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| 1 | Structural, morphological, Raman, optical, magnetic, and antibacterial characteristics of CeO2 nanostructures显示文摘In this study, CeO_2 nanostructures were synthesized by a soft chemical method. A hydrothermal treatment was observed to lead to an interesting morphological transformation of the nanoparticles into homogeneous microspheres composed of nanosheets with an average thickness of 40 nm. Structural analysis revealed the formation of a single-phase cubic fluorite structure of CeO_2 for both samples. A Raman spectroscopic study confirmed the XRD results and furthermore indicated the presence of a large number of oxygen vacancies in the nanosheets. These oxygen vacancies led to room-temperature ferromagnetism(RTFM) of the CeO_2 nanosheets with enhanced magnetic characteristics. Amazingly, the nanosheets exhibited substantially greater antibacterial activity than the nanoparticles. This greater antibacterial activity was attributed to greater exposure of high-surface-energy polar surfaces and to the presence of oxygen vacancies. | Fazal Abbas Javed Iqbal Tariq Jan Noor Badshah Qaisar Mansoor Muhammad Ismail | 2016 | International Journal of Minerals,Metallurgy and Materials2016，23，1： | 6 |
| 2 | Potentially harmful metals,and health risk evaluation in groundwater of Mardan,Pakistan:Application of geostatistical approach and geographic information system显示文摘This study investigates the values of pH,total dissolved solids(TDS),elevation,oxidative reduction potential(ORP),temperature,and depth,while the concentrations of Br,and potentially harmful metals(PHMs)such as Cr,Ni,Cd,Mn,Cu,Pb,Co,Zn,and Fe in the groundwater samples.Moreover,geographic information system(GIS),XLSTAT,and IBM SPSS Statistics 20 software were used for spatial distribution modeling,principal component analysis(PCA),cluster analysis(CA),and Quantile-Quantile(Q-Q)plotting to determine groundwater pollution sources,similarity index,and normal distribution reference line for the selected parameters.The mean values of pH,TDS,elevation,ORP,temperature,depth,and Br were 7.2,322 mg/L,364 m,188 mV,29.6℃,70 m,0.20 mg/L,and PHMs like Cr,Ni,Cd,Mn,Cu,Pb,Co,Zn,and Fe were 0.38,0.26,0.08,0.27,0.36,0.22,0.04,0.43 and 0.86 mg/L,respectively.PHMs including Cr(89%),Cd(43%),Mn(23%),Pb(79%),Co(20%),and Fe(91%)exceeded the guideline values set by the world health organization(WHO).The significant R^(2)values of PCA for selected parameters were also determined(0.62,0.67,0.78,0.73,0.60,0.87,-0.50,0.69,0.70,0.74,-0.50,0.70,0.67,0.79,0.59,and-0.55,respectively).PCA revealed three geochemical processes such as geogenic,anthropogenic,and reducing conditions.The mineral phases of Cd(OH)_(2),Fe(OH)_(3),FeOOH,Mn_(3)O_(4),Fe_(2)O_(3),MnOOH,Pb(OH)_(2),Mn(OH)_(2),MnO_(2),and Zn(OH)_(2)(-3.7,3.75,9.7,-5.8,8.9,-3.6,2.2,-4.6,-7.7,-0.9,and 0.003,respectively)showed super-saturation and under-saturation conditions.Health risk assessment(HRA)values for PHMs were also calculated and the values of hazard quotient(HQ),and hazard indices(HI)for the entire study area were increased in the following order:Cd>Ni>Cu>Pb>Mn>Zn>Cr.Relatively higher HQ and HI values of Ni,Cd,Pb,and Cu were greater than one showing unsuitability of groundwater for domestic,agriculture,and drinking purposes.The long-term ingestion of groundwater could also cause severe health concerns such as kidney,brain dysfunction,liver,stomach problems,and even cancer. | Abdur Rashid Muhammad Ayub Asif Javed Sardar Khan Xubo Gao Chengcheng Li Zahid Ullah Tariq Sardar Juma Muhammad Shahla Nazneen | 2021 | Geoscience Frontiers2021，12，3： | 2 |
