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| 1 | Investiga- tion into microstructures of maraging steel 250 weldments and effect of post-weld heat treatments 显示文摘 | FAWAD T BALOCH R A AHMED B | 2010 | Journal of Ma- terials Engineering and Performance2010，19，2： | 1 |
| 2 | Classification of Citrus Plant Diseases Using Deep Transfer Learning显示文摘In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques. | Muhammad Zia Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain | 2022 | Computers, Materials & Continua2022，，1： | 1 |
| 3 | Investigation into Microstructures of Maraging Steel 250 Weldments and Effect of Post-Weld Heat Treatments显示文摘 | Fawad Tariq Rasheed Ahmed Baloch Bilal Ahmed Nausheen Naz | 2010 | Journal of Materials Engineering and Performance2010，，2： | 1 |
| 4 | Evolution of microstructure and mechanical properties during quenching and tempering of ultrahigh strength 0.3C Si–Mn–Cr–Mo low alloy steel显示文摘 | Fawad Tariq Nausheen Naz Rasheed Ahmed Baloch Ashraf Ali | 2010 | Journal of Materials Science2010，，6： | 1 |
| 5 | Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks显示文摘Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM. | Muneeb Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Faisal Abdulaziz Alfouzan Nouf M.Alzahrani Jawad Ahmad | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 6 | A Secure and Lightweight Chaos Based Image Encryption Scheme显示文摘In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm.The method works on the principle of confusion and diffusion.The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes.Initially,an image(red,green,and blue components)is partitioned into blocks with an equal number of pixels.Each block is then processed with Tinkerbell Chaotic Map(TBCM)to get shuffled pixels and shuffled blocks.Composite Fractal Function(CFF)change the value of pixels of each color component(layer)to obtain a random sequence.Through the obtained random sequence,three layers of plain image are encrypted.Finally,with each encrypted layer,Brownian Particles(BP)are XORed that added an extra layer of security.The experimental tests including a number of statistical tests validated the security of the presented scheme.The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature. | Fadia Ali Khan Jameel Ahmed Fehaid Alqahtani Suliman A.Alsuhibany Fawad Ahmed Jawad Ahmad | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 7 | An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features显示文摘In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%. | Ejaz Khan Muhammad Zia Ur Rehman Fawad Ahmed Suliman A.Alsuhibany Muhammad Zulfiqar Ali Jawad Ahmad | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
| 8 | 斯特林发动机功率控制系统设计显示文摘基于Matlab的建模仿真,采用PID调节,设计一种斯特林发动机功率控制方法,对GPU-3β型斯特林发动机以改变压力的方式进行功率调节。根据20组实际数值模拟出传递函数,模拟的数据与真实数据对比符合度高于99%。输入压力值通过传递函数计算得到实际的输出功率,调节输入压力值,输出结果随之改变。结果表明运用闭环负反馈和PID调节后,随着输入压力值的变化,输出功率能按特定的规律进行变化。本设计消除了输入数据的扰动以及外界干扰对输出功率稳定的影响,使得斯特林发动机的功率控制系统具有相对较强的稳定性以及对扰动具有一定的自适应能力。 | 王鑫 黄护林 Fawad Ahmed 赵永彬 | 2022 | 太阳能学报2022，43，2： | 0 |
| 9 | Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’Response Against COVID Variants显示文摘TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. | Hassam Tahir Muhammad Shahbaz Khan Fawad Ahmed Abdullah M.Albarrak Sultan Noman Qasem Jawad Ahmad | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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