2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)最新文献_第9页

Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover 土地利用与土地覆盖洪水影响评价的LANDSAT数据图像处理与监督分类

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988164

M. Kalidhas, R. Sivakumar

{"title":"Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover","authors":"M. Kalidhas, R. Sivakumar","doi":"10.1109/ICTACS56270.2022.9988164","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988164","url":null,"abstract":"Floods are one of the most frequent natural disasters in the world, causing economic damage as well as human losses. In the present research Pre and Post flood Landsat satellite data was processed through image processing techniques. Landsat satellite image was initially correct the geometry correction and radiometric correction carried out. Similarly follow the pre-processing techniques on satellite image. The GIS platform used to find out the pre and post flood impact on land use and land cover changes for supervised classification techniques in Chengalpattu taluk, Tamil Nadu. The study Area which are classified into five classes in supervised classification for water, urban, forest, barren land and agriculture using GIS platform. Using high resolution Landsat 8 images, study area were categorised into five types in Level 1 classifications. Supervised classifications provide better result in the representation of classes for pre and post flood impact on land use land cover. The overall accuracy of image classification obtained in 2015 and 2016 is 94.25 % and 90.8%. Hence the result proves that satellite data has capability for analysing the changes in LULC through image classification techniques due to flood.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128065559","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

Key Frame Extraction Analysis Based on Optimized Convolution Neural Network (OCNN) using Intensity Feature Selection (IFS) 基于优化卷积神经网络(OCNN)的关键帧提取分析

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988474

T. Prabakaran, L. Kumar, S. Ashabharathi, S. Prabhavathi, Maneesh Vilas Deshpande, M. Fahlevi

{"title":"Key Frame Extraction Analysis Based on Optimized Convolution Neural Network (OCNN) using Intensity Feature Selection (IFS)","authors":"T. Prabakaran, L. Kumar, S. Ashabharathi, S. Prabhavathi, Maneesh Vilas Deshpande, M. Fahlevi","doi":"10.1109/ICTACS56270.2022.9988474","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988474","url":null,"abstract":"The multimedia is playing role of timing frames in videos. The representation frame shows the intention on video definition. The keyframes the important factor for extraction information from video frames. The non-related frames is a problem for finding new key exposure. In this paper, we present a new method for extracting essential frames from motion capture data using Optimized Convolution Neural Network (OCNN) and Intensity Feature Selection (IFS) for better visualisation and understanding of motion content. It first removes noise from motion capture data using the Butterworth filter, then reduces the size via principal component analysis (PCA). Finding the zero-crosses of velocity in the main components yields the initial set of crucial frames. To avoid redundancy, the first batch of important frames is divided into identical poses. Experiments are based on data access from frames in the motion capture database, and experimental results suggest that crucial frames retrieved by our method can improve motion capture visualisation and comprehension.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270666","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

Predicting Parkinson's Disease Using Different Features Based on Xgboost of Voice Data 基于语音数据Xgboost的不同特征预测帕金森病

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988089

Rahim Hassani, C. Manjunath

{"title":"Predicting Parkinson's Disease Using Different Features Based on Xgboost of Voice Data","authors":"Rahim Hassani, C. Manjunath","doi":"10.1109/ICTACS56270.2022.9988089","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988089","url":null,"abstract":"The purpose of this article is to determine Parkinson's disease (PD) is a neurotic condition characterized by the demise of nerve cells in the middle nervous system. The neurologic capacity is speech or voice recognition to predict if a body has been applied speech stored dataset of patients, to use an engine learning algorithm to analyze the sound patients to predict the PD patients that affect approximately 92 percent of patients, a voice problem issue, to work on dataset decision tree to predict the PD with maximum exactitude. It was the better pattern to utilize on the data with an accuracy of 90–96 percent (PD), a format speaks signals, to offer a better outcome from our patients who modify the old age system above the time of 66 years and it develops at a superior price till 2060. Several contemporary engine learning and pattern recognition techniques were used in this study to categorize or predict the risk of Parkinson's disease based on speech signal data. A number of classification approaches, including as K-NN, Decision Trees, and Neural Networks, are presented in this project, as well as some “Ensemble” Gradient boosting, which is an engine that learns reflux and grouping difficulty knowledge. This results in an ensemble of incapable divination patterns as a divination pattern. Within coming period, combining voice messages and some other medical information, our system will help clinicians in more accurately and swiftly identifying the PD subgroup from of the normal participants.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115642912","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

Efficient Plant Disease Prediction based on Convolutional Neural Network using Optimized Proposed Logistic Decision Regression 基于优化逻辑决策回归的卷积神经网络植物病害预测

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988195

Priyanka Chandani, Shambhavi Gupta, M. S. P. K. Patnaik, N. K. Munagala, A. Sivasangari, H. Tannady

{"title":"Efficient Plant Disease Prediction based on Convolutional Neural Network using Optimized Proposed Logistic Decision Regression","authors":"Priyanka Chandani, Shambhavi Gupta, M. S. P. K. Patnaik, N. K. Munagala, A. Sivasangari, H. Tannady","doi":"10.1109/ICTACS56270.2022.9988195","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988195","url":null,"abstract":"Agriculture nature is important for growing plants with supports of artificial intelligence. This work aims to detect the disease in the leaves, realizing the image analysis and classification technology. Manual identification of medicinal plants is a time-consuming process that requires the help of plant identification experts and manual identification of medicinal plants is a time-consuming process that requires the help of plant identification experts. Specifically, there are several innovations in image segmentation and recognition system for plant disease detection. In this way, to proposed Logistic Decision Regression (LDR) algorithm and Convolutional Neural Network (CNN) is implemented detecting the feature selection and classification. Initially the preprocessing and filter process correction task is usually performed by the wrapping filters. Then LDR feature selection is used to select the best features of medicinal plants for reducing classification problems. Leaves are most used to identify medicinal plants, also stems, flowers, petals, seeds, and even the entire plant used in an automated process. An automated disease detection system is based on the development of changes in the disease status of the plant's leave. For Convolutional Neural Network (CNN), it uses a complex feed-forward neural network, and a CNN has high accuracy in image classification and recognition. After evaluating the results of different image training library systems, effective image recognition function has been demonstrated to have high precision and strong reliability.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127512028","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

