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| 1 | Concomitant-chemoradiotherapy-associated oral lesions in patients with oral squamous-cell carcinoma显示文摘Objective:Oral squamous-cell carcinoma(OSCC)accounts for >90% of oral cancers affecting adults mostly between the fourth to seventh decades of life.The most common OSCC treatment is concomitant chemoradiotherapy(CCRT)having both locoregional and distant control,but CCRT has acute and chronic toxic effects on adjacent normal tissue.This study aimed to determine the side effects of CCRT on the oral mucosa and to characterize the clinicopathology of oral lesions in patients with OSCC.Methods:This descriptive,cross-sectional study was certified by the Ethical Review Committee(UHS/Education/126-12/2728)of the University of Health Sciences,Lahore,Pakistan.OSSC patients(n=81)with various histological subtypes,grades,and stages were recruited,and findings on their oral examination were recorded.These patients received 70,90,and 119 Gy of radiotherapy dosages in combination with the chemotherapy drugs cisplatin and 5-fluorouracil.Data were analyzed using SPSS 20.0.Results:The most common presentation of OSCC was a nonhealing ulcer(63%) involving tongue(55.6%).Clinical findings included mucositis(92.6%)and xerostomia of mild,moderate,and severe degrees in 11.1%,46.9%,and 35.8% cases,respectively.Ulcers(87.7%),palpable lymph nodes(64.2%),limited mouth opening(64.2%)and fistula(40.7%) were also observed.In females,the association of radiotherapy dosage with limited mouth opening,xerostomia,and histological grading was statistically significant(P<0.05).The association of chemotherapy drugs with xerostomia(P=0.003)was also statistically significant.Conclusions:CCRT induced mucositis,xerostomia,and trismus in patients with OSCC. | Sadia Minhas Muhammad Kashif Wasif Altaf Nadeem Afzal Abdul Hanan Nagi | 2017 | Cancer Biology & Medicine2017，14，2： | 6 |
| 2 | Ischemic heart diseases in Egypt: role of xanthine oxidase system and ischemia-modified albumin显示文摘 | Ola Sayed Ali Hanan Muhammad Abdelgawad Makram Sayed Mohammed Rehab Refaat El-Awady | 2014 | Heart and Vessels2014，，： | 1 |
| 3 | Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s显示文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection. | Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 4 | Liver Ailment Prediction Using Random Forest Model显示文摘Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated. | Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 5 | A Stochastic Study of the Fractional Order Model of Waste Plastic in Oceans显示文摘In this paper,a fractional order model based on the management of waste plastic in the ocean(FO-MWPO)is numerically investigated.The mathematical form of the FO-MWPO model is categorized into three components,waste plastic,Marine debris,and recycling.The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks(LMQBP-NNs)have been applied to present the numerical solutions of the FO-MWPO system.The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives.The data ratio is provided for training,testing and authorization is 77%,12%,and 11%respectively.The exactness of LMQBP-NNs is observed by using the comparative performances of the obtained and the Adams-BashforthMoulton method.To verify the competence,validity,capability,exactness,and consistency of LMQBP-NNs,the performances have been obtained using the regression,state transitions,error histograms,correlation and mean square error. | Muneerah Al Nuwairan Zulqurnain Sabir Muhammad Asif Zahoor Raja Maryam Alnami Hanan Almuslem | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 6 | Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples显示文摘Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server. | Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 7 | 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 |
| 8 | 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 |
| 9 | Human Emotions Classification Using EEG via Audiovisual Stimuli and AI显示文摘Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the brain.The recorded signals from the brain are rich with useful information.The inference of this useful information is a challenging task.This paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual stimuli.The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned emotions.Using a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are preprocessed.The preprocessed signals then further analyzed and twelve selected features in different domains are extracted.The Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted features.The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different subjects.The proposed model outperforms in terms of cost and accuracy. | Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 10 | Energy Management System Design and Testing for Smart Buildings Under Uncertain Generation (Wind/Photovoltaic) and Demand显示文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand. | Syed Furqan Rafique Jianhua Zhang Muhammad Hanan Waseem Aslam Atiq Ur Rehman Zmarrak Wali Khan | 2018 | Tsinghua Science and Technology2018，23，3： | 0 |
| 11 | Heart Disease Risk Prediction Expending of Classification Algorithms显示文摘Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy. | Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf A.Alshamrani | 2022 | Computers, Materials & Continua2022，，12： | 0 |
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