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| 1 | A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks显示文摘Power systems are being transformed to enhance the sustainability.This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles(EVs).Large-scale integration of EVs into residential distribution networks(RDNs)is an evolving issue of paramount significance for utility operators.Unbalanced voltages prevent effective and reliable operation of RDNs.Diversified EV loads require a stochastic approach to predict EVs charging demand,consequently,a probabilistic model is developed to account several realistic aspects comprising charging time,battery capacity,driving mileage,state-of-charge,traveling frequency,charging power,and time-of-use mechanism under peak and off-peak charging strategies.An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern.The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs.The equipment capability of RDNs must be evaluated to determine the potential overloads.Performance specifications of RDNs including voltage unbalance factor,voltage behavior,domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels.Moreover,the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network. | Salman HABIB Muhammad Mansoor KHAN Farukh ABBAS Muhammad NUMAN Yaqoob ALI Houjun TANG Xuhui YAN | 2020 | Frontiers in Energy2020，14，2： | 3 |
| 2 | Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches显示文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels. | Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，5： | 2 |
| 3 | Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine显示文摘An epidemic is a quick and widespread disease that threatens many lives and damages the economy.The epidemic lifetime should be accurate so that timely and remedial steps are determined.These include the closing of borders schools,suspension of community and commuting services.The forecast of an outbreak effectively is a very necessary but difficult task.A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available.This work proposes and examines a prediction model based on a deep extreme learning machine(DELM).This methodology is used to carry out an experiment based on the recent Wuhan coronavirus outbreak.An optimized prediction model that has been developed,namely DELM,is demonstrated to be able to make a prediction that is fairly best.The results show that the new methodology is useful in developing an appropriate forecast when the samples are far from abundant during the critical period of the disease.During the investigation,it is shown that the proposed approach has the highest accuracy rate of 97.59%with 70%of training,30%of test and validation.Simulation results validate the prediction effectiveness of the proposed scheme. | Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed AAl Ghamdi Abdur Rehman | 2020 | Computers, Materials & Continua2020，，9： | 1 |
| 4 | 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 |
| 5 | Effects of bismuth on structural and dielectric properties of cobalt-cadmium spinel ferrites fabricated via micro-emulsion route显示文摘Spinel ferrites have a significant role in high-tech applications.In the present work nano-crystalline ferrites having general formula Co0.5Cd0.5BixFe2-xO4 with(x=0.0,0.05,0.1,0.15,0.2,and 0.25)are synthesized via micro-emulsion route.Powder x-ray diffraction(XRD)studies discover the FCC spinel structure.Crystalline size is calculated in a range of 11 nm-15 nm.Lattice parameter calculations are reduced due to its substitution which leads to the exchange of large ionic radius of Fe^3+for small ionic radius of Bi^3+.The x-ray density is analyzed to increase with doping.Fourier transform infrared spectroscopy(FTIR)is performed to analyze absorption band spectra.The two absorption bands are observed in a range of 400 cm^-1-600 cm^-1,and they are the characteristic feature of spinel structure.Thermo-gravimetric analysis(TGA)reveals the total weight loss of nearly 1.98%.Dielectric analysis is carried out by impedance analyzer in a frequency span from 1 MHz to 3 GHz by using the Maxwell Wagner model.Dielectric studies reveal the decrease of dielectric parameters.The alternating current(AC)conductivity exhibits a plane behavior in a low frequency range and it increases with the applied frequency increasing.This is attributed to the grain effects in a high frequency range or may be due to the reduction of porosity.Real and imaginary part of impedance show the decreasing trend which corresponds to the grain boundary action.The imaginary modulus shows the occurrence of peak that helps to understand the interfacial polarization.Cole-Cole graph shows a single semicircle which confirms that the conduction mechanism is due to the grain boundaries at low frequency.Dielectric studies reveal the applicability of these ferrites in high frequency equipment,microwave applications,high storage media,and semiconductor devices. | Furhaj Ahmed Sheikh Muhammad Khalid Muhammad Shahzad Shifa H M Noor ul Huda Khan Asghar Sameen Aslam Ayesha Perveen Jalil ur Rehman Muhammad Azhar Khan Zaheer Abbas Gilani | 2019 | Chinese Physics B2019，28，8： | 1 |
| 6 | Alzheimer Disease Detection Empowered with Transfer Learning显示文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. | Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 7 | Clear P-wave arrival of weak events and automatic onset determination using wavelet filter banks显示文摘 | Ali Gamal Hafez Muhammad Tahir Abbas Khan Tohru Kohda | 2009 | Digital Signal Processing2009，，3： | 1 |