Machine Learning Approach to Predict Asthma Prevalence with Decision Trees 用决策树预测哮喘患病率的机器学习方法

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988210

Abeda Begum Mahammad, Rajeev Kumar

{"title":"Machine Learning Approach to Predict Asthma Prevalence with Decision Trees","authors":"Abeda Begum Mahammad, Rajeev Kumar","doi":"10.1109/ICTACS56270.2022.9988210","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988210","url":null,"abstract":"Deep Learning and Machine Learning algorithms are popularly used in the healthcare sector for diagnosis with many algorithms have been successfully implemented to perform patient disease diagnosis and treatment plans. Decision Tree algorithms are profoundly used in the healthcare industry to implement the methods for various disease diagnoses, predictions, therapeutic recommendations, automated tasks, and communication between patients and customer service. Decision Trees work effectively with classification as well as regression techniques. Decision Trees are easy and swift to efficiently implement for faster outcomes in disease diagnosis and they are comprehensively used in data mining and decision-making processes. Decision Trees conjoined with ensemble methods such as Random Forest and Gradient Boost, enhance the performance and accuracy of results in predictions associated with regression tasks. Asthma is an inflammatory and chronic disease that affects a large population worldwide, with severe conditions resulting in emergency visits to the hospital. Asthma is a lung disease caused by airway inflammation and the airways become sensitive to allergic substances. Timely detection of this disease wards off undesirable events, and critical care visits, and is the basis for a good prognosis for patient recovery. Precautionary measures possibly reduce the complications of disease progression by knowing the disease level and associated complications at an early stage. This research article wants to focus on the best model for predicting Asthma prevalence with Decision Tree algorithms as these techniques work faster and provide quicker reports. The Weka tool was used for the model creation with datasets downloaded from data.world and The California Department of Public Health's Open Data Portal.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497086","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

Detection of Eye in a Face Image using Adaboost Algorithm in Comparison with Boosting Algorithm to Measure Accuracy and Sensitivity 用Adaboost算法检测人脸图像中的眼睛，并与boost算法进行比较，以衡量其准确性和灵敏度

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988734

Haranadh Reddy Malepati, S. Premkumar

引用次数: 0

Research on Efficient Software Defect Prediction Using Deep Learning Approaches 基于深度学习方法的高效软件缺陷预测研究

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988292

Razauddin, Sindhu Madhuri G, Ashish Oberoi, Aman Vats, A. Sivasangari, Kuldeep Siwach

{"title":"Research on Efficient Software Defect Prediction Using Deep Learning Approaches","authors":"Razauddin, Sindhu Madhuri G, Ashish Oberoi, Aman Vats, A. Sivasangari, Kuldeep Siwach","doi":"10.1109/ICTACS56270.2022.9988292","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988292","url":null,"abstract":"Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data; it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981297","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

Brain Tumor Detection using Novel Kernel Extreme Learning with Deep Belief Network and Compare Prediction Accuracy with Fuzzy C-means Clustering 基于深度信念网络的新型核极值学习脑肿瘤检测及与模糊c均值聚类的预测精度比较

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988190

V. V. Vardhan Reddy, U. S

引用次数: 0

Improvement in Threshold Voltage and Leakage Current in Double Stacked NCFET Using PZT and PMN Ferroelectric Materials 利用PZT和PMN铁电材料改善双堆叠nfet的阈值电压和泄漏电流

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988069

V. K. Chaubey, R. Rastogi, Arun Kumar, B. Kumar, Aryan Kannaujiya

引用次数: 0

Design and Development of an Agricultural Mobile Application using Machine Learning 使用机器学习的农业移动应用程序的设计与开发

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) Pub Date : 2022-10-10 DOI: 10.1109/ICTACS56270.2022.9988450

Vempati Krishna, Ashish Tamrakar, Rajesh Banala, Damera Saritha, A. Rao, D. Buddhi

{"title":"Design and Development of an Agricultural Mobile Application using Machine Learning","authors":"Vempati Krishna, Ashish Tamrakar, Rajesh Banala, Damera Saritha, A. Rao, D. Buddhi","doi":"10.1109/ICTACS56270.2022.9988450","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988450","url":null,"abstract":"Machine learning algorithms such as KNN and SVM can provide assistance with a variety of issues, including determining what crops should be planted when, as well as determining when the field requires additional water and fertilizer. The proposed system is intended to collect data on the current condition of the soil and make use of that data in order to establish the types of nutrients that are present in the soil. Farmers will be able to identify pest damage to their crops using camera sensor modules for the internet of things. They will be able to take the appropriate actions now that they have the ability to. Through the use of the app, the farmer is able to receive notifications and other information regarding crops based on the conditions of the soil and the weather. The types of soil, crops, nitrogen, potassium, and phosphorus are a few examples of the types of information that fall under this category. In addition to the characteristics of the soil and the weather, farmers can also base their decisions on the kind of crops they grow based on these elements. Because of this, the farmer is given the ability to take the appropriate measures to reduce crop loss and increase crop yield.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122795794","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