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| 1 | Micro RNAs in colorectal cancer:Role in metastasis and clinical perspectives显示文摘Colorectal cancer(CRC)is the third most common malignancy and the third leading cause of cancer related deaths in the United States.Almost 90%of the patients diagnosed with CRC die due to metastases.Micro RNAs(mi RNAs)are evolutionarily conserved molecules that modulate the expression of their target genes post-transcriptionally,and they may participate in various physiological and pathological processes including CRC metastasis by influencing various factors in the human body.Recently,the role mi RNAs play throughout the CRC metastatic cascade has gain attention.Many studies have been published to link them with CRC metastasis.In this review,we will briefly discuss metastatic steps in the light of mi RNAs,along with their target genes.We will discuss how the aberration in the expression of mi RNAs leads to the formation of CRC by effecting the regulation of their target genes.As mi RNAs are being exploited for diagnosis,prognosis,and monitoring of cancer and other diseases,their high tissue specificity and critical role in oncogenesis make them new biomarkers for the diagnosis and classification of cancer as well as for predicting patients’outcome.Mi RNA signatures have been identified for many human tumors including CRC,and mi RNA-based therapies to treat cancer have been emphasized lately.These will also be discussed in this review. | Shan Muhammad Kavanjit Kaur Rui Huang Qian Zhang Paviter Kaur Hamza Obaid Yazdani Muhammad Umar Bilal Jiang Zheng Liu Zheng Xi-Shan Wang | 2014 | World Journal of Gastroenterology2014，20，45： | 13 |
| 2 | Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network显示文摘Herbicide use is rising globally to enhance food production,causing harm to environment and the ecosystem.Precision agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse effects of herbicides.Accurate weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture data.Labelling large agriculture data at pixel level is a time-consuming and tedious job.In this paper,a methodology is developed to accelerate manual labelling of pixels using a two-step procedure.In the first step,the background and foreground are segmented using maximum likelihood classification,and in the second step,the weed pixels are manually labelled.Such labelled data is used to train semantic segmentation models,which classify crop and background pixels as one class,and all other vegetation as the second class.This paper evaluates the proposed methodology on high-resolution colour images of canola fields and makes performance comparison of deep learning meta-architectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50.ResNet-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869. | Muhammad Hamza Asad Abdul Bais | 2020 | Information Processing in Agriculture2020，7，4： | 3 |
| 3 | 双金属镍-锰/γ-Al2O3催化剂对甲烷二氧化碳重整反应的影响显示文摘用浸渍法制备γ-Al2O3负载的Ni-Mn双金属催化剂.在500-700。C按照17:17:2的C02/CHa/N2比例,以36mL/min的载气流速进行甲烷二氧化碳重整反应,利用甲烷二氧化碳的转化率、生成的合成气H2/CO比例以及长期稳定性等指标评价了催化剂的催化性能.实验表明,添加Mn提高催化性能并使双金属催化剂的稳定性更高,比单金属催化剂更好地抑制焦炭生成,Mn最合适的添加量为0.5wt%.通过BET、C02-TPD、TGA、XRD、SEM、EDX和FTIR各种技术对催化剂进行了表征. | Anis Hamza Fakeeha Muhammad Awais Naeem Wasim Ullah Khan Ahmed Elhag Abasaeed Ahmed Sadeq Al-Fatesh | 2014 | Chinese Journal of Chemical Physics2014，27，2： | 1 |
| 4 | Frequency-Agile WLAN Notch UWB Antenna for URLLC Applications显示文摘This paper introduces a compact dual notched UWB antenna with an independently controllable WLAN notched band integrated with fixed WiMAX band-notch.The proposed antenna utilizes a slot resonator placed in the main radiator of the antenna for fixed WiMAX band notch,while an inverted L-shaped resonator in the partial ground plane for achieving frequency agility within WLAN notched band.The inverted L-shaped resonator is also loaded with fixed and variable capacitors to control and adjust the WLAN notch.The WLAN notched band can be controlled independently with a wide range of tunability without disturbing the WiMAX bandnotch performance.Step by step design approach of the proposed antenna is discussed and the corresponding mathematical analysis of the proposed resonators are provided in both cases.Simulation of the proposed antenna is performed utilizing commercially available 3D-EM simulator,Ansoft High Frequency Structure Simulator(HFSS).The proposed antenna has high selectivity with experimental validation in terms of reflection coefficient,radiation characteristics,antenna gain,and percentage radiation efficiency.The corresponding measured frequency response of the input port corresponds quite well with the calculations and simulations in both cases.The proposed antenna is advantageous and can adjust according to the device requirements and be one of the attractive candidates for overlay cognitive radio UWB applications and URLLC service in 5G tactile internet.The proposed multifunctional antenna can also be used for wireless vital signs monitoring,sensing applications,and microwave imaging techniques. | Amir Haider MuhibUr Rahman Hamza Ahmad Mahdi NaghshvarianJahromi Muhammad Tabish Niaz Hyung Seok Kim | 2021 | Computers, Materials & Continua2021，，5： | 1 |
