Pub Date : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117729
Neetima Agarwal, Arpana Kumari
The workplace is going digital with the inclusion of artificial intelligence tools. These tools are deeply reshaping the service industry and influencing customer relationship management. There have been various studies that have shown the correlation between technology and its effect on the organization. Through the study, the effect of AI tools on the EmQ of Customer Care Executives has been analyzed as perceived by the customers. The study highlights the effect of AI tools on the affective and cognitive empathy of the CCE and thus on responsiveness on the job.
{"title":"Effect of Artificial Intelligent on Empathy Quotient (EmQ) and Responsiveness of Customer Care Executive- A Study from Customer's Lenses","authors":"Neetima Agarwal, Arpana Kumari","doi":"10.1109/ICIPTM57143.2023.10117729","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117729","url":null,"abstract":"The workplace is going digital with the inclusion of artificial intelligence tools. These tools are deeply reshaping the service industry and influencing customer relationship management. There have been various studies that have shown the correlation between technology and its effect on the organization. Through the study, the effect of AI tools on the EmQ of Customer Care Executives has been analyzed as perceived by the customers. The study highlights the effect of AI tools on the affective and cognitive empathy of the CCE and thus on responsiveness on the job.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163623","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118162
Arman Raj, Vandana Sharma, S. Rani, B. Balusamy, Ankit Kumar Shanu, A. Alkhayyat
Big Data has influenced almost every sector such as banking, agriculture, Healthcare, Manufacturing and Natural Resources, Government, Communication, Entertainment Industry, Insurance and Education. Moreover, applications of Big Data especially in education sector have been exponentially increased. Big Data is currently a buzzword in both educational sector with the term being used to describe a wide range of concepts, ranging from extricating data from outside sources, storing and properly managing it and to processing it such data with inquisitive methods and tools. Big Data has significantly helped to improve Technology Enabled Learning (TEL) and Outcome Based Education (OBE). With the proliferation in these, AI-based Ed-Tech tools in Big Data, TEL has able to elongate and enhanced personalized learning. The numerous challenges faced by Ed-tech tools are data privacy issues, data quality issues, data storage issues and data analysis issues. In this paper, authors have presented a comprehensive review on AI based Ed-Tech tools using Big Data on parameters like size limit, data loading, Type of Data, user-interface and features.
{"title":"Revealing AI-Based Ed-Tech Tools Using Big Data","authors":"Arman Raj, Vandana Sharma, S. Rani, B. Balusamy, Ankit Kumar Shanu, A. Alkhayyat","doi":"10.1109/ICIPTM57143.2023.10118162","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118162","url":null,"abstract":"Big Data has influenced almost every sector such as banking, agriculture, Healthcare, Manufacturing and Natural Resources, Government, Communication, Entertainment Industry, Insurance and Education. Moreover, applications of Big Data especially in education sector have been exponentially increased. Big Data is currently a buzzword in both educational sector with the term being used to describe a wide range of concepts, ranging from extricating data from outside sources, storing and properly managing it and to processing it such data with inquisitive methods and tools. Big Data has significantly helped to improve Technology Enabled Learning (TEL) and Outcome Based Education (OBE). With the proliferation in these, AI-based Ed-Tech tools in Big Data, TEL has able to elongate and enhanced personalized learning. The numerous challenges faced by Ed-tech tools are data privacy issues, data quality issues, data storage issues and data analysis issues. In this paper, authors have presented a comprehensive review on AI based Ed-Tech tools using Big Data on parameters like size limit, data loading, Type of Data, user-interface and features.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130397626","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}
In the fast-paced Information and communication technology, cyber-crimes are also evolving and growing very fast thereby increasing damage of the organizations and individuals universally. This paper is an attempt to get an overview of the different trends of cyber-crimes, to spread awareness about the cyber-crimes among people to increase security of the people of Delhi and NCR from cyber-crimes. Since Internet has become a basic need of life in metro cities today for almost every individual, increased dependence on Internet has led to the rise of cyber-crime and one of the best ways of protection from cybercrimes is its awareness. The paper intends to understand the level and intensity of awareness about various cyber-crimes present in the era of Society 4.0 in capital of India. The paper also identifies the importance of being acquainted with the effects of cyber-crime and awareness of the methods of prevention.
