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| 1 | 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 |
| 2 | A novel symmetric 10 Gbit/s architecture with a single feeder fiber for WDM-PON based on chirp-managed laser显示文摘We propose the single feeder fiber architecture for wavelength division multiplexing passive optical network(WDM-PON)based on directly modulated chirp managed laser(CML).The downlink(DL)signal output from the laser is converted to return-to-zero(RZ)differential phase shift signal using a pulse carver.The downstream signal is reused as a carrier for the upstream using intensity modulation technique.Simulation results show the error-free performance at symmetric data rate of 10 Gbit/s per channel with negligible power penalty and improved receiver sensitivity for the uplink(UL),over 25 km standard single-mode fiber(SSMF).A low-cost and reduced circuitry network design is implemented on a single feeder fiber with the elimination of differential encoder and one external modulator. | Aftab Hussain 忻向军 Abdul Latif Ashiq Hussain 余重秀 Abid Munir Yousaf Khan Muhammad Idrees Afridi | 2012 | Optoelectronics Letters2012，8，6： | 2 |
| 3 | A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction显示文摘The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment. | Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 4 | Properties of Certain Subclasses of Analytic Functions Involving q-Poisson Distribution显示文摘By using the basic(or q)-Calculus many subclasses of analytic and univalent functions have been generalized and studied from different viewpoints and perspectives.In this paper,we aim to define certain new subclasses of an analytic function.We then give necessary and sufficient conditions for each of the defined function classes.We also study necessary and sufficient conditions for a function whose coefficients are probabilities of q-Poisson distribution.To validate our results,some known consequences are also given in the form of Remarks and Corollaries. | Bilal Khan Zhi-Guo Liu Nazar Khan Aftab Hussain Nasir Khan Muhammad Tahir | 2022 | Computer Modeling in Engineering & Sciences2022，，6： | 0 |
| 5 | Scandix pecten-veneris L.的植物素、生物学潜力及酶抑制活性研究(英文)显示文摘目的:本研究旨在测定Scandix pecten-veneris L.的植物素和评价其生物学潜力。方法:测定S.pecten-veneris提取物中植物素的含量,包括生物碱、黄酮、多酚和单宁。使用2,2-二苯基-1-苦肼基(DPPH)测定抗氧化活性;同时通过铁还原/抗氧化能力(FRAP)测定还原能力;使用琼脂扩散测定法评价对七种细菌和四种真菌菌株的抗微生物活性。同时,对脲酶、磷酸二酯酶-Ⅰ和过氧化氢酶-Ⅱ进行酶抑制研究。结论:沙门氏菌显示出适度的抗自由基活性;羟基自由基的潜能降至初始值的20%左右。S.pectenveneris多种提取物的抗氧化活性与总酚含量呈线性相关。S.pecten-veneris叶对金黄色葡萄球菌表现出最高的抑制活性;对白色念珠菌表现出到最高的抗真菌活性。植物提取物对脲酶的活性最有效;对磷酸二酯酶-Ⅰ和碳酸酐酶-Ⅱ显示出中等活性。结果表明,S.pecten-veneris具有良好的药用潜力,可用于治疗一些特定的疾病。 | Abdul WAHAB Syed Aleem JAN Abdur RAUF Zia ur REHMAN Zahid KHAN Aftab AHMED Fatima SYED Sher Zaman SAFI Hamayun KHAN Muhammad IMRAN | 2018 | Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)2018，19，2： | 0 |
| 6 | Analyzing and Enabling the Harmonious Coexistence of Heterogeneous Industrial Wireless Networks显示文摘Nowadays multiple wireless communication systems operate in industrial environments side by side.In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold.Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems(iWCS)this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN(general-purpose WLAN)used for non-real time communication.In this paper,we present a Markov chain-based performance model that described the transmission failure of iWCS due to geographical collision with gWLAN.The presented analytic model accurately determines throughput,packet transaction delay,and packet loss probability of iWCS when it is collocated with gWLAN.The results of the Markov model match more than 90%with our simulation results.Furthermore,we proposed an adaptive transmission power control technique for iWCS to overcome the potential interferences caused by the gWLAN transmissions.The simulation results show that the proposed technique significantly improves iWCS performance in terms of throughput,packet transaction,and cycle period reduction.Moreover,it enables the industrial network for the use of delay critical applications in the presence of gWLAN without affecting its performance. | Bilal Khan Danish Shehzad Numan Shafi Ga-Young Kim Muhammad Umar Aftab | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 7 | Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning显示文摘Controlling feedback control systems in continuous action spaces has always been a challenging problem.Nevertheless,reinforcement learning is mainly an area of artificial intelligence(AI)because it has been used in process control for more than a decade.However,the existing algorithms are unable to provide