Pub Date : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753015
S.Sasikala Devi, V. Arulkumar, Saroj Kumar, D. Priyanka
The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet.
{"title":"A Novel system for Tracking and Screening COVID Patients","authors":"S.Sasikala Devi, V. Arulkumar, Saroj Kumar, D. Priyanka","doi":"10.1109/ICACTA54488.2022.9753015","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753015","url":null,"abstract":"The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972466","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9752986
Guru Kiran Reddy K, Angad Pal, Shravan Krishna V, R. J, S. K.
The world is leaping into a future where everything will be automated. Most of the tasks could be completed without the intervention of human beings. Heading in the right direction, chatbots have taken the world by storm. A chatbot is an automated query response system which deals with end users to answer their queries and eliminates the need for customer services. There wouldn't be any delay in providing services if a chatbot handles all the queries in a systematic way. The University website is the place where students, teachers, aspiring teenagers and parents tend to visit to know about the university. By designing a chatbot for the university website which can handle the frequently asked questions we could reduce the workload on the services team. Chatbots fail to answer queries outside their domain of interest, which causes user inconvenience. We have implemented dynamic querying by integrating the chatbot with the SERP API which parses through the internet and generates answers in the form of snippets. This way we have ensured that the chatbot will answer cross domain questions and enhance user experience. Also, the implementation of Speech recognition which records users' query as a voice input helps users to have a quality experience. Using these methods to implement a FAQ chatbot will increase the number of users who visit the website.
{"title":"Cross Domain Answering FAQ Chatbot","authors":"Guru Kiran Reddy K, Angad Pal, Shravan Krishna V, R. J, S. K.","doi":"10.1109/ICACTA54488.2022.9752986","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752986","url":null,"abstract":"The world is leaping into a future where everything will be automated. Most of the tasks could be completed without the intervention of human beings. Heading in the right direction, chatbots have taken the world by storm. A chatbot is an automated query response system which deals with end users to answer their queries and eliminates the need for customer services. There wouldn't be any delay in providing services if a chatbot handles all the queries in a systematic way. The University website is the place where students, teachers, aspiring teenagers and parents tend to visit to know about the university. By designing a chatbot for the university website which can handle the frequently asked questions we could reduce the workload on the services team. Chatbots fail to answer queries outside their domain of interest, which causes user inconvenience. We have implemented dynamic querying by integrating the chatbot with the SERP API which parses through the internet and generates answers in the form of snippets. This way we have ensured that the chatbot will answer cross domain questions and enhance user experience. Also, the implementation of Speech recognition which records users' query as a voice input helps users to have a quality experience. Using these methods to implement a FAQ chatbot will increase the number of users who visit the website.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122359099","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753331
K. Murugan, Kishan S, Akash S M, Krupa Sugumaran
Predicting in what way or manner the stock exchange will act is an individual's ultimate troublesome agenda. There are so many determinant factors that make the declaration -made in advance complicated and concerning the body determinant angle. Concerning the mind, of sound mind, and a careless manner of conduct, etc. All these elements to consider connect to form share prices that are explosive and very difficult to express in advance with a large degree of precision or correctness. Using facial characteristics like new proclamations about a group bound by interest/work/goal, their income results, etc., machine intelligence methods bear the potential to dig up patterns and awareness we didn't visualize before, and these may be used to create accurate and correct declarations made in advance. Here in this method, we have tried random forest, KNN, and ensemble means to express an outcome in advance, the correct results of stock.
