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Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

Computers, Materials & Continua 2022年第9期72卷 4755-4769页

作者： Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Shahbaz Ahmad yazeed ghadi.zubair nawaz Department of Computer Science National Textile UniversityFaisalabadPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia Department of Software Engineering and Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates Department of Data Science University of the PunjabPakistan

Social media provide digitally interactional technologies to facilitate information sharing and exchanging ***,in case of disasters,a massive corpus is placed on platforms such as *** accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is *** approaches have been developed to address this kind of *** recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic ***,these approaches suffer from limitations as they are dataset-dependent and not *** this paper,we proposed a method to identify eyewitnesses from *** experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the *** each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this *** algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words ***,keyword matching for each feature in tweets is *** a feature exists in a tweet,it is termed as 1;otherwise,***,for 13 features,we created a file that reflects features in each *** classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were *** approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest *** results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure *** the same time,the proposed approach gained a 0.88 value of *** results were comparable as the proposed approach is not ***,it can be used for the identification of eyewitness accounts.

关键词： Word dictionary social media eyewitness identification disasters

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A Deep Learning Approach for Prediction of Protein Secondary Structure

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Computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad zubair Muhammad Kashif Hanif Eatedal Alabdulkreem yazeed ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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