| 5 | A single-step synthesis of nitrogen-doped graphene sheets decorated with cobalt hydroxide nanoflakes for the determination of dopamine显示文摘Nitrogen-doped reduced graphene oxide(NrGO)sheets decorated with Co(OH)_2nanoflakes were prepared by a single-step hydrothermal process.The morphological and structural characterizations of as synthesized Nr GO@Co(OH)_2nanoflakes were performed by field emission scanning electron microscopy(FESEM),EDX-mapping and X-ray diffraction(XRD).Nr GO@Co(OH)_2nanoflakes modified glassy carbon electrode(GCE)was used for electrochemical sensing of dopamine in neutral medium.The nanocomposite modified electrode showed enhanced electrochemical sensing ability for the detection of dopamine and the limit of detection(Lo D)was found to be 0.201μM with a sensitivity value of 0.0286±0.002 m A m M^(-1).Interference studies revealed that Nr GO@Co(OH)_2─GCE endow excellent selectivity for DA detection even in the presence of higher concentration of common co-existing physiological interfering analytes.Additionally,proposed sensor demonstrated excellent performance in urine samples with promising reproducibility and stability. | Muhammad Mehmood Shahid Ahmad H. Ismail Ali MA. Abdul Amir AL-Mokaram R. Vikneswaran Sohail Ahmad Amir Hamza Arshid Numan | 2017 | Progress in Natural Science:Materials International2017，27，5： | 1 |
| 6 | Central depressant activity of butanol fraction of Securinega virosa root bark in mice显示文摘 | Mohammed Garba Magaji Abdullahi Hamza Yaro Aliyu Muhammad Musa Joseph Akponso Anuka Ibrahim Abdu-Aguye Isa Marte Hussaini | 2012 | Journal of Ethnopharmacology2012，，1： | 1 |
| 7 | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification显示文摘Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%. | Ibrar Amin Saima Hassan Samir Brahim Belhaouari Muhammad Hamza Azam | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 8 | Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions显示文摘Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 years.Many strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity prices.Fuzzy c-means(FCM)is one of the popular clustering approach for generating fuzzy membership functions.However,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian MF.The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions.As a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required.Therefore,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm.The approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is used.The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted. | Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan Said Jadid Abdulkadir | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 9 | Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein:An in silico approach显示文摘Objective:To predict immunogenic promiscuous T-cell epitopes from the polyprotein of the Zika virus using a range of bioinformatics tools.To date,no epitope data are available for the Zika virus in the IEDB database.Methods:We retrieved nearly 54 full length polyprotein sequences of the Zika virus from the NCBI database belonging to different outbreaks.A consensus sequence was then used to predict the promiscuous T cell epitopes that bind MHC 1 and MHC II alleles using Propred1 and Propred immunoinformatic algorithms respectively.The antigencity predicted score was also calculated for each predicted epitope using the Vaxi Jen 2.0 tool.Results:By using Pro Pred1,23 antigenic epitopes for HLA class I and 48 antigenic epitopes for HLA class II were predicted from the consensus polyprotein sequence of Zika virus.The greatest number of MHC class I binding epitopes were projected within the NS5(21%),followed by Envelope(17%).For MHC class II,greatest number of predicted epitopes were in NS5(19%) followed by the Envelope,NS1 and NS2(17% each).A variety of epitopes with good binding affinity,promiscuity and antigenicity were predicted for both the HLA classes.Conclusion:The predicted conserved promiscuous T-cell epitopes examined in this study were reported for the first time and will contribute to the imminent design of Zika virus vaccine candidates,which will be able to induce a broad range of immune responses in a heterogeneous HLA population.However,our results can be verified and employed in future efficacious vaccine formulations only after successful experimental studies. | Hamza Dar Tahreem Zaheer Muhammad Talha Rehman Amjad Ali Aneela Javed Gohar Ayub Khan Mustafeez Mujtaba Babar Yasir Waheed | 2016 | Asian Pacific Journal of Tropical Medicine2016，9，9： | 0 |
| 10 | QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities显示文摘In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods. | Khaled Mohamad Almustafa Taiseer Abdalla Elfadil Eisa Amani Abdulrahman Albraikan Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Muhammad Imran Babar | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 11 | Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya显示文摘The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure. | Hamza DAUD Javed Iqbal TANOLI Sardar Muhammad ASIF Muhammad QASIM Muhammad ALI Junaid KHAN Zahid Imran BHATTI Ishtiaq Ahmad Khan JADOON | 2024 | Journal of Mountain Science2024，21，1： | 0 |