{"title":"Growth of Cyber-crimes in Society 4.0","authors":"Vinita Sharma, Tanu Manocha, Seema Garg, Saatwik Sharma, Anshita Garg, Ritu Sharma","doi":"10.1109/ICIPTM57143.2023.10118185","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118185","url":null,"abstract":"In the fast-paced Information and communication technology, cyber-crimes are also evolving and growing very fast thereby increasing damage of the organizations and individuals universally. This paper is an attempt to get an overview of the different trends of cyber-crimes, to spread awareness about the cyber-crimes among people to increase security of the people of Delhi and NCR from cyber-crimes. Since Internet has become a basic need of life in metro cities today for almost every individual, increased dependence on Internet has led to the rise of cyber-crime and one of the best ways of protection from cybercrimes is its awareness. The paper intends to understand the level and intensity of awareness about various cyber-crimes present in the era of Society 4.0 in capital of India. The paper also identifies the importance of being acquainted with the effects of cyber-crime and awareness of the methods of prevention.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132989349","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117778
Sabari L Uma Maheswari, Resna S R, R. Yalini, A. M, R. Pandian, G. P
The seven-level flowing, dynamic, unbiased, point-cinched converter of the half-breed. The converter geography is made up of an H-span for each stage and a three-level Active Neural Point Clamped (ANPC) converter. Through the selection of the converter's exchanging circumstances, the voltage of the H-span is ferociously maintained with fundamental force. With extensive geographic reenactment effects, working ethics, voltage regulating techniques, and converter restrictions are jointly studied. By directing the exchanging obligation patterns of 2 PWM signals, which veer the activity event of excess exchanging states in each exchanging cycle, the voltage slantingly the flying capacitor is also synchronised. There are recreation and trial grades available to demonstrate the effectiveness of this tactic. a method for altering the voltage of capacitors, including flying and dc-interface capacitors, for the 7 level ANPC (7L-ANPC) converters. 7L-ANPC converters are worked at major repetition rates whereas various switches are worked with a constant exchanging repetition rate. to test the connection among the zero grouping voltage and the typical impartial point current. The impartial point potential is meant to be controlled by an ideal zero-arrangement voltage. Altering the trading responsibility cycles also synchronises the voltage across the flying capacitor. Every time a recurrent swapping state occurs throughout an exchange period, it is altered. It is possible to test the validity of this tactic using simulation and exploratory data.
{"title":"Space vector Pulse Width Modulation with 7 Level ANPC Converters for Capacitor Voltage Balancing","authors":"Sabari L Uma Maheswari, Resna S R, R. Yalini, A. M, R. Pandian, G. P","doi":"10.1109/ICIPTM57143.2023.10117778","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117778","url":null,"abstract":"The seven-level flowing, dynamic, unbiased, point-cinched converter of the half-breed. The converter geography is made up of an H-span for each stage and a three-level Active Neural Point Clamped (ANPC) converter. Through the selection of the converter's exchanging circumstances, the voltage of the H-span is ferociously maintained with fundamental force. With extensive geographic reenactment effects, working ethics, voltage regulating techniques, and converter restrictions are jointly studied. By directing the exchanging obligation patterns of 2 PWM signals, which veer the activity event of excess exchanging states in each exchanging cycle, the voltage slantingly the flying capacitor is also synchronised. There are recreation and trial grades available to demonstrate the effectiveness of this tactic. a method for altering the voltage of capacitors, including flying and dc-interface capacitors, for the 7 level ANPC (7L-ANPC) converters. 7L-ANPC converters are worked at major repetition rates whereas various switches are worked with a constant exchanging repetition rate. to test the connection among the zero grouping voltage and the typical impartial point current. The impartial point potential is meant to be controlled by an ideal zero-arrangement voltage. Altering the trading responsibility cycles also synchronises the voltage across the flying capacitor. Every time a recurrent swapping state occurs throughout an exchange period, it is altered. It is possible to test the validity of this tactic using simulation and exploratory data.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126456689","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}
Green technology is critical to reaching the global sustainable development goals. It is critical to understand and analyze why different people adopt green technologies in different ways. Despite the fact that we recognize that numerous factors influence adoption, there is still a general lack of desire to accept new green technologies. This research is an attempt to find the level of knowledge and adoption of green technologies by residents of Delhi and the NCR region. This research advances knowledge of a better understanding of green technology awareness and uptake. The findings are consistent, and people are aware of Green Technologies. The demographic profile of the respondents have a statistically significant influence on the application of these green technologies.