{"title":"Stock Market Prognosticate using Machine Learning","authors":"K. Murugan, Kishan S, Akash S M, Krupa Sugumaran","doi":"10.1109/ICACTA54488.2022.9753331","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753331","url":null,"abstract":"Predicting in what way or manner the stock exchange will act is an individual's ultimate troublesome agenda. There are so many determinant factors that make the declaration -made in advance complicated and concerning the body determinant angle. Concerning the mind, of sound mind, and a careless manner of conduct, etc. All these elements to consider connect to form share prices that are explosive and very difficult to express in advance with a large degree of precision or correctness. Using facial characteristics like new proclamations about a group bound by interest/work/goal, their income results, etc., machine intelligence methods bear the potential to dig up patterns and awareness we didn't visualize before, and these may be used to create accurate and correct declarations made in advance. Here in this method, we have tried random forest, KNN, and ensemble means to express an outcome in advance, the correct results of stock.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116251623","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753342
A. Yogeshwar, S. Kamalakkannan
Blockchain technology has the potential to address present interoperability issues in health information systems by establishing a technological standard that ensures safe electronic health data exchange amongst people, healthcare suppliers and medicinal up keep organizations including medical professionals. Patients' sensory data can be fed into Internet of Things (IoT) devices in real timewhich can be evaluated and managed in the healthcare industry. The privacy and security of health data pertaining topatients' is now also a major concern with IoT devices across a wide variety of product sectors. According to previous research, blockchain technology has been determined to be a substantial answer to the data security concerns that present in IoT. The Dynamic Permutation with Multi-Modal Safe Data (DPMMSD) based Hyper Elliptic Curve Cryptography (HECC) Framework (DPMMSD-HECC) is suggested in this research for safeadmission and regulator topatient's data in IoT. The suggested framework of healthcare data management in IoT devices is successfully utilized for fulfilling the optimum confidentiality and safety requirements. Blockchain approach has beenused in this study to develop a dependable and safe data sharing stage that connects several information sources and encrypts and records IoT data in a distributed ledger. A research on security revealed that a particular information secures the parameters related to DPMMSD-HECC model for data analysts and maintains the secrecy of important data from each data source. The recommended strategy is evaluated compared to two benchmark datasets from the UCI AI repository: Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD). The. Simulation results showed that the proposed DPMMSD-HECC model has outperformed all of the other techniques in a number of ways.
{"title":"Building Dynamic permutation based Privacy Preservation Model with Block Chain Technology for IoT Healthcare Sector","authors":"A. Yogeshwar, S. Kamalakkannan","doi":"10.1109/ICACTA54488.2022.9753342","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753342","url":null,"abstract":"Blockchain technology has the potential to address present interoperability issues in health information systems by establishing a technological standard that ensures safe electronic health data exchange amongst people, healthcare suppliers and medicinal up keep organizations including medical professionals. Patients' sensory data can be fed into Internet of Things (IoT) devices in real timewhich can be evaluated and managed in the healthcare industry. The privacy and security of health data pertaining topatients' is now also a major concern with IoT devices across a wide variety of product sectors. According to previous research, blockchain technology has been determined to be a substantial answer to the data security concerns that present in IoT. The Dynamic Permutation with Multi-Modal Safe Data (DPMMSD) based Hyper Elliptic Curve Cryptography (HECC) Framework (DPMMSD-HECC) is suggested in this research for safeadmission and regulator topatient's data in IoT. The suggested framework of healthcare data management in IoT devices is successfully utilized for fulfilling the optimum confidentiality and safety requirements. Blockchain approach has beenused in this study to develop a dependable and safe data sharing stage that connects several information sources and encrypts and records IoT data in a distributed ledger. A research on security revealed that a particular information secures the parameters related to DPMMSD-HECC model for data analysts and maintains the secrecy of important data from each data source. The recommended strategy is evaluated compared to two benchmark datasets from the UCI AI repository: Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD). The. Simulation results showed that the proposed DPMMSD-HECC model has outperformed all of the other techniques in a number of ways.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"113 30","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113945558","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753055
Alisha Banga, Sc Sharma
Due to industrialization and an increase in population, the electricity demand has increased sharply. There is a gap between the supply and requirement of electricity. Electricity forecasting plays a very significant role in power grid as it is required to maintain balance between supply and load demand at all the times, to provide a quality supply of electricity, for financial planning, generation reserve, system security, and many more. Forecasting power is one of the complex problems due to various factors like time and weather. It becomes easier to store relevant data due to technological advancements (Smart Home and Internet of Things-IoT). The electricity consumption data collected through sensor devices can be utilized to know future electricity requirements. In this paper we have applied ten models on the Electricity consumption dataset of house from 11 Jan, 2016, to 27 May 2016 (around 4.5 Months duration) per 10-minute observation. It is observed from the results that Facebook Prophet model is the best performing model.