satisfactory results.Therefore,this research uses a reinforcement learning(RL)algorithm to manage the control system.We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient(DDPG).The actor-critic scenario using DDPG is implemented to build the RL agent.In addition,a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.The RL algorithms are robust and proficient in using trial and error to search for the best strategy.Our proposed algorithm is a deep deterministic policy gradient,in which a large amount of training data trains the agent.Once the system is trained,the agent can automatically adjust the control parameters.The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment.The performance of the proposed RL algorithm is compared with a proportional integral derivative(PID)controller and a linear quadratic regulator(LQR)controller.The simulation results of the proposed scheme are promising for the feedback control problems. | Jehangir Arshad Ayesha Khan Mariam Aftab Mujtaba Hussain Ateeq Ur Rehman Shafiq Ahmad Adel M.Al-Shayea Muhammad Shafiq | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 8 | Genome editing in cotton:challenges and opportunities显示文摘Cotton has enormous economic potential providing high-quality protein,oil,and fibre.A large increase in cotton output is necessary due to the world’s changing climate and constantly expanding human population.In the past,conventional breeding techniques were used to introduce genes into superior cotton cultivars to increase production and to improve quality.The disadvantages of traditional breeding techniques are their time-consuming,reliance on genetic differences that are already present,and considerable backcrossing.To accomplish goals in a short amount of time,contemporary plant breeding techniques,in particular modern genome editing technologies(GETs),can be used.Numerous crop improvement initiatives have made use of GETs,such as zinc-finger nucleases,transcription-activator-like effector nucleases,clustered regularly interspaced palindromic repeats(CRISPR),and CRISPR-associated proteins systems(CRISPR/Cas)-based technologies.The CRISPR/Cas system has a lot of potential because it combines three qualities that other GETs lack:simplicity,competence,and adaptability.The CRISPR/Cas mechanism can be used to improve cotton tolerance to biotic and abiotic stresses,alter gene expression,and stack genes for critical features with little possibility of segregation.The transgene clean strategy improves CRISPR acceptability addressing regulatory issues associated with the genetically modified organisms(GMOs).The research opportunities for using the CRISPR/Cas system to address biotic and abiotic stresses,fibre quality,plant architecture and blooming,epigenetic changes,and gene stacking for commercially significant traits are highlighted in this article.Furthermore,challenges to use of CRISPR technology in cotton and its potential for the future are covered in detail. | KHAN Zulqurnain KHAN Sultan Habibullah AHMED Aftab IQBAL Muhammad Umar MUBARIK Muhammad Salman GHOURI Muhammad Zubair AHMAD Furqan YASEEN Saba ALI Zulfiqar KHAN Asif Ali AZHAR Muhammad Tehseen | 2023 | Journal of Cotton Research2023，6，1： | 0 |
| 9 | Impact of Rayleigh backscattering on single/dual feeder fiber WDM-PON architectures based on array waveguide gratings显示文摘The performance of colorless wavelength- division multiplexing passive optical network (WDM- PON) systems suffers from transmission impairments due to Rayleigh backscattering (RB). A single feeder fiber colorless WDM-PON architecture was modeled, simulated and analyzed at 25 km distance that sustained the noise induced by RB. We analytically compared the performances between single feeder and dual feeder WDM-PON architectures based on array waveguide gratings (AWGs). For single feeder WDM-PON, the high extinction ratios in both return-to-zeros (RZ)-shaped differential phase shift keying (DPSK) downstream and intensity remodulated upstream data signals helped to increase the tolerance to the noise induced by RB. However, a cost effective colorless system in dual feeder WDM-PON architecture was achieved without any optical amplification and dispersion compensation, low power penalty. These results illustrate that single feeder fiber architecture was cost effective in terms of deployment having a power penalty, while dual feeder fiber had lower power penalty thereby with better performance. Simulation results show that downstream and upstream signals achieved error-free performance at 10-Gbps with negligible penalty and enhanced tolerance to the noise induced by RB over 25 km single mode fiber. | Muhammad Idrees AFRIDI Jie ZHANG Yousaf KHAN Jiawei HAN Aftab HUSSEIN Shahab AHMAD | 2013 | Frontiers of Optoelectronics2013，6，1： | 0 |