| 12 | An IoT Environment Based Framework for Intelligent Intrusion Detection显示文摘Software-defined networking(SDN)represents a paradigm shift in network traffic management.It distinguishes between the data and control planes.APIs are then used to communicate between these planes.The controller is central to the management of an SDN network and is subject to security concerns.This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks.Overfitting,low accuracy,and efficient feature selection is all discussed.We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in SDN.To obtain the best and most relevant features,a feature selection technique is used.Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies.The performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s performance.Experiments show that the adopted methodology outperforms existing models. | Hamza Safwan Zeshan Iqbal Rashid Amin Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang | 2023 | Computers, Materials & Continua2023，，5： | 0 |
| 13 | Early Detection of Autism in Children Using Transfer Learning显示文摘Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice. | Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan | 2023 | Intelligent Automation & Soft Computing2023，，4： | 0 |
| 14 | Diabetes mellitus:Is Pakistan the epicenter of the next pandemic?显示文摘To the Editor,Diabetes mellitus(DM)is an endocrine disorder of chronic hyperglycemia diagnosed by fasting blood sugar levels of≥126 mg/dL or glycated hemoglobin(HbA1c)levels of≥6.5%.Type 1 DM involves a lack of insulin secretion by the pancreas,whereas the human body's resistance to insulin combined with impaired function of beta cells causes type 2 DM. | Muhammad Bilal Shahid Mahnoor Saeed Hamza Naeem Usha Kumari | 2024 | Chronic Diseases and Translational Medicine2024，10，1： | 0 |
| 15 | Regulating metalloimmunology with nanomedicine for cancer therapy显示文摘Metals are essential components of both micronutrients and macronutrients in living organisms and are involved in a variety of immune processes in the forms of free ions or protein-coupled complexes(metalloproteins).Multiple aspects of the immune system,from the structural and functional control of immune-related proteins to the cellular responses to immunotherapy,could be affected by metals.Therefore,the employment of metal for the regulation of immunity,termed as metalloimmunology,is gaining interest as a prevalent and efficacious approach to combating cancer.However,the manipulation of metalloimmunology using traditional drugs presents several challenges,including limited bioavailability,adverse effects,and a lack of targeting specificity.This review provides an overview of the latest findings in metal and metal-regulatory therapeutic agents for the treatment of cancer.Essential trace metal elements,such as iron,zinc,copper,manganese,magnesium,and calcium,as well as heavy metal drugs and their mechanisms of action,will be discussed with a particular focus on their roles in regulating the tumor-immune interplay.The latest nanotechnology employed in the administration of metal-regulatory drugs and the design concepts for tailored therapeutic interventions will be discussed.These concepts and information offer promising clinical possibilities of modulating cancer immunology by targeting metal metabolism. | Saibo Ma Lin Chen Muhammad Hamza Jing Chang Motao Zhu | 2023 | Nano Research2023，16，12： | 0 |
| 16 | Deep Learning-Based Digital Image Forgery Detection Using Transfer Learning显示文摘Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified.In the current cyber world where deepfakes have shaken the global community,confirming the legitimacy of a digital image is of great importance.With the advancements made in deep learning techniques,now we can efficiently train and develop state-of-the-art digital image forensic models.The most traditional and widely used method by researchers is convolution neural networks(CNN)for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.Therefore,in this study,a transfer learning based deep learning technique for image forgery detection is proposed.The proposed methodology consists of three modules namely;preprocessing module,convolutional module,and the classification module.By using our proposed technique,the training time is drastically reduced by utilizing the pre-trained weights.The performance of the proposed technique is evaluated by using benchmark datasets,i.e.,BOW and BOSSBase that detect five forensic types which include JPEG compression,contrast enhancement(CE),median filtering(MF),additive Gaussian noise,and resampling.We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%.The results show the superiority of the proposed system. | Emad Ul Haq Qazi Tanveer Zia Muhammad Imran Muhammad Hamza Faheem | 2023 | Intelligent Automation & Soft Computing2023，38，12： | 0 |
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