Pub Date : 2022-03-04DOI: 10.1109/ICACTA54488.2022.9753434
Punitha Nicholine J, Preethi D M D
An Intelligent data processing is essential to create a large amount of data in Internet of things. We progress the consistent smooth and computerized uses of artificial intelligence, machine learning, deep Learning. To analyze the data using deep learning that is subcategory of machine learning techniques. This investigation designed and implemented the intelligent system that is used to detect the rise of Covid-19 cases using various artificial intelligent algorithms through machine learning. Here best algorithm is chosen for prediction of Covid 19 Omicron cases based on their accuracy of performance metrics.
{"title":"Artificial Intelligence and Machine Learning Techniques for COVID-19 Prediction","authors":"Punitha Nicholine J, Preethi D M D","doi":"10.1109/ICACTA54488.2022.9753434","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753434","url":null,"abstract":"An Intelligent data processing is essential to create a large amount of data in Internet of things. We progress the consistent smooth and computerized uses of artificial intelligence, machine learning, deep Learning. To analyze the data using deep learning that is subcategory of machine learning techniques. This investigation designed and implemented the intelligent system that is used to detect the rise of Covid-19 cases using various artificial intelligent algorithms through machine learning. Here best algorithm is chosen for prediction of Covid 19 Omicron cases based on their accuracy of performance metrics.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"11 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":"132073121","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/ICACTA54488.2022.9753036
V. Karthik, Balashanmugam K, A. S., A. S, A. S.
We use a number of wireless devices to access the internet in a modern world of technology. Wireless communication is used by most of these devices. Because of the lack of radio spectrum, we can't use the electromagnetic spectrum for a longer period of time. This will lead to network complexity, bandwidth shortages, and increase the risk of interferences between radio frequencies. A new technology in wireless communication called Li-Fi uses light instead of using radio waves to transmission of data and it has an assured future. Data is transferred using light-emitting diodes in the visible spectrum. When compared to Wi-fi, it offers a less delay, higher efficiency, and the ability to transfer high amounts of data. Data is secure using Li-fi because it cannot penetrate walls, so it cannot be hacked. In this paper, the aim is to design Li-fi transceivers for high-speed data and video transmission.
{"title":"High Speed Transmission of Data or Video Over Visible Light Using Li-Fi","authors":"V. Karthik, Balashanmugam K, A. S., A. S, A. S.","doi":"10.1109/ICACTA54488.2022.9753036","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753036","url":null,"abstract":"We use a number of wireless devices to access the internet in a modern world of technology. Wireless communication is used by most of these devices. Because of the lack of radio spectrum, we can't use the electromagnetic spectrum for a longer period of time. This will lead to network complexity, bandwidth shortages, and increase the risk of interferences between radio frequencies. A new technology in wireless communication called Li-Fi uses light instead of using radio waves to transmission of data and it has an assured future. Data is transferred using light-emitting diodes in the visible spectrum. When compared to Wi-fi, it offers a less delay, higher efficiency, and the ability to transfer high amounts of data. Data is secure using Li-fi because it cannot penetrate walls, so it cannot be hacked. In this paper, the aim is to design Li-fi transceivers for high-speed data and video transmission.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"107 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":"132972788","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/ICACTA54488.2022.9752827
S. Pavalarajan, B. Kumar, S. Hammed, K. Haripriya, C. Preethi, T. Mohanraj
Identification of dementia is an important concern in medical image processing. Alzheimer is a common kind of dementia. Four machine learning models were designed for identifying this disease. This is classified as a classification problem, and the classification algorithms tested include logistic regression, support vector classifier, decision tree, and random forest classifier. The models are fine tuned by choosing optimal values for parameters that influences the accuracy of the model. The optimal parameters are found using a K-fold cross validation score, and the models are generated using that. The dataset used in the model is longitudinal cross sectional data from OASIS. It has been inferred from the results that random forest classifier performs well than the other models.
