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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 equations.Our 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 FCDLT.We obtain the solutions of fuzzy partial differential equations(both one-dimensional and two-dimensional cases)through the double Laplace approach.We 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

Semi-closed synthesis of nitrogen and oxygen Co-doped mesoporous carbon for selective aqueous oxidation

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Green Energy & Environment 2022年第1期7卷 43-52页

作者： Chen Xing Daihui Yang Yan Zhang Tian Sun Junfei Duan Hussein A.younus Shiguo Zhang College of Materials Science and Engineering Hunan Province Key Laboratory for Advanced Carbon Materials and Applied TechnologyHunan UniversityChangsha410082China School of Materials Science and Engineering Changsha University of Science and TechnologyChangsha410004China Chemistry Department Faculty of ScienceFayoum UniversityFayoum63514Egypt

Heteroatom-doped meso/micro-porous carbon materials are conventionally produced by harsh carbonization under an inert atmosphere involving specific precursors,hard/soft templates,and heteroatom-containing agents.Herein,we report a facile synthesis of N and O co-doped meso/micro-porous carbon(NOMC)by template-free carbonization of a small-molecule precursor in a semi-closed system.The semi-closed carbonizaiton process yields hydrophilic NOMCs with large surface area in a high yield.The porous structure as well as the elemental composition of NOMCs can be modulated by changing the holding time at a particular temperature.NOMCs as metal-free heterogeneous catalysts can selectively oxidize benzyl alcohol and its derivatives into aldehydes/ketones with>85%conversion in aqueous solution,which is much higher than that of the control sample obtained in tube furnace(21%conversion),mainly due to their high N content,high percentage of pyridinic N,and large surface area.The presence of O-containing moieties also helps to improve the hydrophilicity and dispersion ability of catalysts and thus facilitates the mass transfer process during aqueous oxidation.The NOMC catalysts also dispayed excellent activity for a wide range of substrates with a selectivity of>99%.

关键词： Semi-closed carbonization Nitrogen and oxygen doping Mesoporous carbon Metal-free catalyst Selective aqueous oxidation

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 distribution.For 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 importance.These 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 cable.This paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission line.The equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured data.The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function.An improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters.Two 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 account.The 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 trials.It 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

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 storm.However, 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

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 technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors.The major covariant factors are walking while carrying a bag and walking while wearing a coat.Deep learning is a new machine learning technique that is gaining popularity.Many techniques for HGR based on deep learning are presented in the literature.The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this work.The proposed framework consists of four primary steps.In the first step,the database is normalized in a video frame.In the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature.After that,we trained both modified models using transfer learning and extracted the features.The IACO algorithm is used to improve the extracted features.IACO is used to select the best features,which are then passed to the Cubic SVM for final classification.The cubic SVM employs a multiclass method.The 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

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 Pakistan.The aim of this study was to identify spatial clusters of human cases of CCHF reported in country.Methods:Kulldorff’s spatial scan statisitc,Anselin’s Local Moran’s I and Getis Ord Gi*tests were applied on data(i.***.number 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 Afghanistan.The cluster comprised the following districts:Qilla Abdullah;Qilla Saifullah;Loralai,Quetta,Sibi,Chagai,and Mastung.Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad.Conclusion:We provide empirical evidence of spatial clustering of human CCHF cases in the country.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

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 shortage.In order to exercise the facile route to acquire massive production,a laser irradiation treatment method was established.We have investigated the effects of diode laser irradiation on wheat seeds germination,its early growth and biochemical parameters.The 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 seeds.The 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 seeds.Further green laser showed 9.67 cm increase in shoot length,5.39 cm in root length as compared to untreated seeds.It 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 seeds.Thus,these investigations are promising and suitable for upcoming research on a large scale.

关键词： Wheat Diode laser Germination parameter Biochemical parameters Protein

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 task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In 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 discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During 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 issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The 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

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