{"title":"Factors Affecting Awareness and Practices of Green Technology","authors":"Vinita Sharma, Tanu Manocha, Seema Garg, Dr. Anchal Luthra, Shivani Dixit, Meghna Sharma","doi":"10.1109/ICIPTM57143.2023.10118326","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118326","url":null,"abstract":"Green technology is critical to reaching the global sustainable development goals. It is critical to understand and analyze why different people adopt green technologies in different ways. Despite the fact that we recognize that numerous factors influence adoption, there is still a general lack of desire to accept new green technologies. This research is an attempt to find the level of knowledge and adoption of green technologies by residents of Delhi and the NCR region. This research advances knowledge of a better understanding of green technology awareness and uptake. The findings are consistent, and people are aware of Green Technologies. The demographic profile of the respondents have a statistically significant influence on the application of these green technologies.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876922","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117651
Sujin Jose Arul, Mithilesh B S, S. L, Sufiyan, Gopal Kaliyaperumal, Jayasheel Kumar K A
Saving time is very essential for humans. Every day people are spending some time at the traffic signal due to the drawbacks of the conventional traffic light system. In the existing traffic light system, a defined timer system is used and it is working based on preset timing. Due to the preset timing, there is no flexibility of ON/OFF in the signal light based on the emergency vehicle and congestion of the vehicle. Sometimes emergency vehicle like an ambulance needs to wait at a traffic signal for a long time and this would lead to a risk to a patient's life. Traffic police must personally identify an ambulance and release the congestion, but this is not possible as there are an enormous number of vehicles present these days. This project aims to providea solution for the issue in the conventional system. The model was designed using an image processing system that reads the image and determines the presence of an emergency vehicle and the density of vehicles in each lane the ON/OFF signal for the particular lane will be given to the traffic light system which helpsto reduce the unnecessary waiting time of vehicles. The system calculates the vehicle's density and to detect the emergency vehicle using image processing to provide the green light signal tothe lane. This project used Open CV and Yolo (you only look once)algorithm in the image processing method to develop the system. The simulation has been done on the proposed smart traffic systemand it identifies that the proposed system is efficient. Multiple times of programming and testing have been done on the proposedsystem to ensure accuracy and for validation.
节约时间对人类来说是非常重要的。由于传统交通信号灯系统的缺点，每天人们都要在交通信号灯前花费一些时间。在现有的交通灯系统中，使用的是一个定义好的定时系统，它是基于预设的定时进行工作的。由于时间是预先设定的，没有根据应急车辆和车辆的拥堵情况灵活选择信号灯的开/关。有时像救护车这样的紧急车辆需要在交通信号处等待很长时间，这可能会危及病人的生命。交通警察必须亲自识别救护车并疏导拥堵，但由于目前车辆数量庞大，这是不可能的。本项目旨在为常规系统中的问题提供解决方案。该模型使用图像处理系统进行设计，该系统读取图像并确定每条车道上是否存在紧急车辆和车辆密度，并将特定车道的开/关信号发送给交通灯系统，从而减少车辆不必要的等待时间。该系统计算车辆密度，利用图像处理技术检测紧急车辆，为车道提供绿灯信号。本项目采用Open CV和Yolo (you only look once)算法中的图像处理方法来开发系统。对所提出的智能交通系统进行了仿真，结果表明所提出的系统是有效的。对所提出的系统进行了多次编程和测试，以确保准确性和有效性。
{"title":"Modelling and Simulation of Smart Traffic Light System for Emergency Vehicle using Image Processing Techniques","authors":"Sujin Jose Arul, Mithilesh B S, S. L, Sufiyan, Gopal Kaliyaperumal, Jayasheel Kumar K A","doi":"10.1109/ICIPTM57143.2023.10117651","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117651","url":null,"abstract":"Saving time is very essential for humans. Every day people are spending some time at the traffic signal due to the drawbacks of the conventional traffic light system. In the existing traffic light system, a defined timer system is used and it is working based on preset timing. Due to the preset timing, there is no flexibility of ON/OFF in the signal light based on the emergency vehicle and congestion of the vehicle. Sometimes emergency vehicle like an ambulance needs to wait at a traffic signal for a long time and this would lead to a risk to a patient's life. Traffic police must personally identify an ambulance and release the congestion, but this is not possible as there are an enormous number of vehicles present these days. This project aims to providea solution for the issue in the conventional system. The model was designed using an image processing system that reads the image and determines the presence of an emergency vehicle and the density of vehicles in each lane the ON/OFF signal for the particular lane will be given to the traffic light system which helpsto reduce the unnecessary waiting time of vehicles. The system calculates the vehicle's density and to detect the emergency vehicle using image processing to provide the green light signal tothe lane. This project used Open CV and Yolo (you only look once)algorithm in the image processing method to develop the system. The simulation has been done on the proposed smart traffic systemand it identifies that the proposed system is efficient. Multiple times of programming and testing have been done on the proposedsystem to ensure accuracy and for validation.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123645703","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118252
Preethi Samantha Bennet, Deepthi Tabitha Bennet, Anitha D
In mission critical systems, system failure is a major hazard and may cause huge losses including loss or threat to lives. Organisations, industries, hospitals and companies can benefit hugely if an accurate prediction of the impending failure can be made, with enough time to initiate appropriate maintenance routines. Here, we propose and demonstrate that machine failure prediction can be done using suitable machine learning models with high accuracy. We apply the principles of Logistic Regression, Bootstrap Aggregation and Multinomial Logistic Regression to a predictive maintenance dataset of 10,000 data points to predict machine failure under five independent failure modes. Applying ensemble methods like bootstrap aggregation push the accuracy to greater than 99% The machine fails even if one failure mode is true. We are able to predict the possible cause of failure too, with a high accuracy of up to 99%.
