International Journal of Artificial Intelligence & Applications最新文献_第4页

Prior Bush Fire Identification Mechanism based on Machine Learning Algorithms 基于机器学习算法的丛林火灾先验识别机制

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13405

C. Atheeq, Mohammad Mohammad, Aleem Mohammed

{"title":"Prior Bush Fire Identification Mechanism based on Machine Learning Algorithms","authors":"C. Atheeq, Mohammad Mohammad, Aleem Mohammed","doi":"10.5121/ijaia.2022.13405","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13405","url":null,"abstract":"Besides causing awful fatalities resulting in deaths and significant resources like many acres of timberland and dwelling places, forest fires are a significant threat to sound enormous wilderness biologically and environmentally. Consistently, a considerable number of fires around the globe reason debacles to different habitats and layouts. The stated matter has been the investigation premium for a significant length of time; there is a considerable amount of good concentrated on arrangements available for testing or perhaps ready to be utilized to determine this disadvantage. Woods and actual flames have been severe issues for quite some time. Presently, there is a wide range of answers for distinguishing woods fires. Individuals are utilizing sensors to determine the fire. However, this case isn't workable for vast sections of land woods. This paper discusses another fire-recognition methodology with incremental advancements. Specifically, we put forward a stage-Artificial Intelligence. The PC innovation strategies for acknowledgment and whereabouts of smog and fires, in light of the inert photographs or the graphics captured by the cameras. AI for tracing down the fires. The accuracy relies on the calculations that use dataset values later divided in various test and train sets, respectively.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124307373","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

Multiple Instance Learning Networks for Stock Movements Prediction with Financial News 基于财经新闻的股票走势预测的多实例学习网络

International Journal of Artificial Intelligence & Applications Pub Date : 2022-07-31 DOI: 10.5121/ijaia.2022.13402

Yiqi Deng, Siu Ming Yiu

引用次数: 0

COVFILTER: A Low-cost Portable Device for the Prediction of Covid-19 for Resource-Constrained Rural Communities COVFILTER:一种用于资源受限农村社区Covid-19预测的低成本便携式设备

International Journal of Artificial Intelligence & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijaia.2022.13201

Sajedul Talukder, F. Hossen

{"title":"COVFILTER: A Low-cost Portable Device for the Prediction of Covid-19 for Resource-Constrained Rural Communities","authors":"Sajedul Talukder, F. Hossen","doi":"10.5121/ijaia.2022.13201","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13201","url":null,"abstract":"Early identification of COVID-19 is critical for preventing death and significant illness. People living in remote parts of resource-constrained countries find it more difficult to get tested due to a lack of adequate testing. As a result, having a primary filtering tool that can assist us in simplifying bulk COVID testing to prevent community spread is vital. In this paper, we introduce CovFilter, a low-cost portable device for COVID-19 prediction for resource-constrained rural communities, with the goal of encouraging people to be tested for COVID-19 in a more informed manner. CovFilter Hardware Module collects health parameters from three sensors while the CovFilter Prediction Module predicts COVID-19 status using the health data. We train supervised learning algorithms and an artificial neural network to predict COVID-19 from vital sign readings where MultilayerPerceptron outperformed ANN, NaiveBayes, Logistic, SGD, DecisionStump, and SVM with an F1 of 93.22%. We further show that a weighted majority voting ensemble classifier can outperform all single classifiers achieving an F1 of over 94%.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122898768","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

Diagnosis of Obesity Level based on Bagging Ensemble Classifier and Feature Selection Methods 基于Bagging集成分类器和特征选择方法的肥胖水平诊断

International Journal of Artificial Intelligence & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijaia.2022.13203

