2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)最新文献_第10页

Application of Data Science and Artificial Intelligence in Financial decisions: A survey 数据科学与人工智能在财务决策中的应用综述

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119241

R. Shukla, Vishal Vyas, Ankur Roy, Animesh Chaturvedi

引用次数: 0

Design and Implementation of an IoT-Enabled Remote Surveillance Rover for Versatile Applications 多用途物联网远程监控漫游者的设计与实现

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119249

Bandi Raja Babu, Pathan Mohammed Afreed Khan, S. Vishnu, K. Lova Raju

{"title":"Design and Implementation of an IoT-Enabled Remote Surveillance Rover for Versatile Applications","authors":"Bandi Raja Babu, Pathan Mohammed Afreed Khan, S. Vishnu, K. Lova Raju","doi":"10.1109/IATMSI56455.2022.10119249","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119249","url":null,"abstract":"This article presents the designing and implementation of an IoT-enabled remote surveillance rover for human-assisted applications. The proposed system consists of two modules, namely the Remote Sensing Monitoring Unit (RSMU) and Image Acquisition Module (IAM), for controlling the Rover and video streaming respectively. The Rover (RSMU) unit is controlled through a Graphical User Interface (GUI) developed on the Blynk platform. A dashboard is designed to monitor the temperature, humidity, and gas levels around the rover premises in addition to the live video streaming. The video streaming is done through the Image Acquisition Module (IAM). The live streaming can be transferred to multiple authorized users using the port forwarding methodology by using NGROK application. The proposed system is deployed in an open ground and validated the functionality by operating it for one hour. Moreover, the expected current consumption and the battery life expectancy of the developed system are estimated as 287 mA and 2.5 days, respectively.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128746530","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

Performance Comparison of Various Features for Human Face Recognition using Machine Learning 使用机器学习进行人脸识别的各种特征的性能比较

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119449

K. Swamy, A. Supraja, P. S. Vinuthna, D. L. Sindhura

{"title":"Performance Comparison of Various Features for Human Face Recognition using Machine Learning","authors":"K. Swamy, A. Supraja, P. S. Vinuthna, D. L. Sindhura","doi":"10.1109/IATMSI56455.2022.10119449","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119449","url":null,"abstract":"Facial features are one of the most vital biometrics and are used to identify an individual. Facial recognition is a technology having the capacity to distinguish a specific individual. This technology mainly concentrates on machine learning techniques to learn, acquire, store, and examine facial features to fit them with a database. In this project, the features are extracted in transform domain. Discrete Wavelet Transform (DWT) is applied on images. In transform domain, features like mean, energy, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), Gabor Filters are explored. Appropriate features are extracted from LL, LH, HL, and HH bands to build a machine learning model. Database is divided into training and testing. Model is built based on the 80% of images from the database. Model is tested with 20% images of test data. Three important machine learning algorithms are popular. These are Decision Tree (DT), Support Vector Machines and Naive Bayes (NB). NB is used for probability-based inferences. DT is simpler than SVM. Hence, Decision Tree-based Machine Learning is employed to recognize the faces. Accuracy is used to test the performance of various features.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126832995","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

Effective Recognition System of American Sign Language Alphabets using Machine Learning Classifiers, ANN and CNN 基于机器学习分类器、人工神经网络和CNN的美国手语字母有效识别系统

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119336

Diponkor Bala, Mohammad Alamgir Hossain, Mohammad Anwarul Islam, Mohammed Mynuddin, Md Shamim Hossain, M. Abdullah

{"title":"Effective Recognition System of American Sign Language Alphabets using Machine Learning Classifiers, ANN and CNN","authors":"Diponkor Bala, Mohammad Alamgir Hossain, Mohammad Anwarul Islam, Mohammed Mynuddin, Md Shamim Hossain, M. Abdullah","doi":"10.1109/IATMSI56455.2022.10119336","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119336","url":null,"abstract":"People who cannot talk are called audibly impaired, and they communicate with others through other means. The most popular method of communication is through sign language. American Sign Language (ASL) is the de-facto standard for sign languages taught globally. Automated sign language recognition tries to bridge the gap. Convolutional neural networks are the method of choice these days when it comes to the classification of multiclass images. To recognize ASL alphabets, we used a CNN, traditional machine learning classifiers, and an artificial neural network. The Sign Language MNIST dataset has a total of 34,627 image data, of which 27,455 and 7172 are training and test data respectively. Except for J and Z, the dataset comprises 24 alphabets. We used the training dataset to train our CNN, ANN, and other machine learning models. Then examined our proposed CNN model as well as other models including ANN on the test dataset to check how well they recognize ASL alphabets correctly. The traditional classifiers such as Linear Regression, Logistic Regression, Random Forest, SVM, and ANN were able to achieve an accuracy of 71.94%, 90.16%, 98.63%, 97.92%, 82.96% respectively whereas the proposed CNN model achieved 100 % of accuracy on the unseen test data.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114584436","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

Reconfigurable Antennas for Future Wireless Communication: An Analytical Review 面向未来无线通信的可重构天线:分析综述

