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Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

Computers, Materials & Continua 2021年第12期69卷 2917-2932页

作者： muhammad Rizwan Latif Muhamamd Attique Khan muhammad younus javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan Wah Engineering College University of WahWah Cantt47080Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton *** 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton *** 1.5 million people in Pakistan are engaged in the cotton value ***,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton *** diagnosis is not a good solution due to several factors such as high cost and unavailability of an ***,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early *** this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature *** proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further *** final recognition,a Cubic SVM approach was utilized and validated on a prepared *** the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..

关键词： Plants diseases data augmentation deep learning features fusion features selection

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Multi-Layered Deep Learning Features Fusion for Human Action Recognition

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Computers, Materials & Continua 2021年第12期69卷 4061-4075页

作者： Sadia Kiran muhammad Attique Khan muhammad younus javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius muhammad Sharif Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Mathematics Silesian University of TechnologyGliwicePoland Department of Computer Science COMSATS University IslamabadWah CampusPakistan

Human Action Recognition(HAR)is an active research topic in machine learning for the last few *** surveillance,robotics,and pedestrian detection are the main applications for action *** vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of *** this article,we proposed a new method for the use of deep learning for *** the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer *** Resnet-50 Pre-Trained Model is used as a deep learning model in this *** are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold *** selected features are finally passed to multiple classifiers for final *** are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and *** accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,*** with existing techniques has shown that the proposed method provides improved accuracy for ***,the proposed method is computationally fast based on the time of execution.

关键词： Action recognition transfer learning features fusion features selection classification

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Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

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Computers, Materials & Continua 2022年第2期70卷 2113-2130页

作者： Awais Khan muhammad Attique Khan muhammad younus javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric *** main challenges in gait recognition are the change in in-person view angle and covariant *** major covariant factors are walking while carrying a bag and walking while wearing a *** learning is a new machine learning technique that is gaining *** techniques for HGR based on deep learning are presented in the *** requirement of an efficient framework is always required for correct and quick gait *** proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this *** proposed framework consists of four primary *** the first step,the database is normalized in a video *** the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s *** that,we trained both modified models using transfer learning and extracted the *** IACO algorithm is used to improve the extracted *** is used to select the best features,which are then passed to the Cubic SVM for final *** cubic SVM employs a multiclass *** experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

关键词： Gait recognition deep learning transfer learning features optimization classification

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Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

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Computers, Materials & Continua 2022年第5期71卷 2477-2495页

作者： Amina Bibi Muhamamd Attique Khan muhammad younus javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Computer Science Department Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt

Background:In medical image analysis,the diagnosis of skin lesions remains a challenging *** lesion is a common type of skin cancer that exists *** is one of the latest technologies used for the diagnosis of skin ***:Many computerized methods have been introduced in the literature to classify skin ***,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant *** Work:In this study,a new technique is proposed based on the conventional and deep learning *** proposed framework consists of two major tasks:lesion segmentation and *** the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color ***,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding *** the lesion classification task,two pre-trained CNN models were modified and trained using transfer *** features were extracted from both models and fused using canonical correlation *** the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this *** features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final ***:The experimental process was conducted on two datasets:ISIC 2017 and *** ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification *** achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.

关键词： Skin cancer lesion segmentation deep learning features fusion classification

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Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan

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Infectious Diseases of Poverty 2015年第1期4卷 62-66页

作者： Tariq Abbas muhammad younus Sayyad Aun muhammad Section of Veterinary Preventive Medicine and Public Health University College of Veterinary and Animal SciencesThe Islamia University of BahawalpurBahawalpurPakistan Department of Pathobiology College of Veterinary and Animal SciencesJhangPakistan Department of Clinical Sciences College of Veterinary and Animal SciencesJhangPakistan

Background:Crimean Congo hemorrhagic fever(CCHF)is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in *** aim of this study was to identify spatial clusters of human cases of CCHF reported in ***:Kulldorff’s spatial scan statisitc,Anselin’s Local Moran’s I and Getis Ord Gi*tests were applied on data(*** of laboratory confirmed cases reported from each district during year 2013).Findings:The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with *** cluster comprised the following districts:Qilla Abdullah;Qilla Saifullah;Loralai,Quetta,Sibi,Chagai,and *** cluster was detected in Punjab and included Rawalpindi district and a part of ***:We provide empirical evidence of spatial clustering of human CCHF cases in the *** districts in the clusters should be given priority in surveillance,control programs,and further research.

