2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)最新文献_第9页

Revealing AI-Based Ed-Tech Tools Using Big Data 利用大数据揭示基于人工智能的教育技术工具

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10118162

Arman Raj, Vandana Sharma, S. Rani, B. Balusamy, Ankit Kumar Shanu, A. Alkhayyat

{"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}

引用次数: 0

A Numerical study of Thermal Management of single cylindrical LiFePO4 battery 单圆柱形LiFePO4电池热管理数值研究

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10118228

Maheep Dwivedi, G. Kumar, R. Singh

引用次数: 0

Effect of Artificial Intelligent on Empathy Quotient (EmQ) and Responsiveness of Customer Care Executive- A Study from Customer's Lenses 人工智能对客户服务主管共情商和响应性的影响——基于客户视角的研究

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10117729

Neetima Agarwal, Arpana Kumari

引用次数: 0

Technological Intervention System for Attention Deficit Hyperactivity Disorder 注意缺陷多动障碍的技术干预系统

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10117576

S. Mishra, Anurag Sharma, Vikas Khullar

{"title":"Technological Intervention System for Attention Deficit Hyperactivity Disorder","authors":"S. Mishra, Anurag Sharma, Vikas Khullar","doi":"10.1109/ICIPTM57143.2023.10117576","DOIUrl":"https://doi.org/10.1109/ICIPTM57143.2023.10117576","url":null,"abstract":"The paper will examine the use of technology in the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder that affects millions globally. It will first highlight the existing challenges in diagnosing and treating ADHD and how technology can help overcome these challenges. The paper will then focus on various technological interventions, such as digital therapeutics and mobile health apps, and discuss their potential benefits and drawbacks. Lastly, the paper will consider the impact of technology-based diagnosis and treatment on the healthcare system, patient autonomy, and overall effectiveness. The traditional diagnosis process for ADHD is often lengthy and unreliable, relying on a combination of tests, leading to a lack of accuracy and inefficiency. Technological solutions have the potential to improve the accuracy and timeliness of diagnosis and treatment. Digital therapeutics, for instance, can offer individuals with ADHD an efficient way to manage their symptoms, reduce costs, and improve outcomes, all from the convenience of their own devices.","PeriodicalId":178817,"journal":{"name":"2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"668 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":"122619388","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}

引用次数: 0

Growth of Cyber-crimes in Society 4.0 4.0社会中网络犯罪的增长

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10118185

Vinita Sharma, Tanu Manocha, Seema Garg, Saatwik Sharma, Anshita Garg, Ritu Sharma

引用次数: 6

Modelling and Simulation of Smart Traffic Light System for Emergency Vehicle using Image Processing Techniques 基于图像处理技术的应急车辆智能交通灯系统建模与仿真

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10117651

Sujin Jose Arul, Mithilesh B S, S. L, Sufiyan, Gopal Kaliyaperumal, Jayasheel Kumar K A

{"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}

引用次数: 1

Intelligent Machine-Failure Prediction System (IMPS) 智能机器故障预测系统(IMPS)

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10118252

Preethi Samantha Bennet, Deepthi Tabitha Bennet, Anitha D

引用次数: 0

Deep Learning Approaches for Pneumonia Classification in Healthcare 医疗保健领域肺炎分类的深度学习方法

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10117615

S. K, K. A, S. R, A. Malini

{"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}

引用次数: 0

IoT Based Fish Pond Monitoring System to Enhance Its Productivity 基于物联网的鱼塘监测系统，提高其生产力

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 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

{"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}

引用次数: 2

Diagnosing malaria with AI and image processing 用人工智能和图像处理诊断疟疾

2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2023-02-22 DOI: 10.1109/ICIPTM57143.2023.10118264

Mogalraj Kushal Dath, Nahida Nazir

{"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}

引用次数: 0