| 10 | Joint Channel and Multi-User Detection Empowered with Machine Learning显示文摘The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate. | Mohammad Sh.Daoud Areej Fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid Shabib Aftab | 2022 | Computers, Materials & Continua2022，，1： | 0 |
| 11 | Multi Sensor-Based Implicit User Identification显示文摘Smartphones have ubiquitously integrated into our home and work environments,however,users normally rely on explicit but inefficient identification processes in a controlled environment.Therefore,when a device is stolen,a thief can have access to the owner’s personal information and services against the stored passwords.As a result of this potential scenario,this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors.A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features,then further optimized using a non-linear unsupervised feature selection method.The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely.Different classifiers are adopted to achieve accurate legitimate user identification.Extensive experiments on a group of 16 individuals in an indoor environment show the effectiveness of the proposed solution:with 5 to 70 samples per window,KNN and bagging classifiers achieve 87–99%accuracy,82–98%for ELM,and 81–94%for SVM.The proposed pipeline achieves a 100%true positive and 0%false-negative rate for almost all classifiers. | Muhammad Ahmad Rana Aamir Raza Manuel Mazzara Salvatore Distefano Ali Kashif Bashir Adil Khan Muhammad Shahzad Sarfraz Muhammad Umar Aftab | 2021 | Computers, Materials & Continua2021，，8： | 0 |
| 12 | Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System显示文摘The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%. | Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 13 | Atypical focal nodular hyperplasia of the liver显示文摘BACKGROUND:Focal nodular hyperplasia,a benign hepatic tumor,is usually asymptomatic.However,rarely the entity can cause symptoms,mandating intervention. METHOD:We present a case of focal nodular hyperplasia of the liver,which caused a considerable diagnostic dilemma due to its atypical presentation. RESULTS:A 29-year-old woman presented with a 15-year history of a progressively increasing mass in the right upper quadrant which was associated with pain and emesis. Examination showed a firm,mobile mass palpable below the right subcostal margin.A computed tomography scan of the abdomen showed an exophytic mass arising from hepatic segments III and IVb.Trucut biopsy of the hepatic mass was equivocal.Angiography showed a vascular tumor that was supplied by a tortuous branch of the proper hepatic artery. Surgical intervention for removal of the mass was undertaken. Intra-operatively,two large discrete tumors were found and completely resected.Histopathological examination showed features consistent with focal nodular hyperplasia. CONCLUSION:This description of an unusual case of focal nodular hyperplasia of the liver highlights the point that the diagnosis of otherwise benign hepatic tumors may be difficult despite extensive work-up in some cases. | Muhammad Rizwan Khan Taimur Saleem Tanveer Ul Haq Kanwal Aftab | 2011 | Hepatobiliary & Pancreatic Diseases International2011，10，1： | 0 |
| 14 | Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction显示文摘Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of medication.Emerging technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular disease.Cloud computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample data.ML is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and prediction.Accordingly,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms.Our recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve Bayes.Real-time patient data were extracted using the fuzzy-based model stored in the cloud. | Munir Ahmad Majed Alfayad Shabib Aftab Muhammad Adnan Khan Areej Fatima Bilal Shoaib Mohammad Sh.Daoud Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 15 | Drug-Drug Interaction Studies of Levocetirizine with Atenolol显示文摘 | Shafaque Mehboob Muhammad Azhar Mughal Khalid Aftab MoonaMehboob Khan Najma Sultana Syed Arayne | 2017 | Journal of Pharmacy and Pharmacology2017，5，3： | 0 |
| 16 | Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion显示文摘Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes. | Shabib Aftab Saad Alanazi Munir Ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，7： | 0 |
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