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}
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/ICTACS56270.2022.9988540
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/ICTACS56270.2022.9988045
Namit Chawla, Mukul Bedwa
Radiographs of the musculoskeletal system provide significant expertise in the treatment of boned https://stanfordmlgroup.github.io/competitions/mura/isease (BD) or injury. To deal with such conditions Artificial Intelligence (Machine Learning & Deep Learning mainly) can play an important part in diagnosing anomalies in a musculoskeletal system. The approach in the proposed paper aims to create a more efficient computer diagnostics (CBD) model. During the initial stage of research, a few pre-processing techniques are used in the data set selected for wrist radiographs, which eliminates image size variability in radiographs. The given data set was then classified as abnormal or normal using three primary architectures: DenseNet201, Inception V3, and Inception ResNet V2. To improve performance of the model, the model's performance is then improved using ensemble approaches. The suggested approach is put to the test on a widely available MURA dataset also known as the musculoskeletal radiographs dataset, and the obtained outcomes are analyzed with respect to the reference document's current results. An accuracy of 86.49% was achieved for wrist radiographs. The results of the implementation show that the presented process is a worthy strategy for classifying diseases in bones.
{"title":"Optimized Ensemble Learning Technique on Wrist Radiographs using Deep Learning","authors":"Namit Chawla, Mukul Bedwa","doi":"10.1109/ICTACS56270.2022.9988045","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988045","url":null,"abstract":"Radiographs of the musculoskeletal system provide significant expertise in the treatment of boned https://stanfordmlgroup.github.io/competitions/mura/isease (BD) or injury. To deal with such conditions Artificial Intelligence (Machine Learning & Deep Learning mainly) can play an important part in diagnosing anomalies in a musculoskeletal system. The approach in the proposed paper aims to create a more efficient computer diagnostics (CBD) model. During the initial stage of research, a few pre-processing techniques are used in the data set selected for wrist radiographs, which eliminates image size variability in radiographs. The given data set was then classified as abnormal or normal using three primary architectures: DenseNet201, Inception V3, and Inception ResNet V2. To improve performance of the model, the model's performance is then improved using ensemble approaches. The suggested approach is put to the test on a widely available MURA dataset also known as the musculoskeletal radiographs dataset, and the obtained outcomes are analyzed with respect to the reference document's current results. An accuracy of 86.49% was achieved for wrist radiographs. The results of the implementation show that the presented process is a worthy strategy for classifying diseases in bones.","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":"117150726","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}
Telemedicine has the potential to be a good resource for early disease diagnosis, provided that it is utilised in the correct manner. The Internet of Things (IoT) is a concept that has developed in recent years as people have become more aware that they are continuously being watched. As a result of the increased prevalence of neurodegenerative disorders like Alzheimer's disease (AD), biomarkers for these conditions are in high demand for early-stage resource prognosis. Because of the precarious nature of the situation, it is absolutely necessary for these structures to offer remarkable qualities such as accessibility and precision. Deep learning strategies could be useful in fitness applications in situations in which there are a large number of data points to be analysed. Excellent data for a decentralized Internet of Things device that is based on block chain technology. By utilizing a connection to the internet that is of a high speed, it is feasible to obtain a prompt answer from these structures. It is not possible to run deep learning algorithms on smart gateway devices since they do not have sufficient computational capacity. In this study, we investigate the potential for increasing the speed of data flow in the healthcare industry while simultaneously improving data quality through the incorporation of blockchain-based deep neural networks into the control system. Experiments are being conducted to evaluate the speed and accuracy of real-time fitness tracking for the purpose of classifying groups. We are able to determine if diseases of the brain are benign or malignant by employing a model that utilises deep learning. For the purpose of determining the relative severity of each condition, the research examines the symptoms of several different mental diseases and compares them to those of Alzheimer's disease, moderate cognitive impairment, and normal cognition. The research calls for a number of different procedures. The majority of the data is used to train the classifiers, while the remainder of the data is utilised in conjunction with an ensemble model and meta classifier to classify individuals into the appropriate categories. The OASIS-three database is a long-term study that incorporates neuroimaging, cognitive, clinical, and biomarker measurements. This study focuses on healthy ageing as well as Alzheimer's disease. When comparing the outcomes of the simulation to those acquired from the real world, the OASIS-three database (AD), in addition to the ADNI UDS dataset, is employed as a comparison tool. The findings show that answers to questions about this issue can be arrived at quickly and categorized utilizing an in-depth methodology (98% accuracy).
