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News Modeling and Retrieving Information: Data-Driven Approach

Intelligent Automation & Soft Computing 2023年第11期38卷 109-123页

作者： Elias Hossain Abdullah Alshahrani wahidur rahman Electrical&Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science and Engineering Uttara UniversityDhaka1230Bangladesh

This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic *** Methodology of this study is categorized into three phases:the Text Classiﬁcation Approach(TCA),the Proposed Algorithms Interpretation(PAI),andﬁnally,Information Retrieval Approach(IRA).The TCA reﬂects the text preprocessing pipeline called a clean *** Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this *** PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 ***,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this ***,some text analyses and the most inﬂuential aspects of each document have been explored in this *** have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered ***,the proposed system can be adjustable in the real-time news classiﬁcation of COVID-19.

关键词： COVID-19 news retrieving data-driven machine learning BERT topic modelling

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IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

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Computers, Materials & Continua 2022年第7期72卷 2033-2053页

作者： wahidur rahman Naif Al Mudawi Abdulwahab Alazeb Muhammad Minoar Hossain Saima Siddique Tashfia MdTarequl Islam Shisir Mia Mohammad Motiur rahman Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh Department of Computer Science Najran UniversityNajran55461Saudi Arabia

On the edge of the worldwide public health crisis,the COVID-19 disease has become a serious headache for its destructive nature on humanity *** a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death ***,wearing a face mask in public places such as shopping malls,hotels,restaurants,homes,and offices needs to be *** research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning(DL),Internet of things(IoT),and *** absence or displacement of the mask will be identified with a raspberry pi,a camera module,and the operations of DL and Machine Learning(ML).The detected information will be sent to the cloud server with the mechanism of IoT for real-time data *** proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security,monitoring,and storage *** research further includes an IoT-based mask detection scheme with signal bulbs,alarms,and notifications in the *** find the efficacy of the proposed method,a set of experiments has been enumerated and *** research work finds the highest accuracy of 99.95%in the detection and classification of facial *** related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data *** Scale(SUS)has been accomplished to check the satisfaction level of use and found the SUS score of 77%.Further,a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed ***,the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification.

关键词： Internet of things(IoT) blockchain deep learning feature reduction surveillance system

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Dm-Health App:Diabetes Diagnosis Using Machine Learning with Smartphone

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Computers, Materials & Continua 2022年第7期72卷 1713-1746页

作者： Elias Hossain Mohammed Alshehri Sultan Almakdi Hanan Halawani Md.Mizanur rahman wahidur rahman Sabila Al Jannat Nadim Kaysar Shishir Mia Department of Software Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science Najran UniversityNajran55461Saudi Arabia Department of Computer Science and Engineering Rajshahi University of Engineering and TechnologyRajshahi6204Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of Computer Science and Engineering World University BangladeshDhaka1230Bangladesh

Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate *** research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical *** study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture *** Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster *** Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to *** Doctor Consultation Module ensures free treatment related to *** this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&*** evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,*** manuscript presents some exploratory data analysis,including a correlation matrix and survey ***,the proposed solution can be adjustable in the daily activities of a diabetic patient.

关键词： Machine learning diabetes-prediction support vector machine(SVM) LightGBM eHealth ROC-AUC

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Forecasting Mental Stress Using Machine Learning Algorithms

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Computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque wahidur rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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