| 3 | Development of bis-thiobarbiturates as successful urease inhibitors and their molecular modeling studies显示文摘Bis-thiobarbiturate derivatives 1–15 have been synthesized, characterized by1 HNMR and EI-MS and screened for urease inhibition. All compounds showed various degree of urease inhibitory activity with IC_(50) values ranging 7.45 0.12 74.24 0.81 mmol/L while the standard thiourea behaved normally(IC_(50) = 21.10 0.12). Compounds 1(IC_(50) = 7.45 0.12 μmol/L), 9(IC_(50) = 18.17 1.03 μmol/L) and 13(IC_(50) = 8.61 0.45 μmol/L) showed excellent urease inhibitory activity in the series. Molecular modeling studies were performed to understand the binding site with the bimetallic nickel center of the enzyme.Structure-activity relationship has also been established for these compounds. This study identified bisthiobarbiturate as a novel class of urease inhibitors. | Fazal Rahim Muhammad Ali Shifa Ullah Umer Rashid Hayat Ullah Muhammad Taha Muhammad Tariq Javed Wajid Rehman Aftab Ahmad Khan Obaid Ur Rahman Abid Muhammad Bilal | 2016 | Chinese Chemical Letters2016，27，5： | 2 |
| 4 | Assessment of Metals Concentration and Ecotoxicology of the Sediment Core of Rehri Creek, Karachi Coast, Pakistan显示文摘Trace metal concentrations were investigated in a recent sediment core collected from the Rehri Creek area of the Karachi coast,Sindh-Pakistan.The core was sliced horizontally at 2.5-cm intervals to determine grain size,sediment composition,pH,organic matter,and acid-leachable trace metals:cadmium,chromium,copper,lead,and zinc.The trace metals were analyzed by ICP.To separate anthropogenic from geogenic input,several approaches were made,including comparison with sediment quality guidelines—ecotoxicological sense of heavy metal contamination and classification by quantitative indexes.Grain-size analysis and sediment composition of core sample show a sandy nature with neutral pH.Elemental sequence(ES)of the trace metals is in the order of Zn(19.2-109.56 ppm)>Si(66.46-101.71 ppm)>Ba(12.05-26.86 ppm)>As(8.18-17.36 ppm)>Ni(4.2-14.69 ppm)>Cr(3.02-9.62 ppm)>Pb(2.79-6.83 ppm)>Cu(2.2-5.29 ppm)>Co(0.9-2.05 ppm).Thus it is likely that the area may face a serious threat of metal pollution with the present deposition rates unless stringent pollution control norms are adopted.The Sediment Geo-accumulation Index shows that there is no Cr,Cu,Ni,Pb,Zn,or Fe pollution;however,the former index and the Pollution Load Index indicate arsenic pollution in the sediments. | Muhammad Z. CHAUDHARY Nasir AHMAD Azhar MASHIATULLAH Sajjad MUNIR Tariq JAVED | 2013 | Acta Geologica Sinica(English Edition)2013，87，5： | 1 |
| 5 | Facile Synthesis as well as Structural, Raman, Dielectric and Antibacterial Characteristics of Cu Doped ZnO Nanoparticles显示文摘Here, undoped and Cu doped ZnO nanoparticles(NPs) have been prepared by chemical co-precipitation technique. X-ray diffraction(XRD) results reveal that Cu ions are successfully doped into ZnO matrix without altering its wurtzite phase. The single wurtzite phase of ZnO is retained even for 10 wt% Cu doped ZnO sample. It is observed from the electron microscopy results that higher level of Cu doping varies the morphology of ZnO NPs from spherical to flat NPs. Moreover, the particle size is found to increase with the increase in Cu doping level. Raman spectroscopy results further confirm that Cu dopant has not altered the wurtzite structure of ZnO. Impedance spectroscopy results reveal that the dielectric constant and dielectric loss have increasing trend with Cu doping. Cu doping has been found to slightly decrease the bactericidal potency of ZnO nanoparticles. | Javed Iqbal Nauman Safdar Tariq Jan Muhammad Ismail S.S.Hussain Arshad Mahmood Shaheen Shahzad Qaisar Mansoor | 2015 | Journal of Materials Science & Technology2015，31，3： | 1 |
| 6 | Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI显示文摘Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and classification.In traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data.The major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy results.In this study,motor imagery(MI)signals have been classified using different deep learning algorithms.We have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset IIa.The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms.Amongst the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%. | Ayesha Sarwar Kashif Javed Muhammad Jawad Khan Saddaf Rubab Oh-Young Song Usman Tariq | 2021 | Computers, Materials & Continua2021，，9： | 1 |
| 7 | 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 |
