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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi muhammad rizwan saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

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Computers, Materials & Continua 2023年第4期75卷 1863-1881页

作者： Hong-Hu Chu muhammad rizwan saeed Javed Rashid muhammad Tahir Mehmood Israr Ahmad Rao Sohail Iqbal Ghulam Ali College of Civil Engineering Hunan UniversityChangsha410082China Department of CS University of OkaraOkara56310Pakistan Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Department of CS Government College UniversityFaisalabad38000Pakistan Department of Automation Science Beihang UniversityBeijing100191China

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance *** the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani *** proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

关键词： Road cracks and potholes CNN smart cities pothole crack detection decision support system

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Wear and Friction Properties of Electrodeposited Ni-Based Coatings Subject to Nano-enhanced Lubricant and Composite Coating

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Acta Metallurgica Sinica(English Letters) 2016年第10期29卷 902-910页

作者： rizwan Bajwa Zulfiqar Khan H.Nazir Vivek Chacko Adil saeed NanoCorr Energy and Modelling Research GroupBournemouth University

This paper presents research findings on the tribological performance of electrodeposited coatings subject to nano-lubricants with the addition of nano-Al2O3 and graphene and Ni/nano-Al2O3 composite coatings. Electrodeposited coatings were produced by using a pulse electrodeposition method. Tribological experiments were conducted by using a linear reciprocating ball on fiat sliding tribometer. Experimental results confirmed that the wear and friction resistance properties were significantly enhanced by doping of nano-effects in the lubricating oil and composite coating. The addition of Al2O3 nanoparticles in the lubricating oil showed the best tribological properties, followed by Ni-Al2O3 composite coatings and nano-oil with graphene. The surface morphology and microstructure of electrodeposited coatings were examined by scanning electron microscopy, energy-dispersive spectroscopy and X-ray diffraction. The wear mechanisms of these coatings subjected to tribological testing were investigated by post-test surface analyses. This research provides a novel approach to design durable nano-coatings for tribological applications in various industries such as automotive, aerospace, locomotive and renewable energy technologies.

关键词： Tribology Nanoparticles Graphene Nano-additives Nano-coatings

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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Computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub muhammad Idrees Zain Ali Kashif rizwan Abdullah saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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Genetic variability predicting breeding potential of upland cotton(Gossypium hirsutum L.)for high temperature tolerance

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Journal of Cotton Research 2023年第2期6卷 81-97页

作者： FAROOQ Amjad SHAKEEL Amir saeed Asif FAROOQ Jehanzeb rizwan muhammad CHATTHA Waqas Shafqat SARWAR Ghulam RAMZAN Yasir Cotton Research Station Ayub Agricultural Research InstituteFaisalabad 38000Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38040Pakistan

Background High temperature stress at peak flowering stage of cotton is a major hindrance for crop *** study aimed to increase genetic divergence regarding heat tolerance in newly developed cultivars and *** cotton genotypes and 40 F1(hybrids)were tested under field conditions following the treatments,viz.,high temperature stress and control at peak flowering stage in August and October under April and June sowing,*** The mean squares revealed significant differences among genotypes,treatments,genotype×treatment for relative cell injury,chlorophyll contents,canopy temperature,boll retention and seed cotton yield per *** genetic diversity among 50 genotypes was analyzed through cluster analysis and heat susceptibility index(HSI).The heat tolerant genotypes including FH-Noor,NIAB-545,FH-466,FH-Lalazar,FH-458,NIAB-878,IR-NIBGE-8,Weal-AGShahkar,and heat sensitive,i.e.,CIM-602,Silky-3,FH-326,SLH-12 and FH-442 were hybridized in line×tester fashion to produce F1 *** breeding materials’populations(40 F1)revealed higher specific combining ability variances along with dominance variances,decided the non-additive type gene action for all the *** best general combining ability effects for most of the traits were displayed by the lines,i.e.,FH-Lalazar,NIAB-878 along with testers FH-326 and *** combining ability effects and better-parent heterosis were showed by the crosses,viz.,FH-Lalazar×Silky-3,FH-Lalazar×FH-326,NIAB-878×Silky-3,and NIAB-878×FH-326 for seed cotton yield and yield contributing traits under high temperature *** Heterosis breeding should be carried out in the presence of non-additive type gene action for all the studied *** best combiner parents with better-parent heterosis may be used in crossing program to develop high yielding cultivars,and hybrids for high temperature stress tolerance.

关键词： High temperature Upland cotton Peak flowering Heterosis Gene action Combining ability

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Fentanyl and xylazine crisis:Crafting coherent strategies for opioid overdose prevention

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World Journal of Psychiatry 2024年第6期14卷 760-766页

作者： Lakshit Jain Jasleen Kaur Shahana Ayub Danya Ansari rizwan Ahmed Abdul Qadir Dada saeed Ahmed Department of Psychiatry University of ConnecticutFarmingtonCT 06032United States Addiction Services Division Connecticut Valley HospitalMiddletownCT 06457United States Department of Psychiatry Institute of LivingHartfordCT 06102United States Department of Medicine Islamabad Medical and Dental CollegeIslamabad 44000Pakistan Department of Medicine Liaquat College of Medicine and DentistryKarachi 75290Pakistan Department of Medicine Trinity School of MedicineRoswellGA 30075United States Addiction Services and Dual Diagnosis Unit Saint Francis Hospital and Medical CenterHartfordCT 06105United States

The United States is in the throes of a severe opioid overdose epidemic,primarily fueled by the pervasive use of fentanyl and the emerging threat of xylazine,a veterinary sedative often mixed with *** high potency and long duration of fentanyl is compounded by the added risks from xylazine,heightening the lethal danger faced by opioid *** such as enhanced surveillance,public awareness campaigns,and the distribution of fentanylxylazine test kits,and naloxone have been undertaken to mitigate this ***-related overdose deaths persist despite these efforts,partly due to inconsistent policies across states and resistance towards adopting harm reduction strategies.A multifaceted approach is imperative in effectively combating the opioid overdose *** approach should include expansion of treatment access,broadening the availability of medications for opioid use disorder,implementation of harm reduction strategies,and enaction of legislative reforms and diminishing stigma associated with opioid use disorder.

关键词： Fentanyl Xylazine Opioid overdose Epidemic Opioid use disorder Buprenorphine Medications for opioid use disorder

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