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| 1 | Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan显示文摘Background:Crimean Congo hemorrhagic fever(CCHF)is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in Pakistan.The aim of this study was to identify spatial clusters of human cases of CCHF reported in country.Methods:Kulldorff’s spatial scan statisitc,Anselin’s Local Moran’s I and Getis Ord Gi*tests were applied on data(i.e.number of laboratory confirmed cases reported from each district during year 2013).Findings:The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with Afghanistan.The cluster comprised the following districts:Qilla Abdullah;Qilla Saifullah;Loralai,Quetta,Sibi,Chagai,and Mastung.Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad.Conclusion:We provide empirical evidence of spatial clustering of human CCHF cases in the country.The districts in the clusters should be given priority in surveillance,control programs,and further research. | Tariq Abbas Muhammad Younus Sayyad Aun Muhammad | 2015 | Infectious Diseases of Poverty2015，4，1： | 2 |
| 2 | SurgicalRepair of Congenital Recto-Vaginal Fistula with AtresiaAni in a Cow Calf显示文摘 | Shakoor A Muhammad SA Younus M | 2012 | Pakistan Veterinary Journal2012，32，2： | 1 |
| 3 | In vitro and in vivo acaricidal activity of a herbal extract显示文摘 | Muhammad Arfan Zaman Zafar Iqbal Rao Zahid Abbas Muhammad Nisar Khan Ghulam Muhammad Muhammad Younus Sibtain Ahmed | 2011 | Veterinary Parasitology (-)2011，，3： | 1 |
| 4 | Prevalence and Molecular Characterization of Dengue Viruses Serotypes in 2010 Epidemic显示文摘 | Nasir Mahmood Muhammad Younus Rana Zafar Qureshi Ghulam Mujtaba Uzma Shaukat | 2012 | The American Journal of the Medical Sciences2012，，1： | 1 |
| 5 | Research on knowledge-based system for typical aircraft composite component design显示文摘 | Mei Zhongyi Zhu Sanshan Younus Muhammad | 2011 | Procedia Engineering2011，15，5： | 1 |
| 6 | Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework显示文摘Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques. | Amina Bibi Muhamamd Attique Khan Muhammad Younus Javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr | 2022 | Computers, Materials & Continua2022，，5： | 1 |
| 7 | Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video显示文摘The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions that can be addressed to obtain the user’s Quality-of-Experience(QoE). 360-degree videos have already taken up the user’s behavior by storm.However, the users only focus on the part of 360-degree videos, known as aviewport. Despite the immense hype, 360-degree videos convey a loathsomeside effect about viewport prediction, making viewers feel uncomfortablebecause user viewport needs to be pre-fetched in advance. Ideally, we canminimize the bandwidth consumption if we know what the user motionin advance. Looking into the problem definition, we propose an EncoderDecoder based Long-Short Term Memory (LSTM) model to more accuratelycapture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct inputto predict the future user movement. Then, this prediction model is combinedwith a rate adaptation approach that assigns the bitrates to various tiles for360-degree video frames under a given network capacity. Hence, our proposedwork aims to facilitate improved system performance when QoE parametersare jointly optimized. Some experiments were carried out and compared withexisting work to prove the performance of the proposed model. Last but notleast, the experiments implementation of our proposed work provides highuser’s QoE than its competitors. | Muhammad Usman Younus Rabia Shafi Ammar Rafiq Muhammad Rizwan Anjum Sharjeel Afridi Abdul Aleem Jamali Zulfiqar Ali Arain | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 8 | Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm显示文摘Transmission line is a vital part of the power system that connects two major points,the generation,and the distribution.For an efficient design,stable control,and steady operation of the power system,adequate knowledge of the transmission line parameters resistance,inductance,capacitance,and conductance is of great importance.These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable.This paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission line.The equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured data.The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function.An improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters.Two cases are considered for parameter estimation,the first case is when the line conductance is neglected and in the second case,the conductance is considered into account.The results obtained by the improved algorithm are compared with the standard version of the algorithm,firefly algorithm and artificial bee colony algorithm for 30 number of trials.It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis,comparatively. | Abdullah Shoukat Muhammad Ali Mughal Saifullah Younus Gondal Farhana Umer Tahir Ejaz Ashiq Hussain | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 9 | Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization显示文摘Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors.The major covariant factors are walking while carrying a bag and walking while wearing a coat.Deep learning is a new machine learning technique that is gaining popularity.Many techniques for HGR based on deep learning are presented in the literature.The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this work.The proposed framework consists of four primary steps.In the first step,the database is normalized in a video frame.In the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature.After that,we trained both modified models using transfer learning and extracted the features.The IACO algorithm is used to improve the extracted features.IACO is used to select the best features,which are then passed to the Cubic SVM for final classification.The cubic SVM employs a multiclass method.The experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time. | Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 10 | Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm显示文摘Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton consumption.Nearly 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton products.Approximately 1.5 million people in Pakistan are engaged in the cotton value chain.However,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton production.Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert.Therefore,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early stages.In this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature selection.The proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further recognition.For final recognition,a Cubic SVM approach was utilized and validated on a prepared dataset.On the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework.. | Muhammad Rizwan Latif Muhamamd Attique Khan Muhammad Younus Javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry | 2021 | Computers, Materials & Continua2021，，12： | 0 |
| 11 | Multi-Layered Deep Learning Features Fusion for Human Action Recognition显示文摘Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.Features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution. | Sadia Kiran Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius Muhammad Sharif | 2021 | Computers, Materials & Continua2021，，12： | 0 |
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