关键词： Crimean Congo hemorrhagic fever Pakistan Spatial autocorrelation Clusters Adjusted cumulative incidence

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Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

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Computers, Materials & Continua 2022年第5期71卷 2617-2631页

作者： muhammad Usman younus Rabia Shafi Ammar Rafiq muhammad Rizwan Anjum Sharjeel Afridi Abdul Aleem Jamali Zulfiqar Ali Arain Ecole Doctorale Mathematiques InformatiqueTelecommunicationde ToulouseUniversity Paul SabatierToulouse31330France School of Electronics and Information Northwestern Polytechnical UniversityXi’an710129China Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan Department of Electronic Engineering The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Electrical Engineering Sukkur IBA UniversitySukkur65200Pakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience and Technology(QUEST)Nawabshah67450Pakistan Department of Telecommunication Engineering MUETJamshoro76060Pakistan

The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions that can be addressed to obtain the user’s Quality-of-Experience(QoE). 360-degree videos have already taken up the user’s behavior by ***, the users only focus on the part of 360-degree videos, known as aviewport. Despite the immense hype, 360-degree videos convey a loathsomeside effect about viewport prediction, making viewers feel uncomfortablebecause user viewport needs to be pre-fetched in advance. Ideally, we canminimize the bandwidth consumption if we know what the user motionin advance. Looking into the problem definition, we propose an EncoderDecoder based Long-Short Term Memory (LSTM) model to more accuratelycapture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct inputto predict the future user movement. Then, this prediction model is combinedwith a rate adaptation approach that assigns the bitrates to various tiles for360-degree video frames under a given network capacity. Hence, our proposedwork aims to facilitate improved system performance when QoE parametersare jointly optimized. Some experiments were carried out and compared withexisting work to prove the performance of the proposed model. Last but notleast, the experiments implementation of our proposed work provides highuser’s QoE than its competitors.

关键词： Encoder-decoder based lSTM 360-degree video streaming LSTM QoE viewport prediction

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Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

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Computers, Materials & Continua 2022年第4期71卷 269-285页

作者： Abdullah Shoukat muhammad Ali Mughal Saifullah younus Gondal Farhana Umer Tahir Ejaz Ashiq Hussain Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Electrical Engineering Islamia UniversityBahawalpur63100Pakistan Department of Electrical Engineering Iqra UniversityIslamabad44000Pakistan

Transmission line is a vital part of the power system that connects two major points,the generation,and the *** an efficient design,stable control,and steady operation of the power system,adequate knowledge of the transmission line parameters resistance,inductance,capacitance,and conductance is of great *** parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground *** paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission *** equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured *** parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective *** improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line *** cases are considered for parameter estimation,the first case is when the line conductance is neglected and in the second case,the conductance is considered into *** results obtained by the improved algorithm are compared with the standard version of the algorithm,firefly algorithm and artificial bee colony algorithm for 30 number of *** is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis,comparatively.

