2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)最新文献_第4页

Versatile Surveillance Bot: for Remote Monitoring in Hazardous Places 多功能监控机器人:用于危险场所的远程监控

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011049

C. Amali, R. Rajesh, J. S. Abrameyan, G. Raiasekar, N. M. Muhaseen

引用次数: 0

An IoT Sensor Network System- Based Precision Irrigation System Deploying with Collaborative Sensor Techniques for the Optimumlow - Cost Yield 基于物联网传感器网络系统的精确灌溉系统，采用协同传感器技术实现最佳低成本产量

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010743

Anver N Hussain, S. Saradha

引用次数: 0

Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets 用于医学图像数据集分类的集成预训练深度卷积神经网络模型

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011089

K. S, H. Inbarani

{"title":"Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets","authors":"K. S, H. Inbarani","doi":"10.1109/ICAISS55157.2022.10011089","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011089","url":null,"abstract":"Over the last few years, Deep Learning models have shown prominent results in medical image analysis especially to predict disease at the earlier stages. Since Deep Neural Network require more training data for better prediction, it needs more computational time for training. Transfer learning is a technique which uses the learned knowledge to perform the classification task by minimizing the number of training data and training time. To increase the accuracy of a single classifier, ensemble learning is used as a meta-learner. This research work implements a framework Ensemble Pre-Trained Deep Convolutional Neural Network using Resnet50, InceptionV3 and VGG19 pre-trained Convolutional Neural Network models with modified top layers to classify the disease present in the medical image datasets such as Covid X-Rays, Covid CT scans and Brain MRI with less computational time. Further, these models are combined using stacking and bagging ensemble approach to increase the accuracy of single classifier. The datasets are distributed as train, test and validation data and the models are trained and tested for four epochs. All the models are evaluated using validation data and the result shows that the ensemble learning approach increases the prediction accuracy when compared to the single models for all the datasets. In addition, this experiment reveals that the stacked model attains higher test accuracy of 99% for chest X-Ray images, 100% for chest CT scan images and 98% for brain MRI, compared to the bagged models.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129488763","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

Nuclei Localization in Pap Smear Images for Ovarian Cancer Visualization 卵巢癌子宫颈抹片影像的细胞核定位

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010722

J. Jeyshri, M. Kowsigan

{"title":"Nuclei Localization in Pap Smear Images for Ovarian Cancer Visualization","authors":"J. Jeyshri, M. Kowsigan","doi":"10.1109/ICAISS55157.2022.10010722","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010722","url":null,"abstract":"According to healthcare data, cervical cancer is currently the second most frequent disease among women globally. Regular pap image analysis might be used to treat ovarian cancer. The test is for examining the pre-cancerous alterations in the epithelial tissue so effective screening may lower the number of fatalities brought on by the condition basis. The examination of a Pap smear sample is a laborious and time-consuming procedure that is done visually by a cytopathologic. This may sometimes make it difficult to notice with one's eyes. In normal cells, the size of the nucleus is proportionately lower than in defective cells, which have larger nuclei. The defective nucleus is larger, and sometimes the size cannot be determined precisely by sight alone when dividing cervical cancer into phases. This is due to the fact that each physician has a unique viewpoint on how to classify the various stages of cancer by looking at the nucleus without precise dimensionality reduction in the classifier's accuracy. However, the majority of nations lack reliable screening methods for this form of cancer. In this work, we used some learning model to classify normal and cancerous cervical cells as well as their types. We then compare how well these models work. A technique to recognize and categorize the smear cell pictures for the detection of cancer was recently put forward by several researchers. The accuracy of the segmented picture for research analysis may also be increased with a considerable cut-off level according to our upgraded method.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128564873","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

Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing 医学图像处理中基于深度学习的脑肿瘤识别与分类研究

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010954

S. Karpakam, N. Senthilkumar, R. Kishorekumar, U. Ramani, P. Malini, S. Irfanbasha

引用次数: 2

Optimized Algorithm for Lowering the Computation Time and Memory Utilization for Grading of Brain Cancers 降低脑癌分级计算时间和内存利用率的优化算法