A. Alzayed, Waheeda Almayyan, A. Al-Hunaiyyan

{"title":"Diagnosis of Obesity Level based on Bagging Ensemble Classifier and Feature Selection Methods","authors":"A. Alzayed, Waheeda Almayyan, A. Al-Hunaiyyan","doi":"10.5121/ijaia.2022.13203","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13203","url":null,"abstract":"In the current era, the amount of data generated from various device sources and business transactions is rising exponentially, and the current machine learning techniques are not feasible for handling the massive volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a minimal number of feature subsets. We utilized several machine learning algorithms for classifying the obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be promising tools for feature selection in respect of the quality of obtained feature subset and computation efficiency. Analyzing the results obtained from the original and modified datasets has improved the classification accuracy and established a relationship between obesity/overweight and common risk factors such as weight, age, and physical activity patterns.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127100713","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

Credit Risk Management using Artificial Intelligence Techniques 基于人工智能技术的信用风险管理

International Journal of Artificial Intelligence & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijaia.2022.13205

Karim Amzile, Rajaa Amzile

{"title":"Credit Risk Management using Artificial Intelligence Techniques","authors":"Karim Amzile, Rajaa Amzile","doi":"10.5121/ijaia.2022.13205","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13205","url":null,"abstract":"Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial intelligence techniques to the prediction of the creditworthy of bank customers, for this we used historical data of bank customers. However we have adopted the flowing process, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (DT-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133027325","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

Forged Character Detection Datasets: Passports, Driving Licences and Visa Stickers 伪造字符检测数据集:护照，驾驶执照和签证贴纸

International Journal of Artificial Intelligence & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijaia.2022.13202

Teerath Kumar, Muhammad Turab, Shahnawaz Talpur, Rob Brennan, Malika Bendechache

{"title":"Forged Character Detection Datasets: Passports, Driving Licences and Visa Stickers","authors":"Teerath Kumar, Muhammad Turab, Shahnawaz Talpur, Rob Brennan, Malika Bendechache","doi":"10.5121/ijaia.2022.13202","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13202","url":null,"abstract":"Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm starts by reading plain document images, simulates forging simulation tasks on five different countries' passports, driving licences and VISA stickers. Then it keeps the bounding boxes as a track of the forged characters as a labeling process. Furthermore, considering the real world scenario, we performed the selected data augmentation accordingly. Regarding the stats of datasets, each dataset consists of 15000 images having size of 950 x 550 of each. For further research purpose we release our algorithm code 1 and, datasets i.e. FCD-P 2 , FCD-D 3 and FCD-V 4.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134280563","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: 7

AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students AIPSYCH:一个基于移动应用程序的人工精神病学家，用于预测学生的心理疾病和康复建议

International Journal of Artificial Intelligence & Applications Pub Date : 2022-03-31 DOI: 10.5121/ijaia.2022.13204

F. Hossen, Sajedul Talukder, Refatul Fahad

{"title":"AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students","authors":"F. Hossen, Sajedul Talukder, Refatul Fahad","doi":"10.5121/ijaia.2022.13204","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13204","url":null,"abstract":"COVID-19’s outbreak affected and compelled people from all walks of life to self-quarantine in their houses in order to prevent the virus from spreading. As a result of adhering to the exceedingly strict guideline, many people developed mental illnesses. Because the educational institution was closed at the time, students remained at home and practiced self-quarantine. As a result, it is necessary to identify the students who developed mental illnesses at that time. To develop AiPsych, a mobile application-based artificial psychiatrist, we train supervised and deep learning algorithms to predict the mental illness of students during the COVID-19 situation. Our experiment reveals that supervised learning outperforms deep learning, with a 97% accuracy of the Support Vector Machine (SVM) for mental illness prediction. Random Forest (RF) achieves the best accuracy of 91% for the recovery suggestion prediction. Our android application can be used by parents, educational institutes, or the government to get the predicted result of a student’s mental illness status and take proper measures to overcome the situation.","PeriodicalId":391502,"journal":{"name":"International Journal of Artificial Intelligence & Applications","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121514431","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

International Journal of Artificial Intelligence &amp; Applications最新文献

International Journal of Artificial Intelligence & Applications最新文献