{"title":"Detection of Alzheimer's disease at Early Stage using Machine Learning","authors":"S. Pavalarajan, B. Kumar, S. Hammed, K. Haripriya, C. Preethi, T. Mohanraj","doi":"10.1109/ICACTA54488.2022.9752827","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752827","url":null,"abstract":"Identification of dementia is an important concern in medical image processing. Alzheimer is a common kind of dementia. Four machine learning models were designed for identifying this disease. This is classified as a classification problem, and the classification algorithms tested include logistic regression, support vector classifier, decision tree, and random forest classifier. The models are fine tuned by choosing optimal values for parameters that influences the accuracy of the model. The optimal parameters are found using a K-fold cross validation score, and the models are generated using that. The dataset used in the model is longitudinal cross sectional data from OASIS. It has been inferred from the results that random forest classifier performs well than the other models.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"29 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":"130995943","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/ICACTA54488.2022.9752875
Praveena Nuthakki, J. Manju, R. Geetha, S. M, A. S. Abdullah
The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities that is distinct from previous research. Various brain wave patterns related with common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. In response to these activities, we accumulate various sorts of emotion signals in our brain, such as the Delta, Theta, and Alpha bands, which will provide different types of emotion signals in our brain as a consequence of our actions. When dealing with EEG recordings that are non-stationary in nature, time-frequency domain techniques, on the other hand, are more likely to provide good results. The ability to detect diverse neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. On the basis of several parameters such as filtering response, precision, recall, and F-measure, as well as accuracy and precision, the Matlab simulation software was used to evaluate the performance of the proposed system.
{"title":"A Smart wearable motion sensor and acoustic signal processing based on vocabulary for monitoring children's wellbeing using Big Data","authors":"Praveena Nuthakki, J. Manju, R. Geetha, S. M, A. S. Abdullah","doi":"10.1109/ICACTA54488.2022.9752875","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752875","url":null,"abstract":"The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities that is distinct from previous research. Various brain wave patterns related with common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. In response to these activities, we accumulate various sorts of emotion signals in our brain, such as the Delta, Theta, and Alpha bands, which will provide different types of emotion signals in our brain as a consequence of our actions. When dealing with EEG recordings that are non-stationary in nature, time-frequency domain techniques, on the other hand, are more likely to provide good results. The ability to detect diverse neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. On the basis of several parameters such as filtering response, precision, recall, and F-measure, as well as accuracy and precision, the Matlab simulation software was used to evaluate the performance of the proposed system.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"118 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":"134381437","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/ICACTA54488.2022.9752931
R. Niranjana, K. Narayanan, E. I. Rani, A. Agalya, C. Chandraleka, K. Indhumathi
Blood Vessels play a major role in our vision process. Likewise, the segmentation of theses vascular structure of blood vessels segmentation projects as a critical part in diagnosis of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The accurate way of doing the segmentation of retinal blood vessel is a critical part of analysis of retinal images pertaining to the fundus. Image Processing play a vital role in the medical field. Medical image processing provides very appropriate to diagnoses the various vision threatening diseases like Glaucoma and Diabetic Retinopathy (DR). Nowadays, it is a very growing and challenging field. We proposed a simple supervised approach by using deep learning Convolutional Neural Network. The steps that include in our proposed system are Preprocessing, Segmentation, Feature Extraction, and Classification. Wiener filter is used to de-noise the retinal image. OTSU for segmentation, which separate the foreground and the background and ACO for optimization which enhance the filtered image from Wiener filter. GLCM for feature extraction of the segmented image. For classification, we used a deep learning convolution neural network which provides more iterations. So it will give an appropriate classification for vision threatening diseases. After that a MATLAB software core is implemented.
{"title":"Resourceful Retinal Vessel segmentation for Early Exposure of Vision Threatening Diseases","authors":"R. Niranjana, K. Narayanan, E. I. Rani, A. Agalya, C. Chandraleka, K. Indhumathi","doi":"10.1109/ICACTA54488.2022.9752931","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752931","url":null,"abstract":"Blood Vessels play a major role in our vision process. Likewise, the segmentation of theses vascular structure of blood vessels segmentation projects as a critical part in diagnosis of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The accurate way of doing the segmentation of retinal blood vessel is a critical part of analysis of retinal images pertaining to the fundus. Image Processing play a vital role in the medical field. Medical image processing provides very appropriate to diagnoses the various vision threatening diseases like Glaucoma and Diabetic Retinopathy (DR). Nowadays, it is a very growing and challenging field. We proposed a simple supervised approach by using deep learning Convolutional Neural Network. The steps that include in our proposed system are Preprocessing, Segmentation, Feature Extraction, and Classification. Wiener filter is used to de-noise the retinal image. OTSU for segmentation, which separate the foreground and the background and ACO for optimization which enhance the filtered image from Wiener filter. GLCM for feature extraction of the segmented image. For classification, we used a deep learning convolution neural network which provides more iterations. So it will give an appropriate classification for vision threatening diseases. After that a MATLAB software core is implemented.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"11 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":"122340091","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/ICACTA54488.2022.9753203
P. Menakadevi, J. Ramkumar
Increasing use of social media has increased consumer interest in reading product evaluations and ratings before making a purchase. There is now a mechanism to examine natural language processing, sentiment analysis, and domain adaptation separately. A classifier trained on one set of datasets may underperform when applied to another collection of data. Therefore, it's critical to retain an open mind while experimenting with new classifiers. Reviewing datasets in big data is currently taking place. When applied to large datasets, the sentiment analysis algorithm designed for single machines or small datasets will not perform well. Robust Optimization-based Extreme Learning Machine (ROELM) is a classifier proposed in this work for sentiment analysis in massive data. ROELM is using natural wolf-like behavior to analyze an enormous review database. The single-layer hidden layer of ELM improves classification performance by one factor. This classifier's accuracy and f-measure performance have been assessed. According to the results, the suggested classifier achieves a higher level of classification accuracy than current classifiers.