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010790

Deepak V.K, S. R

{"title":"Optimized Algorithm for Lowering the Computation Time and Memory Utilization for Grading of Brain Cancers","authors":"Deepak V.K, S. R","doi":"10.1109/ICAISS55157.2022.10010790","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010790","url":null,"abstract":"Clinical MRI scanning serves an essential part in the diagnostic procedure of several severe disorders including brain cancers and the following medication procedures of a patient. Because the brain is a fragile, intricate, and crucial part of the human body, it is one of the most common causes of death among cancer patients. However, a good and prompt treatment may save lives to a certain degree. Hence, in this publication, an effective brain tumor identification framework is suggested using a Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP), and Gray Wolf Optimization with Adaptive Clustering with Super pixel Segmentation (GWO_ACSP) and are mainly tested on CANCER IMAGE ACHRCHIEVE (CIA) which is a database containing High Grade and Low-Grade astrocytoma tumor images and also with BRATS 2015. The evaluation matrices were computed in which the proposed Gray Wolf Optimization-based ACSP (GWO_ACSP) gives a better answer for brain tumor segmentation with an accuracy of 0.99% than other models like RG, PFCM, SLPSO, MRG. The computational time is reduced to 80% and program memory utilization of about 300% is actually used in the proposed algorithms which shows a remarkable lower value compared to other prominent methods:","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129044455","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

Design of an Artificial Vision System to Detect and Control the Presence of Black Vultures at Airfields 机场黑秃鹫的人工视觉检测与控制系统设计

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010861

Hernando González, Alhm Vera, D. Valle

引用次数: 0

Optimized Design and Application of GaN Based Power Amplifier for C-Ku Band of AESA RADAR 基于GaN的有源相控阵雷达C-Ku波段功率放大器优化设计与应用

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011118

Sapana S. Shirsat, P.Yudhistar Sai, A. Raj, G.R. Shinde, U. Sateesh

{"title":"Optimized Design and Application of GaN Based Power Amplifier for C-Ku Band of AESA RADAR","authors":"Sapana S. Shirsat, P.Yudhistar Sai, A. Raj, G.R. Shinde, U. Sateesh","doi":"10.1109/ICAISS55157.2022.10011118","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011118","url":null,"abstract":"This paper presents the optimized design and application of a C-Ku band for GaN (Gallium Nitrate) based PA (Power Amplifier). PA and LNA are most important sub components of Transmitting and Receiving Module (TRM) for Active Electronically Scan Array (AESA) RADAR. As AESA has n number of patch antenna elements, multiple fabrication techniques, so it is mandatory to explained the updated technologies of design and up-gradation according to new era. According to taken references, can say that, the Self Oscillation (SO) is a effect which cause due to the closed feedback loop in TRM. In the Rx end of the TRM have more losses, to overcome this losses and to achieve greater efficiency of power amplifier, GaN based technology has explained. AESA has the great efficiency of the scanning and seeking as automation of the steering is given and commanded by the chip called FPGA. The reduction of fabrication error according to the theoretical values and practical testing results has updated by the 6 bit phase shiftier, 6 bit attenuator, these working process namely called Collimation. The performance of the 50 W output power, GaN technology based power amplifier with more than 35dBm gain has Power added Efficiency (PAE) is greater than 30 %. The average Fabricated Monolithic Micro Integrated Circuit (MMIC) size is 15 * 15 * 3 mm3 used for Air-born applications. Designing with thermal effect also plays a major role and effects on the components has explained in this paper. The explained GaN technology-based operate up to 180°C with 1500 W/cm2 and thermal packing design will help to increase the gain of PA by 33%.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992936","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 Review on Smart Farming based on Soil and Environmental Factors with Deep Learning Techniques 基于土壤与环境因子的深度学习智能农业研究进展

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010825

Routhu Sathish, Thulluru Prem Chand, Sai Ram Prudhvi Gummaluri, Marripudi Vyas Kanmani, T. Daniya

{"title":"A Review on Smart Farming based on Soil and Environmental Factors with Deep Learning Techniques","authors":"Routhu Sathish, Thulluru Prem Chand, Sai Ram Prudhvi Gummaluri, Marripudi Vyas Kanmani, T. Daniya","doi":"10.1109/ICAISS55157.2022.10010825","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010825","url":null,"abstract":"India's global economy is critically dependent on agriculture, which also accounts for a sizable portion of GDP. This paper provides a review of the use of machine learning and deep learning techniques in agriculture and elaborates different systems developed for smart agriculture including crop recommendations, fertilizer suggestions, categorization procedures and diseases identification by images. Climate factors like temperature, rainfall, soil quality, and fertilizers are the key determinants of agricultural productivity. Decision making such as determining the diseases and applying solutions to them is an important step in agricultural activities. With advanced technologies evolved in this digital world we can build more sustainable systems to make these farming procedures automated. Deep learning techniques are giving very prominent results in solving problems of predictions and decision making. The application of machine learning algorithms that are used in solving different problems that are arising in farming process are discussed. This paper contributes to know the available techniques for the automated farming procedures.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131285667","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 Information System based on Random Sampling Data Clustering Marketing 基于随机抽样数据聚类营销的信息系统优化

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011013

Zixia Xu

引用次数: 0