{"title":"Electricity Demand Forecasting Models at Hourly and Daily Level: A Comparative Study","authors":"Alisha Banga, Sc Sharma","doi":"10.1109/ICACTA54488.2022.9753055","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753055","url":null,"abstract":"Due to industrialization and an increase in population, the electricity demand has increased sharply. There is a gap between the supply and requirement of electricity. Electricity forecasting plays a very significant role in power grid as it is required to maintain balance between supply and load demand at all the times, to provide a quality supply of electricity, for financial planning, generation reserve, system security, and many more. Forecasting power is one of the complex problems due to various factors like time and weather. It becomes easier to store relevant data due to technological advancements (Smart Home and Internet of Things-IoT). The electricity consumption data collected through sensor devices can be utilized to know future electricity requirements. In this paper we have applied ten models on the Electricity consumption dataset of house from 11 Jan, 2016, to 27 May 2016 (around 4.5 Months duration) per 10-minute observation. It is observed from the results that Facebook Prophet model is the best performing model.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368277","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753444
R. Dhivya, N. Shanmugapriya
The noise would be a significant element that affects the quality of leaf images. The level of valuable features that could be extracted from the image has frequently been reduced by the level of noise, also some essential image sections are most often distorted. Image noise has been experimented with by several analysts as the spontaneous variance of illumination or color details within images leads to noises while acquiring. Noise in a leaf image has been the outcome of different forms of errors induced by multiple causes such as the atmosphere and also the instruments involved and is added as a result of errors that arise during processing the image, encoding, and storing. Mainly the effect of Gaussian-Noise (GN) induces higher or lower contrast in both the edge region of the input image that degrades the quality of the leaf images. This research article discusses the strategies and procedures for removing noise from leaf images. The primary objective here would be to upgrade the quality of the leaf image by preprocessing for improving the performance of the automated Leaf Disease Detection (LDD) model. In this research, we propose the following filtering techniques for preprocessing the leaf image “Discrete-Cosine-Transform (DCT)”, “Discrete-Wavelet-Transform (DWT)”, and “K-means Singular-Value-Decomposition and DWT (K-SVD-DWT)”. The superior filtering approach was determined using the metric “Peak-Signal-to-Noise-Ratio (PSNR)”. The outcome of the highest PSNR denoised image can be transmitted into the segmentation task for further LDD process.
{"title":"An Analysis Study of Various Image Preprocessing Filtering Techniques based on PSNR for Leaf Images","authors":"R. Dhivya, N. Shanmugapriya","doi":"10.1109/ICACTA54488.2022.9753444","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753444","url":null,"abstract":"The noise would be a significant element that affects the quality of leaf images. The level of valuable features that could be extracted from the image has frequently been reduced by the level of noise, also some essential image sections are most often distorted. Image noise has been experimented with by several analysts as the spontaneous variance of illumination or color details within images leads to noises while acquiring. Noise in a leaf image has been the outcome of different forms of errors induced by multiple causes such as the atmosphere and also the instruments involved and is added as a result of errors that arise during processing the image, encoding, and storing. Mainly the effect of Gaussian-Noise (GN) induces higher or lower contrast in both the edge region of the input image that degrades the quality of the leaf images. This research article discusses the strategies and procedures for removing noise from leaf images. The primary objective here would be to upgrade the quality of the leaf image by preprocessing for improving the performance of the automated Leaf Disease Detection (LDD) model. In this research, we propose the following filtering techniques for preprocessing the leaf image “Discrete-Cosine-Transform (DCT)”, “Discrete-Wavelet-Transform (DWT)”, and “K-means Singular-Value-Decomposition and DWT (K-SVD-DWT)”. The superior filtering approach was determined using the metric “Peak-Signal-to-Noise-Ratio (PSNR)”. The outcome of the highest PSNR denoised image can be transmitted into the segmentation task for further LDD process.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121870572","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753025
Neenu Sebastian, B. Ankayarkanni
Pneumonia is serious infection that affects the air sacs in our lungs of our body. Air sacs plays a vital role in the procedure of our breathing process. When the lungs are infected by bacterial or viral infection these air sacs will get filled with pus or fluid. As a result, this infection causes fever, cough and leads to a serious medical condition called pneumonia. The severity of this infection can range from mild to severe. It goes to a life-threatening situation in case of infants, young children and old aged people. The doctors use chest X-rays for the confirmation infection. Analyzing the chest x-rays for the detection of pneumonia infection by the doctors visually by naked eyes is time consuming process. Computer aided diagnosis helps the doctors for the faster and accurate detection of Pneumonia infection on chest X-rays. Computer aided diagnosis uses the CNN models for the confirmation of pneumonia which have achieved better performance than humanbeings