| 8 | Anticoccidial activity of herbal complex in broiler chickens challenged with Eimeria tenella显示文摘 | MUHAMMAD A. ZAMAN ZAFAR IQBAL RAO Z. ABBAS MUHAMMAD N. KHAN | 2011 | Parasitology2011，，2： | 1 |
| 9 | Arsenic fractionation in sediments of different origins using BCR sequential and single extraction methods显示文摘 | Jameel Ahmed Baig Tasneem Gul Kazi Muhammad Balal Arain Abdul Qadir Shah Raja Adil Sarfraz Hassan Imran Afridi Ghulam Abbas Kandhro Muhammad Khan Jamali Sumaira Khan | 2009 | Journal of Hazardous Materials2009，，1： | 1 |
| 10 | Contemporary Trends in Power Electronics Converters for Charging Solutions of Electric Vehicles显示文摘Electrifying the transport sector requires new possibilities for power electronics converters to attain reliable and efficient charging solutions for electric vehicles(EVs).With the continuous development in power electronics converters,the desire to reduce gasoline consumption and to increase the battery capacity for more electric range is achievable for EVs in the near future.The main interface between the power network and EV battery system is a power electronics converter,therefore,there is a considerable need of new power converters with low cost and high reliability for the advance charging mechanism of EVs.The rapid growth in power converter topologies brings substantial opportunities in EV charging process.In view of this fact,this paper investigates the significant aspects,current progress,and challenges associated with several power converters to suggest further improvements in charging systems of EVs.In particular,an extensive analysis of front-end as well as back-end converter configurations is presented.Moreover,the comparative properties of resonant converter topologies along with other DCDC converters are discussed in detail.Additionally,isolated,and non-isolated topologies with soft switching techniques are classified and rigorously analyzed with a view to their respective issues and benefits.It is foreseen that this paper would be a valuable addition and a worthy source of information for researchers exploring the area of power converter topologies for charging solutions of EVs. | Salman Habib Muhammad Mansoor Khan Farukh Abbas Abdar Ali Muhammad Talib Faiz Farheen Ehsan Houjun Tang | 2020 | CSEE Journal of Power and Energy Systems2020，6，4： | 1 |
| 11 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 12 | Experimental and DFT Studies of Au Deposition Over WO_(3)/g-C_(3)N_(4) Z-Scheme Heterojunction显示文摘A typical Z-scheme system is composed of two photocatalysts which generate two sets of charge carriers and split water into H2 and O2 at different locations.Scientists are struggling to enhance the efficiencies of these systems by maximizing their light absorption,engineering more stable redox couples,and discovering new O2 and H2 evolutions co-catalysts.In this work,Au decorated WO3/g-C3N4 Z-scheme nanocomposites are fabricated via wet-chemical and photo-deposition methods.The nanocomposites are utilized in photocatalysis for H2 production and 2,4-dichlorophenol(2,4-DCP)degradation.It is investigated that the optimized 4Au/6%WO3/CN nanocomposite is highly efficient for production of 69.9 and 307.3μmol h−1 g−1 H2 gas,respectively,under visible-light(λ>420 nm)and UV–visible illumination.Further,the fabricated 4Au/6%WO3/CN nanocomposite is significant(i.e.,100%degradation in 2 h)for 2,4-DCP degradation under visible light and highly stable in photocatalysis.A significant 4.17%quantum efficiency is recorded for H2 production at wavelength 420 nm.This enhanced performance is attributed to the improved charge separation and the surface plasmon resonance effect of Au nanoparticles.Solid-state density functional theory simulations are performed to countercheck and validate our experimental data.Positive surface formation energy,high charge transfer,and strong non-bonding interaction via electrostatic forces confirm the stability of 4Au/6%WO3/CN interface. | Muhammad Humayun Habib Ullah Junhao Cao Wenbo Pi Yang Yuan Sher Ali Asif Ali Tahir Pang Yue Abbas Khan Zhiping Zheng Qiuyun Fu Wei Luo | 2020 | Nano-Micro Letters2020，12，1： | 1 |
| 13 | An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration显示文摘The scope of the 5G network is not only limited to the enhancements in the form of the quality of service(QoS),but it also includes a wide range of services with various requirements.Besides this,many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning.However,the management of multiple services over heterogeneous platforms is a complex task.Each platform and service have various requirements to be handled by domain experts.Still,if the next-generation network management is dependent on manual updates,it will become impossible to provide seamless service provisioning in runtime.Since the traffic for a particular type of service varies significantly over time,automatic provisioning of resources and orchestration in runtime need to be integrated.Besides,with the increase in the number of devices,amount,and variety of traffic,the management of resources with optimization becomes a challenging task.To this end,this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects,including