{"title":"Intelligent Machine-Failure Prediction System (IMPS)","authors":"Preethi Samantha Bennet, Deepthi Tabitha Bennet, Anitha D","doi":"10.1109/ICIPTM57143.2023.10118252","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118252","url":null,"abstract":"In mission critical systems, system failure is a major hazard and may cause huge losses including loss or threat to lives. Organisations, industries, hospitals and companies can benefit hugely if an accurate prediction of the impending failure can be made, with enough time to initiate appropriate maintenance routines. Here, we propose and demonstrate that machine failure prediction can be done using suitable machine learning models with high accuracy. We apply the principles of Logistic Regression, Bootstrap Aggregation and Multinomial Logistic Regression to a predictive maintenance dataset of 10,000 data points to predict machine failure under five independent failure modes. Applying ensemble methods like bootstrap aggregation push the accuracy to greater than 99% The machine fails even if one failure mode is true. We are able to predict the possible cause of failure too, with a high accuracy of up to 99%.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130150152","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117615
S. K, K. A, S. R, A. Malini
In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet Computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.
{"title":"Deep Learning Approaches for Pneumonia Classification in Healthcare","authors":"S. K, K. A, S. R, A. Malini","doi":"10.1109/ICIPTM57143.2023.10117615","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117615","url":null,"abstract":"In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet Computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023057","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10117894
S. M. R. Kumar, Thayyaba Khatoon Mohammed, S. Rao, Dinesh Anton Raja P, Ruhi Bakhare, Ashok Kumar, Swagata B. Sarkar
Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.
{"title":"IoT Based Fish Pond Monitoring System to Enhance Its Productivity","authors":"S. M. R. Kumar, Thayyaba Khatoon Mohammed, S. Rao, Dinesh Anton Raja P, Ruhi Bakhare, Ashok Kumar, Swagata B. Sarkar","doi":"10.1109/ICIPTM57143.2023.10117894","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117894","url":null,"abstract":"Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289431","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 : 2023-02-22DOI: 10.1109/ICIPTM57143.2023.10118264
Mogalraj Kushal Dath, Nahida Nazir
This research seeks to investigate the possibility of using deep learning strategies in the process of diagnosing malaria, a virus that affects billions of people all over the world. Standard lab tests for malaria require the services of a qualified laboratory technician as well as an in-depth analysis of blood samples. This process can be expensive, time-consuming, and prone to errors caused by humans. This work attempts to enhance the accuracy of malaria diagnosis while also increasing the rate at which it can be performed by utilizing the capabilities of deep learning. We evaluate the performance of various methods for identifying the Plasmodium parasite in thin blood smear images by using deep learning models such as CNN, ResNet50, and VGG19 in accordance with noise reduction techniques and image segmentation methods. This allows us to compare the accuracy of the various methods. According to the findings of our research, the VGG19 model had the greatest overall performance. It had an accuracy of 0.9286 as well as a low false-positive and losing rate. The model is also tiny, making it easy to transport and use in a variety of contexts due to its portability. This study gives an overview of the current advancements in deep learning for malaria diagnosis. It also illustrates the potential for AI to increase both the accuracy and speed of malaria diagnosis.
{"title":"Diagnosing malaria with AI and image processing","authors":"Mogalraj Kushal Dath, Nahida Nazir","doi":"10.1109/ICIPTM57143.2023.10118264","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10118264","url":null,"abstract":"This research seeks to investigate the possibility of using deep learning strategies in the process of diagnosing malaria, a virus that affects billions of people all over the world. Standard lab tests for malaria require the services of a qualified laboratory technician as well as an in-depth analysis of blood samples. This process can be expensive, time-consuming, and prone to errors caused by humans. This work attempts to enhance the accuracy of malaria diagnosis while also increasing the rate at which it can be performed by utilizing the capabilities of deep learning. We evaluate the performance of various methods for identifying the Plasmodium parasite in thin blood smear images by using deep learning models such as CNN, ResNet50, and VGG19 in accordance with noise reduction techniques and image segmentation methods. This allows us to compare the accuracy of the various methods. According to the findings of our research, the VGG19 model had the greatest overall performance. It had an accuracy of 0.9286 as well as a low false-positive and losing rate. The model is also tiny, making it easy to transport and use in a variety of contexts due to its portability. This study gives an overview of the current advancements in deep learning for malaria diagnosis. It also illustrates the potential for AI to increase both the accuracy and speed of malaria diagnosis.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116907372","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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