{"title":"Deep Learning applications to detect pneumonia on chest X-ray: A systematic study","authors":"Neenu Sebastian, B. Ankayarkanni","doi":"10.1109/ICACTA54488.2022.9753025","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753025","url":null,"abstract":"Pneumonia is serious infection that affects the air sacs in our lungs of our body. Air sacs plays a vital role in the procedure of our breathing process. When the lungs are infected by bacterial or viral infection these air sacs will get filled with pus or fluid. As a result, this infection causes fever, cough and leads to a serious medical condition called pneumonia. The severity of this infection can range from mild to severe. It goes to a life-threatening situation in case of infants, young children and old aged people. The doctors use chest X-rays for the confirmation infection. Analyzing the chest x-rays for the detection of pneumonia infection by the doctors visually by naked eyes is time consuming process. Computer aided diagnosis helps the doctors for the faster and accurate detection of Pneumonia infection on chest X-rays. Computer aided diagnosis uses the CNN models for the confirmation of pneumonia which have achieved better performance than humanbeings","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123966009","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753427
S. Prathibha, Swagata B. Sarkar, Z. M, H. R, S. M, Vibha V, Keerthana Sathish
In today's world the amount of data available to organizations every day continues to proliferate at a staggering volume. Using them in an efficient way enterprises will be able to forecast revenues more accurately, improve overall business and make better decisions about new product investment. Data analytics plays a key role to use these datas effectively and can help enterprises to be more resilient, profitable and sustainable. The data driven from enterprises naturally fall into four different kinds of data analytics namely Descriptive, Diagnostic, Predictive & Prescriptive depending on the question it helps to answer. These can equip the decision makers to describe past results, diagnose past results occurrence, predict future happenings and recommend the necessary actions for the organization's next steps. Armed with deeper insights and recommendations the enterprises can gain a better understanding of their performance as a whole and can make better decisions as a result are termed as Intelligent enterprises. In this work, we will apply a mix of machine learning algorithms like Stacked LSTM model and Tf-idf vectorizer which have been utilized for different types of prediction. The core idea is to showcase of these types of algorithms can effectively predict various kinds of outcomes.
{"title":"Synthesizing Data Analytics towards Intelligent Enterprises","authors":"S. Prathibha, Swagata B. Sarkar, Z. M, H. R, S. M, Vibha V, Keerthana Sathish","doi":"10.1109/ICACTA54488.2022.9753427","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753427","url":null,"abstract":"In today's world the amount of data available to organizations every day continues to proliferate at a staggering volume. Using them in an efficient way enterprises will be able to forecast revenues more accurately, improve overall business and make better decisions about new product investment. Data analytics plays a key role to use these datas effectively and can help enterprises to be more resilient, profitable and sustainable. The data driven from enterprises naturally fall into four different kinds of data analytics namely Descriptive, Diagnostic, Predictive & Prescriptive depending on the question it helps to answer. These can equip the decision makers to describe past results, diagnose past results occurrence, predict future happenings and recommend the necessary actions for the organization's next steps. Armed with deeper insights and recommendations the enterprises can gain a better understanding of their performance as a whole and can make better decisions as a result are termed as Intelligent enterprises. In this work, we will apply a mix of machine learning algorithms like Stacked LSTM model and Tf-idf vectorizer which have been utilized for different types of prediction. The core idea is to showcase of these types of algorithms can effectively predict various kinds of outcomes.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134034583","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753564
Madhavikatamaneni, Riya K S, Anvar Shathik J, K. PoornaPushkala
A strategy for communicating with another person that, if done correctly, maybe easily be understood or accepted by the other person. There are a variety of alternative modes of communication available, including visual representation, body language, conversation, written language, among others. Currently, speech recognition is evolving as a powerful technology in today's world, with applications in a wide range of areas requiring specialised hardware. Voice has a wide range of applications and is frequently regarded as the most powerful mode of communication among all other technologies. The attitude, health status, emotion, gender, and speaker's identity are all considered part of the rich dimension, also known as the rich dimension of communication. Gender and emotion are the significant components of this framework for voice recognition, and they are taken into consideration for a number of applications in this framework for voice recognition. We want to demonstrate an emotion detection system that uses a speech signal as its main input to identify various emotions with this framework. We offer a unique approach for emotion recognition from speech input that uses Artificial Neural Networks (ANN) and is implemented on a Field Programmable Gate Array device (FPGA). In this scenario, the back propagation technique underneath the ANN is utilised as a classifier in the emotion identification system. The emotions are categorised based on their intensity using this approach. Speech pre-processing, feature extraction, and classification are the proposed work's major processing stages. Here, during the features extraction process, characteristics from the data are recovered, such as Cepstrum, Pitch, Mel-frequency cepstral coefficients (MFCC), and the Discrete Wavelet Transform (DWT). In addition, the method of back propagation neural networks is used to achieve the classification task—the proposed work outcomes with the 91.235% accuracy with the less error rate.