关键词： Chaos parameter estimation transmission line time-varying particle swarm optimization pi-network

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Effect of low power laser irradiation on bio-physical properties of wheat seeds

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Information Processing in Agriculture 2020年第3期7卷 456-465页

作者： Samiya Sikandar Afta Ayesha younus Institute of Soil and Environmental Sciences University of AgricultureFaisalabadPakistan Department of Civil and Environmental Engineering 209 Neungdong-roGwangjin-guSejong University SeoulSouth Korea Department of Physics and Astronomy and Graphene Research Institute Sejong UniversitySeoul 05006Korea Department of Physics University of AgricultureFaisalabadPakistan

The production of quality food is indispensable and essential to overcome its global *** order to exercise the facile route to acquire massive production,a laser irradiation treatment method was *** have investigated the effects of diode laser irradiation on wheat seeds germination,its early growth and biochemical *** variation in germination parameters,caused by laser treatment were monitored carefully.A comparison study was accomplished to examine the laser irradiation effects on treated and control *** red laser indicated an enhancement of 25%in germination percentage,4.68 cm in shoot length,and 0.64 cm in root length while green laser showed 6.25%less germination percentage than the untreated *** green laser showed 9.67 cm increase in shoot length,5.39 cm in root length as compared to untreated *** was found that the seeds which were irradiated with green laser showed an amount of superoxide dismutase(SOD),catalase(CAT)and peroxidase(POD),of 11.9,10,and 1.53 per mg of protein,respectively as compared to control ***,these investigations are promising and suitable for upcoming research on a large scale.

关键词： Wheat Diode laser Germination parameter Biochemical parameters Protein

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On Fuzzy Conformable Double Laplace Transform with Applications to Partial Differential Equations

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2163-2191页

作者： Thabet Abdeljawad Awais younus Manar A.Alqudah Usama Atta Department of Mathematics and Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Medical Research China Medical UniversityTaichung40402Taiwan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakaryia UniversityMultan60000Pakistan Department of Mathematical Sciences Faculty of SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The Laplace transformation is a very important integral transform,and it is extensively used in solving ordinary differential equations,partial differential equations,and several types of integro-differential *** purpose in this study is to introduce the notion of fuzzy double Laplace transform,fuzzy conformable double Laplace transform(FCDLT).We discuss some basic properties of *** obtain the solutions of fuzzy partial differential equations(both one-dimensional and two-dimensional cases)through the double Laplace *** demonstrate through numerical examples that our proposed method is very successful and convenient for resolving partial differential equations.

关键词： Fuzzy conformable laplace transform fuzzy double laplace transform fuzzy conformable double laplace transform fuzzy conformable partial differential equation

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Photocatalytic and Antibacterial Activities of Ag/ZnO Nanocomposities Fabricated by Co-Precipitation Method

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Acta Metallurgica Sinica(English Letters) 2014年第2期27卷 223-232页

作者： Md.Abdus Subhan M.R.Awal Tanzir Ahmed M.younus Department of Chemistry Shah Jalal University of Science and Technology

A Ag/ZnO nanocomposite has been synthesized and characterized for investigating its photocatalytic activity. The morphology and particle size of the Ag/ZnO was studied by scanning electron microscope （SEM） and the microstructure of the as-synthesized nanocomposite was confirmed by X-ray diffraction （XRD） analysis. The elemental com- position of the metal oxide was determined by energy dispersion spectrometry （EDS）. Diffuse reflectance spectra （DRS）, Photoluminescence （PL） spectra, and Fourier transform infrared spectroscopy （FTIR） were also studied for characterizing the nanocomposite. The average particle size was found to be around 20-30 nm. Photocatalytic activity of Ag/ZnO has been investigated over methyl violet （6B） dye under UV and visible light irradiation. The degradation of methyl violet （6B） dye using ZnO and Ag/ZnO was compared and found that Ag/ZnO composite is more efficient than ZnO. The rate of disappearance of dye was monitored spectrophotometrically in the maximum visible absorption wavelength and the extent of degradation was discussed in terms of Langmuir-Hinshelwood model. The Ag/ZnO composite was found capable of degrading the industrial dye effluent. Effect of H202 addition on dye degradation by the Ag/ZnO was investigated and Ag/ ZnO was found to be an effective antimicrobial agent. Reusability of Ag/ZnO catalyst was also tested.

关键词： Nanocomposite Photocatalyst Metal oxide Dye degradation Photoluminescence

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