| 2 | Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary显示文摘Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals.Precisely,in case of disasters,a massive corpus is placed on platforms such as Twitter.Eyewitness accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult.Different approaches have been developed to address this kind of problem.The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules.However,these approaches suffer from limitations as they are dataset-dependent and not scalable.In this paper,we proposed a method to identify eyewitnesses from Twitter.To experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness.Considering each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this research.This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary.Further,keyword matching for each feature in tweets is performed.If a feature exists in a tweet,it is termed as 1;otherwise,0.Similarly,for 13 features,we created a file that reflects features in each tweet.To classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were utilized.The approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest fires.The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values.At the same time,the proposed approach gained a 0.88 value of F-measure.The results were comparable as the proposed approach is not dataset-dependent.Therefore,it can be used for the identification of eyewitness accounts. | Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Shahbaz Ahmad Yazeed Ghadi Zubair nawaz | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 3 | Convolutional Neural Network for Histopathological Osteosarcoma Image Classification显示文摘Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate.Early diagnosis may increase the chances of treatment and survival however the process is time-consuming(reliability and complexity involved to extract the hand-crafted features)and largely depends on pathologists’experience.Convolutional Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid problems.Therefore,this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet(a high-class imbalanced dataset).Though,during training,class-imbalanced data can negatively affect the performance of CNN.Therefore,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance.In this process,a hierarchical CNN model is designed,in which the former model is non-regularized(due to dense architecture)and the later one is regularized,specifically designed for small histopathology images.Moreover,the regularized model is integrated with CNN’s basic architecture to reduce overfitting.Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training.The training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,respectively.The regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset. | Imran Ahmed Humaira Sardar Hanan Aljuaid Fakhri Alam Khan Muhammad Nawaz Adnan Awais | 2021 | Computers, Materials & Continua2021，，12： | 0 |
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