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| 1 | Diabetes mellitus and cognitive impairments显示文摘There is strong evidence that diabetes mellitus increases the risk of cognitive impairment and dementia. Insulin signaling dysregulation and small vessel disease in the base of diabetes may be important contributing factors in Alzheimer's disease and vascular dementia pathogenesis, respectively. Optimal glycemic control in type 1 diabetes and identification of diabetic risk factors and prophylactic approach in type 2 diabetes are very important in the prevention of cognitive complications. In addition, hypoglycemic attacks in children and elderly should be avoided. Anti-diabetic medications especially Insulin may have a role in the management of cognitive dysfunction and dementia but further investigation is needed to validate these findings. | Elham Saedi Mohammad Reza Gheini Firoozeh Faiz Mohammad Ali Arami | 2016 | World Journal of Diabetes2016，7，17： | 23 |
| 2 | Indian Medicinal Plants: A Potential Source for Anticandidal Drugs显示文摘 | Zafar Mehmood Iqbal Ahmad Faiz Mohammad Shamim Ahmad | 1999 | Pharmaceutical Biology1999，，3： | 1 |
| 3 | Screening of some Indian medicinal plants for their antimicrobial properties显示文摘 | Iqbal Ahmad Zafar Mehmood Faiz Mohammad | 1998 | Journal of Ethnopharmacology1998，，2： | 1 |
| 4 | Inflammation,insulin resistance and carotid IMT in first degree relatives of north Indian type 2 diabetic subjects显示文摘 | Jamal A Faiz A Mohammad AS | 2006 | Diabetes Research and Clinical Practice2006，73，： | 1 |
| 5 | 显示文摘 | Amir Al-Ahmed Faiz Mohammad M Zaki Ab Rahman | 2004 | Synth Met2004，144，： | 1 |
| 6 | Preparation, FTIR Spectroscopic characterization and isothermal stability of differently doped fibrous conducting polymers based on polyaniline and nylon-6,6 显示文摘 | MOHD Khalid FAIZ Mohammad | 2009 | Synthetic Metals2009，，159： | 1 |
| 7 | Sensorless Direct Torque Control of Induction Motors Used in Electric Vehicle 显示文摘 | Jawad Faiz Mohammad Bagher Bannae Sharifian Ali Keyhani | 2003 | IEEE Transactions on Energy Conversion2003，18，1： | 1 |
| 8 | Preparation,FTIR spectroscopic characterization and isothermal stability of differently doped fibrous conducting polymers based on polyaniline and nylon-6,6显示文摘 | MOHD Khalid FAIZ Mohammad | 2009 | Synthetic Metals2009，，159： | 1 |
| 9 | Preparation,FTIR spectroscopic characterization and isothermal stability of differently doped fibrous conducting polymers based on polyaniline and nylon-6,6显示文摘 | MOHD Khalid FAIZ Mohammad | | 0，，159： | 1 |
| 10 | Inflammation, insulin resistance and carotid IMT in first degree relatives of north Indian type 2 diabetic subjects显示文摘 | Jamal Ahmad Faiz Ahmed Mohammad A. Siddiqui Basharat Hameed Ibne Ahmad | 2006 | Diabetes Research and Clinical Practice2006，，2： | 1 |
| 11 | Composites of polyaniline and cellulose acetate: preparation, characterization, thermo-oxidative degradation and stabilityin terms of DC electrical conductivity retention 显示文摘 | Amir Al-Ahmed Faiz Mohammad Zaki M | 2004 | Synthetie Met2004，144，1： | 1 |
| 12 | Situs in-versus totalis with perforated duodenal ulcer:a case report 显示文摘 | Tayeb Mohammad Khan Faiz Mohammad Rauf Fozia | 2011 | JMed Case Rep2011，5，： | 1 |
| 13 | Identification of Anomaly Scenes in Videos Using Graph Neural Networks显示文摘Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques. | Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 14 | Electroanalytical studies on electrically conductive polyaniline: Nylon-6,6 composite film显示文摘 | Mohd. Khalid Atika Khatoon Faiz Mohammad | 2009 | Journal of Chemistry and Chemical Engineering2009，3，10： | 0 |
| 15 | Performance of Object Classification Using Zernike Moment显示文摘Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems. | Ariffuddin Joret Mohammad Faiz Liew Abdullah Muhammad Suhaimi Sulong Asmarashid Ponniran Siti Zuraidah Zainudin | 2014 | Journal of Electronic Science and Technology2014，12，1： | 0 |
| 16 | Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network显示文摘Fungal disease affects more than a billion people worldwide,resulting in different types of fungus diseases facing life-threatening infections.The outer layer of your body is called the integumentary system.Your skin,hair,nails,and glands are all part of it.These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun.The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect.Heat,light,damage,and illness are all protected by it.Fungi-caused infections are found in almost every part of the natural world.When an invasive fungus takes over a body region and overwhelms the immune system,it causes fungal infections in people.Another primary goal of this study was to create a Convolutional Neural Network(CNN)-based technique for detecting and classifying various types of fungal diseases.There are numerous fungal illnesses,but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis(IFDD)system of Candidiasis and Tinea Infections.This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings.To identify and categorize fungal infections,deep machine learning techniques are utilized.A CNN architecture was created,and it produced a promising outcome to improve the proposed system accuracy.The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards.Our CNN-Based can detect fungal diseases through medical images;earmarked IFDD system has a predictive performance of 99.6%accuracy. | Tahir Alyas Khalid Alissa Abdul Salam Mohammad Shazia Asif Tauqeer Faiz Gulzar Ahmed | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 17 | Relationship Population Density of Aquatic Sediment Macrozoobenthos to River Water Quality Parameters: Case Study of Upstream Citarum River in Bandung Regency显示文摘 | Barti Setiani Muntalif Nurul Chasanah Mohammad Faiz Faza | 2016 | Journal of Environmental Science and Engineering(A)2016，5，3： | 0 |
| 18 | An accurate retransmission timeout estimator for content-centric networking based on the Jacobson algorithm显示文摘Accurately estimating of Retransmission TimeOut (RTO) in Content-Centric Networking (CCN) is crucial for efficient rate control in end nodes and effective interface ranking in intermediate routers. Toward this end, the Jacobson algorithm, which is an Exponentially Weighted Moving Average (EWMA) on the Round Trip Time (RTT) of previous packets, is a promising scheme. Assigning the lower bound to RTO, determining how an EWMA rapidly adapts to changes, and setting the multiplier of variance RTT have the most impact on the accuracy of this estimator for which several evaluations have been performed to set them in Transmission Control Protocol/Internet Protocol (TCP/IP) networks. However, the performance of this estimator in CCN has not been explored yet, despite CCN having a significant architectural difference with TCP/IP networks. In this study, two new metrics for assessing the performance of RTO estimators in CCN are defined and the performance of the Jacobson algorithm in CCN is evaluated. This evaluation is performed by varying the minimum RTO, EWMA parameters, and multiplier of variance RTT against different content popularity distribution gains. The obtained results are used to reconsider the Jacobson algorithm for accurately estimating RTO in CCN. Comparing the performance of the reconsidered Jacobson estimator with the existing solutions shows that it can estimate RTO simply and more accurately without any additional information or computation overhead. | Mortaza Nikzad Kamal Jamshidi Ali Bohlooli Faiz Mohammad Faqiry | 2022 | Digital Communications and Networks2022，8，6： | 0 |
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