{"title":"Robust Optimization Based Extreme Learning Machine for Sentiment Analysis in Big Data","authors":"P. Menakadevi, J. Ramkumar","doi":"10.1109/ICACTA54488.2022.9753203","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753203","url":null,"abstract":"Increasing use of social media has increased consumer interest in reading product evaluations and ratings before making a purchase. There is now a mechanism to examine natural language processing, sentiment analysis, and domain adaptation separately. A classifier trained on one set of datasets may underperform when applied to another collection of data. Therefore, it's critical to retain an open mind while experimenting with new classifiers. Reviewing datasets in big data is currently taking place. When applied to large datasets, the sentiment analysis algorithm designed for single machines or small datasets will not perform well. Robust Optimization-based Extreme Learning Machine (ROELM) is a classifier proposed in this work for sentiment analysis in massive data. ROELM is using natural wolf-like behavior to analyze an enormous review database. The single-layer hidden layer of ELM improves classification performance by one factor. This classifier's accuracy and f-measure performance have been assessed. According to the results, the suggested classifier achieves a higher level of classification accuracy than current classifiers.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"84 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":"124638226","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/ICACTA54488.2022.9753581
K. R., Samrudh G R, Gautam, Tejasvi Patil, Sagar Shankar
As the populace of the sector is growing continuously and those are getting older together with it, we must behavior loads of studies in-order to construct higher human carrier robot, because it's miles the destiny. These robots autonomously examine human feelings so we can provide higher offerings to people and be there while its miles required and important. Facial Expression is the maximum essential manner of detecting feelings in people and this is the subject on which the current generation focuses on. To get suitable or higher effects for facial features reputation, we've got proposed 2 strategies: they're double-channel weighted combination deep convolutionary neural community (WMDCNN) that's primarily based totally at the static pics and deep convolutionary neural community lengthy quick period reminiscence community of double channel weighted combination (WMDCNN-LSTM) that's primarily based totally on photograph series. These strategies have a quicker fee for micro facial features detection. The micro facial features are without difficulty diagnosed or detected or diagnosed with the aid of using the WMDCNN andthe bodily capabilities detected withinside the static pics with the aid of using them is dispatched to WMDCNN-LSTM. WMDCNN-LSTM research or acquires those capabilities if you want to accumulatesimilarly the temporal capabilities of the photographseries, via which we will capable of constructing a correct detection version. We have stepped forward the fee of reputation that's higher than the costs in current models.
