2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)最新文献

{"title":"2022 ICETEMS Review Page","authors":"","doi":"10.1109/icetems56252.2022.10093605","DOIUrl":"https://doi.org/10.1109/icetems56252.2022.10093605","url":null,"abstract":"","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638632","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}

{"title":"Improved Current Source Inverter with Lesser Input Inductor for PV-Grid Interface","authors":"Esther Jennifer Isaac, M. Rajeev","doi":"10.1109/ICETEMS56252.2022.10093272","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093272","url":null,"abstract":"Current Source Inverter for Photovoltaic-Grid interface is not much researched at the distribution level, though it is advantageous in many aspects. This is mainly because of the necessity of high value of input inductor and poor dynamic response as related to Voltage Source Inverter. In this paper, a modified Current Source Inverter that requires lesser value of input side inductor as compared to the conventional CSI is presented. A capacitor in series with a controllable switch is added such that the capacitor can supply the 100Hz ripple resulting in a great decrease in the value of input inductor. Design of passive elements of the improved CSI and the methodology used in the control are discussed in detail in the paper. Simulation results that validate the design are presented for conventional as well as for the improved CSI.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131605485","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}

{"title":"Empirical Analysis of Predictive Models For Insurance Claim Classification","authors":"Ankit Harale, Yogita K. Dubey, Vikas Gupta, Abhijeet Motghare, M. Chakole, Aniket G Pathade","doi":"10.1109/ICETEMS56252.2022.10093335","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093335","url":null,"abstract":"In this paper, a framework is proposed to assist in predicting the health insurance claim. Several theories have demonstrated that health insurance claim prediction may be used to estimate the expenses using machine learning algorithms, which a company must incorporate in the early financial budget. Six machine learning models are suggested and analyzed to create the ideal model that will properly forecast claims and lower the cost to the organization.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130770321","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}

{"title":"Classification of Power Quality Disturbances using the Unique Combination of Hilbert Transform, Image Processing and K-Nearest Neighbor","authors":"R. Kankale, S. Paraskar, S. Jadhao","doi":"10.1109/ICETEMS56252.2022.10093403","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093403","url":null,"abstract":"This paper introduces the unique combination of Hilbert Transform (HT), Image Processing, and K-Nearest Neighbor (KNN) for classifying the Power Quality Disturbances (PQDs). Power Quality (PQ) is a term that is frequently used these days. Everyone is cautious of the power supply they are purchasing from the utility because the end-user sensitive equipment may malfunction or trip as a result ofPQDs. In order to get a clean and disturbance free power supply, the utility needs to identify the type of disturbance, the cause of the disturbance, and mitigate it. This paper presents a novel approach for classifying the commonly occurring PQDs like sag, swell, and interruption. The proposed algorithm is realized by generating voltage signals pertaining to the PQDs using integral mathematical models, Simulink models, and experimentation. The voltage signals related to different PQDs are processed using HT and the processed signals having elliptical shapes are plotted and converted into images. These images are further processed using the image processing technique in order to turn the RGB image into a grayscale image. The statistical parameters namely mean and standard deviation are calculated from the grayscale image input to the algorithm for feature extraction. The KNN classifier is trained and tested using these extracted features. In the KNN classifier, the minimum Euclidean distance is calculated to identify the class of PQDs with high accuracy.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130939395","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}