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119351

Monika, A. Taneja, N. Saluja, Somesh Kumar

{"title":"Reconfigurable Antennas for Future Wireless Communication: An Analytical Review","authors":"Monika, A. Taneja, N. Saluja, Somesh Kumar","doi":"10.1109/IATMSI56455.2022.10119351","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119351","url":null,"abstract":"The increasing number of wireless devices, internet-of-things (IoT) nodes, sensor nodes and smart devices has put huge burden on the mobile data traffic. To support the large user base and to enable seamless flow of information between the communicating nodes, the future wireless systems are looking for highly efficient antennas. Reconfigurable antennas are the promising antenna technology which offers the flexibility to adapt to the changing propagation environment by reconfiguring the antenna design properties to obtain the desired outcomes. This paper gives a comprehensive overview of the design considerations of the reconfigurable antennas, their properties and classification. The different approaches and methods used for achieving antenna reconfigurability are discussed along with the current state-of-art comparison. In the end, the different performance parameters are evaluated for comparison with the detailed quantitative analysis featuring the radiation efficiency, operating frequency, gain and return loss. The applicability of these reconfigurable antennas in the future wireless communication scenarios is also provided.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741587","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

A Study of Solar Powerd Charge Controlled Station For Electric Vehicles 电动汽车太阳能充电控制站的研究

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119402

Padmaja Vaibhavi, A. Singhal, Aayush Shrivastava

引用次数: 0

U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review 基于U-Net的非接触式指纹语义分割技术述评

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119359

Puneet Nahar, Preeti Gupta, Harvinder Kaur

引用次数: 0

Vision-Based Skin Lesion Characterization Using GLCM and Haralick Features 基于视觉的皮肤病变GLCM和Haralick特征表征

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119457

Jyoti Madake, Atharva Shembade, Karan Shetty, S. Bhatlawande, S. Shilaskar

{"title":"Vision-Based Skin Lesion Characterization Using GLCM and Haralick Features","authors":"Jyoti Madake, Atharva Shembade, Karan Shetty, S. Bhatlawande, S. Shilaskar","doi":"10.1109/IATMSI56455.2022.10119457","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119457","url":null,"abstract":"Dermatology is a branch of medicine that deals with ailments of the skin of the human body. It is one of the most recognized branches and deals with problems ranging from minor cuts to cancer. The most prominent type of cancer is skin cancer, and early identification can help to avoid it from progressing to the next stage. The goal of this research is to make the process of detecting skin lesions faster and more efficient. Separating the lesions from the background skin is one of the important tasks done by this project as there is very less contrast between them. Skin lesions are divided into two groups: primary and secondary, and these are further divided into many subcategories. Some are benign and some can be malignant. The model thus developed will be easy to use and can detect and classify the lesions with 93.46 percent accuracy. The paper proposes extraction of GLCM matrix and computation of Haralick texture features including contrast, correlation, energy, dissimilarity, homogeneity, and entropy. The LGBM classifier is trained with these features with 93.6% accuracy. Our paper proposes experimental analysis using various feature extraction algorithms and classifiers.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114951568","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

Identification of Diseases for Tomato Leaves Using AlexNet 利用AlexNet对番茄叶片病害进行鉴定

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119326

Sarla Jangir, M. K. Jain, Palika Jajoo, Praveen Shukla

{"title":"Identification of Diseases for Tomato Leaves Using AlexNet","authors":"Sarla Jangir, M. K. Jain, Palika Jajoo, Praveen Shukla","doi":"10.1109/IATMSI56455.2022.10119326","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119326","url":null,"abstract":"Plants are the key source of human energy generation and have nutritional, therapeutic, and other benefits. Plant diseases cause a significant loss in crop productivity, and manually inspecting for plant diseases is a labor-intensive and ineffective approach. To overcome this problem automated plant disease detection systems have been developed using many approaches rely on machine learning and image processing to address the indicated issue. The ability of plant illnesses to alter the color and texture of leaves is exploited to build techniques for detecting plant diseases. In this discipline, deep learning models like VGG and ResNET are often applied. However, because they are primarily focused on disease classification on a specific crop or dataset, the majority of these models are not scalable. The purpose of this work is to present an enhanced approach for detecting leaf diseases. The suggested system is built with Alexnet and trained and tested on a variety of tomato leaf diseases. This model achieves 94.9% accuracy for classification and validation. In future this model is implemented by increasing number of diseased classes as well as other plant disease.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"513 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128904901","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

Dealing heavy IoT systems with hybrid cloud platform 使用混合云平台处理重型物联网系统

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Pub Date : 2022-12-21 DOI: 10.1109/IATMSI56455.2022.10119415

Prashant Vaish, N. Anand, Gaurav Sharma

{"title":"Dealing heavy IoT systems with hybrid cloud platform","authors":"Prashant Vaish, N. Anand, Gaurav Sharma","doi":"10.1109/IATMSI56455.2022.10119415","DOIUrl":"https://doi.org/10.1109/IATMSI56455.2022.10119415","url":null,"abstract":"In Today's world, whenever there is talk about Cloud computing, there is always an assumption of a scalable and secure platform that can provide stability at a low price. No matter which vendor one chooses, expectations are to manage the workload dynamically with high performance and efficient costing. Talking about large IoT systems, hosting them over a single cloud never solves the purpose of workload utilization and cost and thus exploits the computing over the cloud. Since IoT is covering almost every industry around the globe without any geographic limitations and even in space, it is often becoming very hard to cover the cost as well as manage resources with the right utilization in any framework or architecture. In this paper, we have built these gaps and challenges by proposing a Hybrid cloud model that can check the requirements of different IoT systems and lay down these functions over multiple clouds based on predefined policies. These requirements are nothing but business requirements from business holders about resource prices and concepts. The Hybrid cloud model fetches the state of these different clouds and assigns the requirements to the most appropriate cloud. As a result of this hybrid model, there is a major increase in stability and utilization with effective cost.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128341107","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