{"title":"Developing an Intelligent Model to Detect Micro Facial Expression","authors":"K. R., Samrudh G R, Gautam, Tejasvi Patil, Sagar Shankar","doi":"10.1109/ICACTA54488.2022.9753581","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753581","url":null,"abstract":"As the populace of the sector is growing continuously and those are getting older together with it, we must behavior loads of studies in-order to construct higher human carrier robot, because it's miles the destiny. These robots autonomously examine human feelings so we can provide higher offerings to people and be there while its miles required and important. Facial Expression is the maximum essential manner of detecting feelings in people and this is the subject on which the current generation focuses on. To get suitable or higher effects for facial features reputation, we've got proposed 2 strategies: they're double-channel weighted combination deep convolutionary neural community (WMDCNN) that's primarily based totally at the static pics and deep convolutionary neural community lengthy quick period reminiscence community of double channel weighted combination (WMDCNN-LSTM) that's primarily based totally on photograph series. These strategies have a quicker fee for micro facial features detection. The micro facial features are without difficulty diagnosed or detected or diagnosed with the aid of using the WMDCNN andthe bodily capabilities detected withinside the static pics with the aid of using them is dispatched to WMDCNN-LSTM. WMDCNN-LSTM research or acquires those capabilities if you want to accumulatesimilarly the temporal capabilities of the photographseries, via which we will capable of constructing a correct detection version. We have stepped forward the fee of reputation that's higher than the costs in current models.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"23 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":"123408306","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/ICACTA54488.2022.9753364
C. Jamunadevi, P. Ragupathy, P. Sritha, S. Pandikumar, S. Deepa
Epilepsy is a disorder and is identified by baseless seizures that have been associated with unexpected improper neural discharges which result in various health issues and also result in death. One of the most common methods in monitoring and detecting contraction seizures is an electroencephalogram. But it is highly affordable and requires increased temporal resolution. EEG (electroencephalogram) is a commonly used method for monitoring and detecting seizures. The prevalence of EEG seizure detection has increased due to the increasing number of researchers who are focused on developing automated methods to detect the abnormalities in the EEG signals. But, it requires higher temporal resolution and is typically only available for a limited amount of time. Through machine learning, it is possible to extract the details of EEG signals that can help detect seizures. In this paper, the performance analysis is performed under various classifiers such as Random Forest, Gaussian Boosting, and AdaBoost. The results show that Random Forest is the most accurate classifier for achieving high degree of accuracy.
{"title":"Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal","authors":"C. Jamunadevi, P. Ragupathy, P. Sritha, S. Pandikumar, S. Deepa","doi":"10.1109/ICACTA54488.2022.9753364","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753364","url":null,"abstract":"Epilepsy is a disorder and is identified by baseless seizures that have been associated with unexpected improper neural discharges which result in various health issues and also result in death. One of the most common methods in monitoring and detecting contraction seizures is an electroencephalogram. But it is highly affordable and requires increased temporal resolution. EEG (electroencephalogram) is a commonly used method for monitoring and detecting seizures. The prevalence of EEG seizure detection has increased due to the increasing number of researchers who are focused on developing automated methods to detect the abnormalities in the EEG signals. But, it requires higher temporal resolution and is typically only available for a limited amount of time. Through machine learning, it is possible to extract the details of EEG signals that can help detect seizures. In this paper, the performance analysis is performed under various classifiers such as Random Forest, Gaussian Boosting, and AdaBoost. The results show that Random Forest is the most accurate classifier for achieving high degree of accuracy.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"4 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":"128142804","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/ICACTA54488.2022.9753013
Latha A, Gokul N, Chipichakkaravarthy R
This The COVID-19 lockdown had mandated to redesign operations, trigger innovations and embrace contemporary technologies to sustain in retail business. Significant changes in consumption pattern, types of products opted, and place of purchase were observed. This research work studied the shopping behavior of millennial retail customers during the period of lockdown and after relaxations with respect to place of purchase of essential items. Understanding the Millinials preference over the various retail formats including small shops, Modern trade & E commerce sites during and post lockdown is the major objective of this research. The researcher has collected samples from Two hundred and fifty respondents residing in various parts of Tamil Nadu representing Metros, Urban, Semi Urban and Rural. Areas including Chennai, Coimbatore, Vellore, Salem, Madurai, Trichy, Pollachi, Rajapalayam, Bhavani, Kumbakonam, Siruvallur, Poolavadi, Pollavadu, Vellodu and Kattukottai has been identified using the method of convenient sampling for collecting data from the Millinials. Chi-Square and descriptive statistics were conducted as a apart of data analysis. The results implies that Place of Residence and level of Annual Income affects the choice of retailer of millennial customers. Increase in store traffic was observed in modern trade for Groceries (20%), FMCG (24%) and Packaged Food Products (14%) after lockdown restrictions were relaxed. The researcher has also analyzed customer expectations while shopping in online and offline stores through which the authors listed relevant digital technologies for enhancing the shopping experience electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