{"title":"Detection of Oral Cancer Using the Fluorescence Spectroscopy and Classification of Different Stages of Cancer by Multivariate Analysis","authors":"Pavan Kumar","doi":"10.1109/ICETEMS56252.2022.10093684","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093684","url":null,"abstract":"Fluorescence spectroscopy is used as a diagnostic tool for the detection of oral cancer in the present study. For the fluorescence measurement, an excitation wavelength of 350 nm is used. Two diagnostic media, namely human tissue and saliva are incorporated in this study. Measurements are accomplished on oral squamous cell carcinoma (OSCC), dysplastic, and normal tissue and saliva samples. Fluorescence spectra obtained from human oral tissue consists of major bands of collagen and NADH near 390 and 445 nm. However, saliva shows only one major band of NADH near 440 nm. Multivariate analysis has been employed on the fluorescence data of human oral tissue and saliva for the classification of different stages of cancer. In the multivariate analysis, principal component analysis (PCA), Mahalanobis distance (MD) model, and receiver operating characteristic (ROC) analysis are utilized. Fluorescence spectroscopy on human oral tissue and saliva is competent to differentiate OSCC to normal, dysplasia to normal and OSCC to dysplasia with overall accuracies of 90%, 83%, 80% and 100%, 86%, 85% respectively. Obtained results using the fluorescence spectroscopy on human saliva are comparable to human tissue samples. Results imply that we may make use of saliva as a non-invasive diagnostic medium for the detection of oral cancer and multivariate analysis can be employed as a classification tool.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115054304","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}

{"title":"Deep Transfer Learning For Improving Alzheimer Disease Diagnosis","authors":"D. Nakul Pranao, M. Harish, C. Dinesh, S. Sasikala, S. Arun Kumar","doi":"10.1109/ICETEMS56252.2022.10093611","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093611","url":null,"abstract":"Alzheimer disease (AD) is a neurological disorder which shrinks the brain and causes dementia. In the past, this disease was more prevalent in American countries. However, it is now common in other countries as well. When compared to youth, older people are more affected by this disease. The number of people affected by this disease is gradually increasing each year, and according to one study, this number may reach around 15 million in the near future. People who are affected will experience symptoms such as memory loss and confusion. Early detection of Alzheimer disease is essential for providing appropriate treatments. Neuroimaging based Machine Learning methods are commonly utilized for the detection and diagnosis of Alzheimer’s, but they are time-consuming. The time consumption can be reduced, and the detection accuracy can be increased further with the help of Deep Learning and Transfer Learning algorithms. This proposed work compares 4 different Transfer Learning Models. VGG-16 has the highest accuracy of 97.2 percent out of the four models tested.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115280133","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}

{"title":"Analysis of prognosticate Omicron Using SVM & LASSO","authors":"Shubhangi Dc, Basavaraj Gadgay, Syeda Faiza Fatima, M. A. Waheed","doi":"10.1109/ICETEMS56252.2022.10093575","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093575","url":null,"abstract":"Although ML forecasting algorithms frequently use techniques that involve more complex features and predictive methods, their goal is the same as traditional methods: to improve forecast accuracy while minimizing the loss function. To cope with forecasting challenges, a number of prediction approaches are routinely utilized. This study demonstrates how machine learning algorithm could predict how many individuals got infested by Omicron, virus which is presently being taken as possible risk to humanity. Four common forecasting prototypes were used to predict the harmful components of omicron: linear regression (LR), SVM, LASSO & ES. Using these algorithms, system calculates amount of recently infested people, death count, & recovered patient count. In terms of predicting new confirmed cases, mortality rates, and rates of recovery, ES is efficiently accompanied by LASSO, LR, and SVM models. In addition, the system uses symptoms to detect and diagnose Omicron disease.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123650554","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}

{"title":"Performance Analysis of Planar Transformer for DC-DC Converter with Ansys Maxwell","authors":"Chetas B. Gaikwad, V. Rajguru, S. Adhau","doi":"10.1109/ICETEMS56252.2022.10093339","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093339","url":null,"abstract":"Recent developments in the field of power electronics are majorly focusing on innovating power converters with high power density and high operating frequency. These developments have revealed number of drawbacks in the use of conventional wire-wound machines especially transformers. Planar transformer provides many enhanced features compared to conventional transformer such as high efficiency and compact size. Therefore, planar transformers (PT) are ideally suited in electric vehicle application such as battery management systems and military systems. But designing planar transformers within desired size limit with accurate analysis has always been a challenge. Hence a new methodology is required. Ansys Maxwell (Maxwell 2D and 3D) have shown their unmatchable ability of electromagnetic analysis with good accuracy in planar transformer design. Hence, this paper aims at FEM based simulation of planar transformer for DC-DC Forward Converter in Ansys Maxwell. Here, initially 2D Model of Planar transformer is designed in Ansys PExprt. This model is imported in Ansys Maxwell and its performance is analyzed. While designing planar transformer, the most important factors for planar transformer (PT) design which includes winding loss, core loss, leakage inductance has been individually studied. And overall efficiency of planar transformer is 98.82 %. Issues arising and typical phenomena encountered are also discussed in detail.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116286882","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}