| 8 | Sn doping induced enhancement in the activity of ZnO nanostructures against antibiotic resistant S. aureus bacteria显示文摘 | Tariq Jan Badshah Zakaullah Muhammad Ismail Javed Iqbal Sajjad Haider | 2013 | International Journal of Nanomedicine2013，，： | 1 |
| 9 | A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images显示文摘Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis. | Abdullah A.Asiri Amna Iqbal Javed Ferzund Tariq Ali Muhammad Aamir Khalaf A.Alshamrani Hassan A.Alshamrani Fawaz F.Alqahtani Muhammad Irfan Ali H.D.Alshehri | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 10 | 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 |
| 11 | 3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging显示文摘Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones.Diagnosing a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert radiologist.However,this process is time-consuming and costly.Therefore,a computerized technique is required for brain tumor detection in MRI images.Using the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing technique.The standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model validation.Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,respectively.Overall,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning. | Kainat Nazir Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Usman Tariq Jae-Hyuk Cha | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 12 | National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma显示文摘A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the experts.The Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this meeting.This effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these patients.This coordinated effort can minimise delays and give patients a chance for curative treatment and effective palliation.The diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance cholangiopancreato-graphy.Brush cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for diagnosis.However,histopathologic confirmation is not always required before resection.Endoscopic ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for staging.The only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette classification.Selected patients with unresectable hCCA can be considered for liver transplantation.Adjuvant chemotherapy should be offered to patients with a high risk of recurrence.The use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary discussions.Patients with acute cholangitis can be drained with endoscopic or percutaneous biliary drainage.Palliative chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA. | Faisal Saud Dar Zaigham Abbas Irfan Ahmed Muhammad Atique Usman Iqbal Aujla Muhammad Azeemuddin Zeba Aziz Abu Bakar Hafeez Bhatti Tariq Ali Bangash Amna Subhan Butt Osama Tariq Butt Abdul Wahab Dogar Javed Iqbal Farooqi Faisal Hanif Jahanzaib Haider Siraj Haider Syed Mujahid Hassan Adnan Abdul Jabbar Aman Nawaz Khan Muhammad Shoaib Khan Muhammad Yasir Khan Amer Latif Nasir Hassan Luck Ahmad Karim Malik Kamran Rashid Sohail Rashid Mohammad Salih Abdullah Saeed Amjad Salamat Ghias-un-Nabi Tayyab Aasim Yusuf Haseeb Haider Zia Ammara Naveed | 2024 | World Journal of Gastroenterology2024，30，9： | 0 |
| 13 | 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 |
| 14 | 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 |
| 15 | A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor显示文摘Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular shapes.Recently,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets.However,3D networks can be computationally expensive and require significant training resources.This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy.The proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI volumes.Attention and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields.The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset respectively.Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models.Overall,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners. | Wajiha Rahim Khan Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Majed Alhaisoni Usman Tariq Jae-Hyuk Cha | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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