{"title":"A Study on Consumer Preference Over the Retail Format During and Post Covid Pandemic And Adoption of Digital Technologies to Meet Shopper's Expectations","authors":"Latha A, Gokul N, Chipichakkaravarthy R","doi":"10.1109/ICACTA54488.2022.9753013","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9753013","url":null,"abstract":"This The COVID-19 lockdown had mandated to redesign operations, trigger innovations and embrace contemporary technologies to sustain in retail business. Significant changes in consumption pattern, types of products opted, and place of purchase were observed. This research work studied the shopping behavior of millennial retail customers during the period of lockdown and after relaxations with respect to place of purchase of essential items. Understanding the Millinials preference over the various retail formats including small shops, Modern trade & E commerce sites during and post lockdown is the major objective of this research. The researcher has collected samples from Two hundred and fifty respondents residing in various parts of Tamil Nadu representing Metros, Urban, Semi Urban and Rural. Areas including Chennai, Coimbatore, Vellore, Salem, Madurai, Trichy, Pollachi, Rajapalayam, Bhavani, Kumbakonam, Siruvallur, Poolavadi, Pollavadu, Vellodu and Kattukottai has been identified using the method of convenient sampling for collecting data from the Millinials. Chi-Square and descriptive statistics were conducted as a apart of data analysis. The results implies that Place of Residence and level of Annual Income affects the choice of retailer of millennial customers. Increase in store traffic was observed in modern trade for Groceries (20%), FMCG (24%) and Packaged Food Products (14%) after lockdown restrictions were relaxed. The researcher has also analyzed customer expectations while shopping in online and offline stores through which the authors listed relevant digital technologies for enhancing the shopping experience electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"23 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":"127451110","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/ICACTA54488.2022.9752915
G. Saranya, M. Dharaniga, S.K. Dhanushmathi, R. Dharsheeni
The level of air pollution in cities has grown to become a shocking phenomenon across the country. With the rapid growth of cities and industries, there is a sudden raise in the count of automobiles, power stations, and other manufacturing and industrial facilities. Most cities are facing the problem of lack of air that can meet air quality for good health. The exponential increase in the concentration of pollutants in the atmosphere have resulted in various deadly diseases. The first step is to solve the problem is with high spatial and temporal resolution. It is really necessary to build a system for measuring air pollution and a smart city forecasting system. The project utilizes multiple wireless sensors that monitor pollution through various locations and the location with Global Positioning System (GPS) by using the pollution detection sensor and loading in cloud services and transmitting wireless data to the host. The data collected from the local area is sent to the cloud and hence the data of various concentration of the pollutants undergoes various analysis in the cloud and then using IOT they are displayed on the application and pollution level with respective pollutants concentration are displayed on the app with necessary advisory if incase required so that the user can get benefitted. The cloud also stores these data so that it can be further helpful. The data is then displayed on a mapping service field that enables the user to better understand air quality more easily. The proposed system is useful to monitor and reduce the pollution of the smart city by avoiding the causes of pollution.
{"title":"Air Pollution Monitoring and Mapping Services Using Wireless Sensor Nodes and IoT","authors":"G. Saranya, M. Dharaniga, S.K. Dhanushmathi, R. Dharsheeni","doi":"10.1109/ICACTA54488.2022.9752915","DOIUrl":"https://doi.org/10.1109/ICACTA54488.2022.9752915","url":null,"abstract":"The level of air pollution in cities has grown to become a shocking phenomenon across the country. With the rapid growth of cities and industries, there is a sudden raise in the count of automobiles, power stations, and other manufacturing and industrial facilities. Most cities are facing the problem of lack of air that can meet air quality for good health. The exponential increase in the concentration of pollutants in the atmosphere have resulted in various deadly diseases. The first step is to solve the problem is with high spatial and temporal resolution. It is really necessary to build a system for measuring air pollution and a smart city forecasting system. The project utilizes multiple wireless sensors that monitor pollution through various locations and the location with Global Positioning System (GPS) by using the pollution detection sensor and loading in cloud services and transmitting wireless data to the host. The data collected from the local area is sent to the cloud and hence the data of various concentration of the pollutants undergoes various analysis in the cloud and then using IOT they are displayed on the application and pollution level with respective pollutants concentration are displayed on the app with necessary advisory if incase required so that the user can get benefitted. The cloud also stores these data so that it can be further helpful. The data is then displayed on a mapping service field that enables the user to better understand air quality more easily. The proposed system is useful to monitor and reduce the pollution of the smart city by avoiding the causes of pollution.","PeriodicalId":345370,"journal":{"name":"2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)","volume":"54 10 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":"123655653","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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