorithm. Conclusion: The prewitt algorithm is implified greater accuracy when compared with the sobel algorithm.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133676280","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988531
Manish Rao Ghatge, S. Barde
Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).
{"title":"Comparison of CNN-LSTM in Sentiment Analysis for Hindi Mix Language","authors":"Manish Rao Ghatge, S. Barde","doi":"10.1109/ICTACS56270.2022.9988531","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988531","url":null,"abstract":"Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130799886","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987965
Roza Maria Irodah, A. Adriansyah
The main factors affecting the performance of Local Water Company (LWC) when managing consumable water distribution in Indonesia are non-revenue water, less water usage effectiveness, less efficiency of billing records and customer complaints about services not becoming available for up to 24 hours. The factor happens because the process is still done manually. So errors and fraud are often found. This research aims to provide a solution by proposing the design of an LWC recording and billing system with a practical and safe prepaid Self-Service method. The prepaid Self-Service process is divided into two main functions. First, the real-time calculation function is designed to solve the efficiency problem in recording water usage. Second, the self-payment token's process is designed to resolve data processing and bill payment constraints. It generated tokens for self-payment token functions built using the Vernam Cipher Cryptographic Algorithm. An Android platform with an Arduino IDE is used in this system. A token will be sent to other devices through Bluetooth serial communication. The results were successfully performed using the Vernam Cipher Cryptographic Algorithm for the self-payment token function. The encryption token consisting of 48 characters can be automatically transferred to other devices using Bluetooth serial communication. The encryption process takes about 0.34 seconds, and the decryption takes about 0.20 seconds.
{"title":"Analysis and Design of Self-service Local Water Company (LWC) using Vernam Cipher Cryptography Algorithm","authors":"Roza Maria Irodah, A. Adriansyah","doi":"10.1109/ICTACS56270.2022.9987965","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987965","url":null,"abstract":"The main factors affecting the performance of Local Water Company (LWC) when managing consumable water distribution in Indonesia are non-revenue water, less water usage effectiveness, less efficiency of billing records and customer complaints about services not becoming available for up to 24 hours. The factor happens because the process is still done manually. So errors and fraud are often found. This research aims to provide a solution by proposing the design of an LWC recording and billing system with a practical and safe prepaid Self-Service method. The prepaid Self-Service process is divided into two main functions. First, the real-time calculation function is designed to solve the efficiency problem in recording water usage. Second, the self-payment token's process is designed to resolve data processing and bill payment constraints. It generated tokens for self-payment token functions built using the Vernam Cipher Cryptographic Algorithm. An Android platform with an Arduino IDE is used in this system. A token will be sent to other devices through Bluetooth serial communication. The results were successfully performed using the Vernam Cipher Cryptographic Algorithm for the self-payment token function. The encryption token consisting of 48 characters can be automatically transferred to other devices using Bluetooth serial communication. The encryption process takes about 0.34 seconds, and the decryption takes about 0.20 seconds.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133373751","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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