2022 IEEE World Conference on Applied Intelligence and Computing (AIC)最新文献_第3页

Image Retrieval Framework for Anomalies Detection in X-Ray Radiographical Images 用于x射线图像异常检测的图像检索框架

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848987

Shaheen Fatima

引用次数: 0

IoT and WSN integration for Data Acquisition and Supervisory Control 用于数据采集和监控的物联网和WSN集成

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848933

Sanjeev Kumar Shah, K. Joshi, Sandeep Khantwal, Y. Bisht, H. Chander, Ashulekha Gupta

引用次数: 12

Stack Type Detection Using Few-Shot Learning 使用Few-Shot学习的堆栈类型检测

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848884

Henry Lin, K. George

{"title":"Stack Type Detection Using Few-Shot Learning","authors":"Henry Lin, K. George","doi":"10.1109/AIC55036.2022.9848884","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848884","url":null,"abstract":"Wireless digital communication has become so saturated that it is harder for radar receivers to distinguish noise from desired signals, essential for tracking applications like air traffic control towers, defense systems, and communication towers. This is where signal detection is a vital capability of radar systems. Signal detection is the ability to detect signals from noise, and often these signals will be interleaved with noise and other signals. Noise can be alleviated by using filtering techniques, windowing, and transforms, which then can be used by a deinterleaving algorithm to isolate signals. Standard deinterleaving methods isolate signals using deterministic methods; however, more state-of-the-art methods may approach these problems using machine learning or artificial intelligence. Often these methods require copious amounts of data, which can vary from a few hundred to thousands. This might not always be possible in certain situations where privacy limits the amount of available data. This is where Few-Shot Learning (FSL) is utilized for training models on small datasets. This paper proposes a system that can generate interleaved signals and deinterleave them with the help of an FSL model. Various FSL models will be used to compare and determine the optimal configuration of the proposed system.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547572","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

Optimization of Dynamic Economic Dispatch Problem for Micro Grid with incorporation of Wind Energy by Using Seagull Optimization 引入风能的微电网动态经济调度问题海鸥优化

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848893

Jagannath Paramguru, S. K. Barik, A. K. Sahoo, Tanmoy Parida

引用次数: 0

Critical Success Factors in Combating Dengue and COVID-19: Lessons for Future Pandemics 抗击登革热和COVID-19的关键成功因素:给未来大流行的教训

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848938

F. Xavier, G. Barbosa, Cristiano Corrêa de Azevedo Marques, A. Saraiva

引用次数: 0

Investigating the Charity Funding System using Blockchain Technology 利用区块链技术研究慈善资助系统

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848986

Ajendra Saxena, Dileep Kumar, Bhanu Pratap Singh, Bhairu Lal Jatt, J. Kumar

引用次数: 3

Performance Analysis of March M & B Algorithms for Memory Built-In Self-Test (BIST) 基于内存内置自检(BIST)的March M & B算法性能分析

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848869

Khushi, Kuldeep Singh

{"title":"Performance Analysis of March M & B Algorithms for Memory Built-In Self-Test (BIST)","authors":"Khushi, Kuldeep Singh","doi":"10.1109/AIC55036.2022.9848869","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848869","url":null,"abstract":"It has been acknowledged that for the chips designed at the deep sub-micron (DSM) level, the on-chip memory part is covering the maximum area of the integrated circuits. It is recommended to provide the testing mechanism in the IC design at higher abstraction levels to optimize the time, effort, and money. Therefore, memory testing is an essential characteristic of the chip design and strategy. The memory test model comprises a memory test algorithm for a build in self-test controller. The BIST controller utilizes the various functional blocks to test the memory by marching through in a specific order of sequential test elements. This paper represents the comparative performance analysis of March-B and March-M memory test algorithms with help of a memory BIST controller. The march-based testing detects memory structural faults at functional levels. In this case study, a testing architecture for a memory size of8-bit data with a depth of28. The designed model makes use of the March-M algorithm and provides the fault coverage for the functional fault models such as stuck-at fault, transition fault, and coupling fault using different logical operations performed between March-M elements. However, in the case of the March-B algorithm, it provides coverage of stuck-at transition fault only. It is observed that extra sequences of elements ate required for coverage of coupling fault. Thus, it is suggested to use the March-M test algorithm for memory tests to detect the maximum fault and achieve high fault coverage. This architecture is described in VHDL hardware description language (HDL) and simulated using Xilinx Vivado tool [2018}.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117267262","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

Real Time Protection of Farmlands from Animal Intrusion 实时保护农田免受动物入侵

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848808

R. Sumathi, P. Raveena, P. Rakshana, P. Nigila, P. Mahalakshmi

{"title":"Real Time Protection of Farmlands from Animal Intrusion","authors":"R. Sumathi, P. Raveena, P. Rakshana, P. Nigila, P. Mahalakshmi","doi":"10.1109/AIC55036.2022.9848808","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848808","url":null,"abstract":"Crop Vandalization due to animals is becoming area of concern nowadays. When an animal enters the land, farmers lose their crops, properties, livestock. It is eroding the time and efforts of farmers. They also get affected economically due to loss of crops. Conflicts between humans and animals keep putting lives in danger. Methods like electrocution causes intense pain to animals, sometimes leading to their death. An effective system for preventing animal intrusion is more and more necessary. Regarding to this problem we implement a system to provide a real time visibility of farmlands which is perfect and adaptive. Surveillance of farmlands is carried out and when animals are encountered, they are categorized using YOLO algorithm and corrective actions are made depending on the type of intruder present. Finally, farmers and forest officials are supplied with geo-locations and images of intrude. If the presence of animals is discovered after few seconds, strong repellents are used as a backup. As a result, the proposed technology successfully drives away animals without killing them and reduce human animal conflict as it does not require human participation.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125185841","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

Detecting Stress Level of Students Using Brain Waves Reducing It Using Yoga Therapy 用脑电波检测学生压力水平用瑜伽疗法减少压力

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848919

R. Anuradha R, G. Rathi, Manjula Krishnappa, M.S. Suresh Kumar, M. Kalpana

引用次数: 1

Liver Disease Diagnosis Using Machine Learning 使用机器学习进行肝脏疾病诊断

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848916

Manas Minnoor, V. Baths

{"title":"Liver Disease Diagnosis Using Machine Learning","authors":"Manas Minnoor, V. Baths","doi":"10.1109/AIC55036.2022.9848916","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848916","url":null,"abstract":"This paper evaluates the performance of various supervised machine learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Extra Trees, LightGBM as well as a Multilayer Perceptron (MLP) neural network in the detection and diagnosis of liver disease. Existing methods for diagnosis tend to be highly invasive and time-consuming. A lack of qualified experts exacerbates these issues. Since blood tests, known as liver function tests, are a standard method to assess liver health, these models utilize blood enzyme levels like Bilirubin, Albumin, Alanine transaminase (SGPT), and Aspartate Aminotransferase (SGOT) to diagnose liver disease in patients. A total of 11 attributes are used to train the models. The algorithms are compared using metrics including, but not limited to, F1 score, accuracy, and precision. The Extra Trees classifier is shown to provide the highest accuracy of 0.89 as well as an F1 score of 0.88. Thus, it appears to be the best method for the timely and accurate diagnosis of liver disease using blood enzyme levels. In addition, the usage of machine learning algorithms alongside human medical expertise may help drastically reduce errors in clinical diagnosis. This paper establishes the feasibility of applying machine learning in various medical fields including the diagnosis of other diseases.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004552","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