{"title":"Internet of Things Communication protocols optimization using Blockchain Technology integrated with Reinforcement Learning","authors":"M. Kumari, Dr. Mahendra Gaikwad, Dr. Salim A Chavhan","doi":"10.1109/ICETEMS56252.2022.10093387","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093387","url":null,"abstract":"Under the IoT vision, conventional items become sophisticated and self-contained. This ideal has become an actuality due to its technological breakthroughs, although there are still challenges to face. Especially in the field of security, like data accuracy. All academia and commerce are curious about the combined study of block chain and computational modelling (ML) because it may offer significant advantages for achieving decentralized, safe, intelligent, and advanced information management operations and administration. Considering the expected IoT will have to establish relationships in this massive incoming data stream as it evolves in the coming years Data sharing will change as a result of the crucial software known as the blockchain. A scientific innovation that can transform several industries, such as the Internet of Things, is the capacity to make connections within dispersed systems without the need for power. As a result, the controller operates well and may be modified to fit into different, dynamic contexts. Additionally, since greater-level systems are taught with deep reinforcement learning, the machine can expect to study even after it is put into use, which makes it perfect for practical uses. Based on their personal experience with the employment of an agent, learning through Reinforcement: S (State), A (Action), and R (Reaction) are the parameters (Reward Under the IoT vision, ordinary items become clever and self-contained. This paper investigates this relationship, analyses issues with blockchain and IoT systems, and assesses the most pertinent studies in order to establish how blockchain with reinforcement learning may enhance IoT. Depending on the finding, the study then suggests using supervised learning techniques to address some of the major problems faced by blockchain-enabled IIoT systems, such as block time reduction and operations throughput development. There will be a thorough case study that demonstrates how a Q-learning strategy can be used to minimize latency problems for a miner and hence lower the likelihood of forking events.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607396","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}

{"title":"Efficient Deep Learning Algorithm for Diagnosing the Flora Diseases","authors":"R. Ganesh, S. Sivakumar, Gurukirubhakara T, Hariharan Gts, H. S","doi":"10.1109/ICETEMS56252.2022.10093373","DOIUrl":"https://doi.org/10.1109/ICETEMS56252.2022.10093373","url":null,"abstract":"The human race’s entire existence depends on agriculture. A relatively big portion of the people can find work in agriculture in addition to receiving food and raw materials. We are all aware that India’s economy depends heavily on agriculture, which is currently one of the world’s top two agricultural producers. 43 percent of the Indian workforce is employed there, and it produces around 16.5 percent of India’s GDP. This enables us to address the fact that India’s economy is expanding annually as a result of a rise in agricultural productivity. How effectively crops are free from numerous pests and diseases determine, in large part, how successful agriculture production and its economics are. The farmers are being severely impacted by the decrease in yield. Additionally, the nutritional value of the plant’s edible components is too diminished with decreased production. Making short-term modifications to daily agricultural activities that reduce losses brought on by unfavourable conditions and improve yield and quality of agricultural productions is substantially aided by early disease forecasts in the short and medium run. There are now many different misunderstandings regarding plant disease detection. Therefore, in this work, disease diagnosis using leaves is made simple and user-friendly. With this approach, we have suggested an automated method to identify the illness and offer a suitable treatment for it via an application.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124415098","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}