{"title":"A Healthcare System for detecting Stress from ECG signals and improving the human emotional","authors":"Madhavikatamaneni, Riya K S, Anvar Shathik J, K. PoornaPushkala","doi":"10.1109/ICACTA54488.2022.9753564","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753564","url":null,"abstract":"A strategy for communicating with another person that, if done correctly, maybe easily be understood or accepted by the other person. There are a variety of alternative modes of communication available, including visual representation, body language, conversation, written language, among others. Currently, speech recognition is evolving as a powerful technology in today's world, with applications in a wide range of areas requiring specialised hardware. Voice has a wide range of applications and is frequently regarded as the most powerful mode of communication among all other technologies. The attitude, health status, emotion, gender, and speaker's identity are all considered part of the rich dimension, also known as the rich dimension of communication. Gender and emotion are the significant components of this framework for voice recognition, and they are taken into consideration for a number of applications in this framework for voice recognition. We want to demonstrate an emotion detection system that uses a speech signal as its main input to identify various emotions with this framework. We offer a unique approach for emotion recognition from speech input that uses Artificial Neural Networks (ANN) and is implemented on a Field Programmable Gate Array device (FPGA). In this scenario, the back propagation technique underneath the ANN is utilised as a classifier in the emotion identification system. The emotions are categorised based on their intensity using this approach. Speech pre-processing, feature extraction, and classification are the proposed work's major processing stages. Here, during the features extraction process, characteristics from the data are recovered, such as Cepstrum, Pitch, Mel-frequency cepstral coefficients (MFCC), and the Discrete Wavelet Transform (DWT). In addition, the method of back propagation neural networks is used to achieve the classification task—the proposed work outcomes with the 91.235% accuracy with the less error rate.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131448255","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 : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753433
N. Chandolikar, Chirag Dale, Tejas Koli, Mayank Singh, Tarussh Narkhede
As India has an agro-based economy, 58% of its population relies on agriculture as its primary method of livelihood. In spite of this, the economic survey for 2019–2020 indicates that agriculture growth in India has stagnated around 2.9% annually for the past 6 years. Considering the number of people in India still relying on it, it is a real concern. One of the prevailing issues is lack of right information. This problem can be solved by providing farmers with expert advice and relevant information (e.g. determine when to irrigate, how to sow seeds, and which pesticides to use effectively to increase the yields). In this paper, the proposed chatbot called AgroBot is a multi-user chat application. AgroBot can overcome this problem by allowing farmers to obtain the information they need to succeed in an ever-changing market and to enlarge with new technology and market demand in an easy-to-use manner. Farmers can communicate easily with the chatbot since the system uses NLP (Natural Language Processing) to identify and parse farmer inquiries, identify the main key words and their questions, identify the main keywords and compare them to the Knowledge Base, and provide the best possible results. The development of such a system would benefit farmers by allowing them to gain better information about agricultural practices and, as a result, increase agricultural productivity.
{"title":"Agriculture Assistant Chatbot Using Artificial Neural Network","authors":"N. Chandolikar, Chirag Dale, Tejas Koli, Mayank Singh, Tarussh Narkhede","doi":"10.1109/ICACTA54488.2022.9753433","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753433","url":null,"abstract":"As India has an agro-based economy, 58% of its population relies on agriculture as its primary method of livelihood. In spite of this, the economic survey for 2019–2020 indicates that agriculture growth in India has stagnated around 2.9% annually for the past 6 years. Considering the number of people in India still relying on it, it is a real concern. One of the prevailing issues is lack of right information. This problem can be solved by providing farmers with expert advice and relevant information (e.g. determine when to irrigate, how to sow seeds, and which pesticides to use effectively to increase the yields). In this paper, the proposed chatbot called AgroBot is a multi-user chat application. AgroBot can overcome this problem by allowing farmers to obtain the information they need to succeed in an ever-changing market and to enlarge with new technology and market demand in an easy-to-use manner. Farmers can communicate easily with the chatbot since the system uses NLP (Natural Language Processing) to identify and parse farmer inquiries, identify the main key words and their questions, identify the main keywords and compare them to the Knowledge Base, and provide the best possible results. The development of such a system would benefit farmers by allowing them to gain better information about agricultural practices and, as a result, increase agricultural productivity.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131472582","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：