automation,resource management and service assurance.The solution consists of an intent-based system that automaticallymanages different orchestrators,and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts.The proposed systemconsiders handling the scalability of resources in runtime by usingMachine Learning(ML)to automate and optimize service resource utilization. | Talha Ahmed Khan Khizar Abbas Afaq Muhammad Wang-Cheol Song | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 14 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 15 | A novel variant of GALC in a familial case of Krabbe disease:Insights from structural bioinformatics and molecular dynamics simulation显示文摘Krabbe disease or globoid cell leukodystrophy(GLD;MIM#245200)is a rare and fatal lysosomal storage disease with an autosomal recessive mode of inheritance that results from the deficiency of galactocerebrosidase(GALC;E.C.3.2.1.46),a lysosomal enzyme encoded by the GALC gene.1 GALC breaks down galactosylceramide,a cerebroside located mainly in the myelin sheath.Defects in GALC cause the accumulation of a cytotoxic metabolite,galactosylsphingosine or psychosine,which can be toxic to oligodendrocytes and Schwann cells.2 The failure to digest galactosylceramide triggers the formation of multi-nucleated globoid cells,causing severe demyelination,axonopathy,and neuronal death.3 The reported frequency of Krabbe disease is 1 in 100,000 live births with symptoms including irritability,loss of motor ability,spasticity,ataxia,visual dysfunction,seizures,andcognitive impairment. | Ikram Ullah Muhammad Waqas Muhammad Ilyas Sobia Ahsan Halim Akmal Ahmad Natalia Dominik Wahid Ullah Muhammad Abbas Muhammad Aamir Henry Houlden Stephanie Efthymiou Ajmal Khan Ahmed Al-Harrasi | 2023 | Genes & Diseases2023，10，6： | 0 |
| 16 | A Fused Machine Learning Approach for Intrusion Detection System显示文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection. | Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 17 | Personality Detection Using Context Based Emotions in Cognitive Agents显示文摘Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction. | Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad Saad Alanazi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 18 | Call Census,Habitat Suitability Modeling,and Local Communities’Perceptions for the Conservation of a Globally Threatened Avian Flagship Species显示文摘The Western tragopan(Tragopan melanocephalus)is recorded in the IUCN Red List of 2017 as a vulnerable bird species in Pakistan.This study provides valuable information for the conservation of Western tragopan,which is a globally threatened avian flagship species in Pakistan.This study was conducted to investigate and resolve the conservation challenges surrounding the species in two major habitat zones-Salkhala Game Reserve and Machiara National Park.The study was implemented in May-June 2020 for population density using call count data.Also,questionnaire was used for local residents’perceptions and habitat suitability modeling map was generated using the MaxEnt model based on previously recorded occurrence points as well as recorded in the survey.A total of 26 western Tragopans were identified by call count during the sampling period.Moreover,about 77.3%cited Western Tragopan they more likely to hunt while the remaining 22.6%locals recorded other pheasant.About 45.3%of this hunting for fun but only 22%for economic values and only 3%of people consider hunting as a part of their culture.Results of modeling habitat suitability of the Western tragopan showed that the species suitable habitats are small and patchy in Pakistan.We found that the Normalized Difference Vegetation Index(NDVI)with 40.6 percent contribution was the most important variable in shaping the species distribution.Our model identified some new suitable patches which can be the target of future field monitoring for finding new populations of the species and future conservation planning. | Abid Ali Iftikhar Uz Zaman Abbas Khan Masoud Yousefi Zahid Ali Muhammad Numan Khan | 2022 | Journal of Zoological Research2022，4，1： | 0 |
| 19 | Modelling Intelligent Driving Behaviour Using Machine Learning显示文摘In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive intelligence.External skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction time.Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments.The parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive skill.Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations.Environmental changes prompt the parameter values to change,a process which continues unless and until all processes are completed.This paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour patterns.This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another.These patterns constitute the outcome of different modules that collaborate to generate appropriate values.In this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied. | Qura-Tul-Ain Khan Sagheer Abbas Muhammad Adnan Khan Areej Fatima Saad Alanazi Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 20 | Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network显示文摘Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3. | Gulzar Ahmad Saad Alanazi Madallah Alruwaili Fahad Ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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