{"title":"Detection of Alzheimer's Disease Using Deep Learning, Blockchain, and IoT Cognitive Data","authors":"Balbir Singh, Manjusha Tatiya, Anurag Shrivastava, Devvret Verma, Arun Pratap Srivastava, A. Rana","doi":"10.1109/ICTACS56270.2022.9988058","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988058","url":null,"abstract":"Telemedicine has the potential to be a good resource for early disease diagnosis, provided that it is utilised in the correct manner. The Internet of Things (IoT) is a concept that has developed in recent years as people have become more aware that they are continuously being watched. As a result of the increased prevalence of neurodegenerative disorders like Alzheimer's disease (AD), biomarkers for these conditions are in high demand for early-stage resource prognosis. Because of the precarious nature of the situation, it is absolutely necessary for these structures to offer remarkable qualities such as accessibility and precision. Deep learning strategies could be useful in fitness applications in situations in which there are a large number of data points to be analysed. Excellent data for a decentralized Internet of Things device that is based on block chain technology. By utilizing a connection to the internet that is of a high speed, it is feasible to obtain a prompt answer from these structures. It is not possible to run deep learning algorithms on smart gateway devices since they do not have sufficient computational capacity. In this study, we investigate the potential for increasing the speed of data flow in the healthcare industry while simultaneously improving data quality through the incorporation of blockchain-based deep neural networks into the control system. Experiments are being conducted to evaluate the speed and accuracy of real-time fitness tracking for the purpose of classifying groups. We are able to determine if diseases of the brain are benign or malignant by employing a model that utilises deep learning. For the purpose of determining the relative severity of each condition, the research examines the symptoms of several different mental diseases and compares them to those of Alzheimer's disease, moderate cognitive impairment, and normal cognition. The research calls for a number of different procedures. The majority of the data is used to train the classifiers, while the remainder of the data is utilised in conjunction with an ensemble model and meta classifier to classify individuals into the appropriate categories. The OASIS-three database is a long-term study that incorporates neuroimaging, cognitive, clinical, and biomarker measurements. This study focuses on healthy ageing as well as Alzheimer's disease. When comparing the outcomes of the simulation to those acquired from the real world, the OASIS-three database (AD), in addition to the ADNI UDS dataset, is employed as a comparison tool. The findings show that answers to questions about this issue can be arrived at quickly and categorized utilizing an in-depth methodology (98% accuracy).","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"25 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":"115023883","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/ICTACS56270.2022.9988531
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/ICTACS56270.2022.9988489
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/ICTACS56270.2022.9988646
P. William, Yaddanapudi Vssrr Uday Kiran, A. Rana, Durgaprasad Gangodkar, Irfan Khan, Kumar Ashutosh
This article describes a system that uses Internet of Things (IOT) architecture to deliver real-time air quality data. Real-time air quality monitoring enables us to limit the degradation of air quality. The degree of pollution in the air is measured using the Air Quality Index (AQI). In general, a higher AQI indicates that the air quality is more dangerous to breathing. With this setup, it is possible to measure gas concentrations such as NO2, CO, and PM2.5 with the help of an Arduino UNO running on both software and hardware. An IoT platform called Thing Speak serves as an IoT analytics platform that is connected to the hardware through the ESP8266 Wi-Fi module in this research. Additionally, it's capable of integrating real-time data with our Android Studio-built mobile phone app. Finally, an Android app that pulls data from Thing Speak displays the PPM and Air Quality levels of gases in the circuit. Successful development of this model has made it suitable for usage in real-world systems.
{"title":"Design and Implementation of IoT based Framework for Air Quality Sensing and Monitoring","authors":"P. William, Yaddanapudi Vssrr Uday Kiran, A. Rana, Durgaprasad Gangodkar, Irfan Khan, Kumar Ashutosh","doi":"10.1109/ICTACS56270.2022.9988646","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988646","url":null,"abstract":"This article describes a system that uses Internet of Things (IOT) architecture to deliver real-time air quality data. Real-time air quality monitoring enables us to limit the degradation of air quality. The degree of pollution in the air is measured using the Air Quality Index (AQI). In general, a higher AQI indicates that the air quality is more dangerous to breathing. With this setup, it is possible to measure gas concentrations such as NO2, CO, and PM2.5 with the help of an Arduino UNO running on both software and hardware. An IoT platform called Thing Speak serves as an IoT analytics platform that is connected to the hardware through the ESP8266 Wi-Fi module in this research. Additionally, it's capable of integrating real-time data with our Android Studio-built mobile phone app. Finally, an Android app that pulls data from Thing Speak displays the PPM and Air Quality levels of gases in the circuit. Successful development of this model has made it suitable for usage in real-world systems.","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":"129432777","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/ICTACS56270.2022.9988278
A. Rana, Virender Khurana, A. Shrivastava, Durgaprasad Gangodkar, Deepika Arora, Anil Kumar Dixit
Wireless sensor networks (WSNs) make use of an abundance of sensor nodes in order to gain a deeper understanding of the world around them. If the data were not gathered in an open and honest fashion, then no one would be interested in them. In military applications, for instance, the detection of opponent movement relies substantially on the placement of sensor nodes in wireless sensor networks (WSNs). Discovering the locations of all target nodes while utilizing anchor nodes is the major purpose of the localization challenge. This research suggests two adjustments that could be made to the zebra optimization algorithm (ZOA) in order to improve upon its deficiencies, one of which being its tendency to get trapped in the local optimal solution. In versions 1 and 2 of the ZOA, the exploration and exploitation components have been modified to make use of improved global and local search algorithms. In order to assess how effective, the proposed ZOA versions 1 and 2 are, a large number of simulations have been run, each with a different combination of target nodes and anchor nodes and a different number of each. In order to solve the problem of node localization, ZOA, along with a number of other attempted optimization strategies, are employed, and the outcomes obtained by each strategy are compared. Versions 1 and 2 of ZOA perform far better than its competitors in terms of the mean localization error, the number of nodes that are successfully localized, and the computation time. ZOA versions 1 and 2 are proposed, and the initial ZOA is evaluated in terms of how accurately it localizes nodes and the number of errors it generates when provided with a range of possible values for the target node and the anchor node. The simulations prove without a reasonable doubt that the suggested ZOA variation 2 performs better than both the existing ZOA and the original proposal in a variety of ways. The proposed ZOA variation 2 is superior to the proposed ZOA variation 1, ZOA, and other existing optimization methods for determining the location of a node because it performs calculations at a faster rate and has a lower mean localization error. This is due to the fact that the proposed ZOA variation 2 is based on a more accurate probability distribution.
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