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Detection and Classification of Hemorrhages in Retinal Images

Computer Systems Science & Engineering 2023年第2期44卷 1601-1616页

作者： Ghassan ahmed Ali Thamer Mitib Ahmad Al Sariera muhammad Akram Adel Sulaiman Fekry Olayah College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Studies in Computer Science University of MysoreMysoreIndia

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is theﬁrst clinically visible symptoms of *** paper presents a new technique to extract and classify the hemorrhages in fundus *** normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from *** masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newﬁlter to extract the border of vessels based on orienta-tions of vessels are *** masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is *** the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological *** are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet *** different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classiﬁer are applied to classify the candidate dark regions as either hemor-rhages or *** efﬁcacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in *** performance of the method is measured based on average sensitivity,speciﬁcity,F-score and *** results show the Linear SVM classiﬁer gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to speciﬁcity Quadratic SVM gives better result as compared to other SVMs.

关键词： Diabetic retinopathy hemorrhages adaptive thresholding support vector machine

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Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

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Computer Systems Science & Engineering 2023年第5期45卷 2125-2140页

作者： Usman Ahmad muhammad Junaid Ali Faizan ahmed Khan Arfat Ahmad Khan ArifUr Rehman Malik muhammad Ali Shahid Mohd Anul Haq Ilyas Khan Zamil SAlzamil ahmed Alhussen Bahria University Islamabad45550Pakistan COMSATS University Islamabad Islamabad45550Pakistan University of Central Punjab Lahore54000Pakistan College of Computing Khon Kaen UniversityKhon Kaen40000Thailand COMSATS University Islamabad Vehari61170Pakistan Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish ***,it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of *** solve these issues,we propose a transfer learning-based technique in which we use Efficient-Net,which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database,which is a large scale ***,prior to the activation layer,we use Global Average Pooling(GAP)instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense *** check the validity of our model,we validate our model on the validation set which achieves satisfactory ***,for the localization task,we propose an architecture that consists of localization aware block,which captures localization information for better prediction and residual connections to handle the over-fitting ***,the residual connections help the layer to combine missing information with the relevant *** addition,we use class weights and Focal Loss(FL)to handle class imbalance problems along with reducing false ***,class weights assign less weights to classes having fewer instances and large weights to classes having more number of *** the localization,the qualitative assessment shows that we achieve 57%Mean Intersection Over Union(IoU)on testing data,and the classification results show 75%precision,70%recall,78%accuracy and 74%F1-Score for 468 fish species.

关键词： Underwater species transfer learning k-nearest neighbors global average pooling efficientnet

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Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

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Computers, Materials & Continua 2023年第12期77卷 3817-3834页

作者： muhammad Aleem Raza muhammad Anwar Kashif Nisar Ag.Asri Ag.Ibrahim Usman ahmed Raza Sadiq Ali Khan Fahad Ahmad Department of Computer Science and IT Lahore Lead UniversityLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac ***,the early diagnosis of cardiac arrhythmia is one of the most challenging *** manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is ***,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG *** paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the *** proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

关键词： Arrhythmia ECG signal deep learning convolutional neural network physioNet MIT-BIH arrhythmia database

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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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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An investigation of the effects of synthesized zinc oxide nanoparticles on the properties of water-based drilling fluid

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Petroleum Research 2022年第1期7卷 131-137页

作者： muhammad Hasibul Ahasan MdFazla Alahi Alvi Nayem ahmed Md.Saiful Alam Department of Petroleum and Mining Engineering Shahjalal University of Science and TechnologySylhet3114Bangladesh

The deterioration of drilling fluid properties during deep drilling causes problems like inefficient cutting removal,fluid invasion,and shale swelling,which increases non-productive time(NPT)and *** nanoparticles(NPs)based drilling fluid are introduced to address these impediments since these particles prove their advantages in different stages of petroleum exploration and *** developments are concerned with the use of commercial zinc oxide(ZnO)NPs in inorganic KCl salt-based drilling fluid,although application of KCl in drilling fluid arouses environmental *** this study,ZnO NPs prepared in the laboratory are used in water-based drilling fluid(WBDF)to assess the effect of nanoparticles on the properties of drilling fluid at two temperature conditions of 40C and 80C.A base mud is formulated,and ZnO NPs with five different concentrations of 0.05,0.1,0.5,1.0,and 2.0 wt%are mixed with the base *** experimental analysis shows that NPs improve the rheological properties of *** the concentration of NPs was 0.1 wt%,the yield point increased by 61.54%at 40C,and when the concentration of NPs was 0.05 wt%,the yield point increased by 60%at 80***,when the concentration of NPs is 0.05 wt%,10-sec gel strengths are enhanced by 25%and 50%at 40C and 80C,*** API low-pressure low-temperature(LPLT)filter volume is not significantly affected by the addition of NPs in ***,mud cake thickness continuously reduces with the increase of NPs *** result reveals that a superior rheological improvement is achieved at low NPs concentration(0.05 wt%)at higher temperature.

关键词： Drilling fluids Zinc oxide Nanoparticles Rheology Mud cake thickness

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Molecular epidemiology, characterization of virulence factors and antibiotic resistance profile of Streptococcus agalactiae isolated from dairy farms in China and Pakistan

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Journal of Integrative Agriculture 2023年第5期22卷 1514-1528页

作者： Ambreen LEGHARI Shakeel ahmed LAKHO Faiz muhammad KHAND Khaliq ur Rehman BHUTTO Sameen Qayoom LONE muhammad Tahir ALEEM Iqra BANO muhammad Ali CHANDIO Jan muhammad SHAH LIN Hui-xing FAN Hong-jie MOE Joint International Research Laboratory of Animal Health and Food Safety College of Veterinary MedicineNanjing Agricultural UniversityNanjing 210095P.R.China Shaheed Benazir Bhutto University of Veterinary and Animal Sciences Sakrand 67210Pakistan Central Veterinary Diagnostic Laboratory Tando Jam 70050Pakistan Jiangsu Co-innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses Yangzhou UniversityYangzhou 225009P.R.China

Streptococcus agalactiae is one of the most common pathogens that cause bovine mastitis worldwide. Identifying pathogen prevalence and virulence factors is critical for developing prevention and control approaches. Herein, 1 161 milk samples from various dairy farms in China(n=558) and Pakistan(n=603) were collected between 2019–2021 and were subjected to S. agalactiae isolation. Prevalence, serotyping, virulence genes, and antibiotic-resistant genes of S. agalactiae were evaluated by PCR assay. All isolates were characterized for haemolysis, biofilm production, cytotoxicity, adhesion, and invasion on bovine mammary epithelial cells. The prevalence of S. agalactiae-induced mastitis in cattle was found to be considerably higher in Pakistan than in China. Jiangsu and Sindh provinces had the highest area-wise prevalence in China and Pakistan, respectively. Serotypes Ia and II were prevalent in both countries, whereas serotype III was found only in Pakistan. Moreover, all isolates tested positive for PI-2b gene but negative for PI-1 and PI-2a genes. All isolates harboured cfb, cylE, hylB, and fbsB virulent genes, whereas many of them lacked bibA, rib and bca. However, the absence of bac and scp genes in Chinese isolates and cspA in Pakistani isolates was noted, while spb1 and lmb were not detected in isolates of both countries. Pakistani isolates, particularly serotype Iapositive, had a considerably higher ability to produce biofilm, haemolysis, cytotoxicity, adhesion, and invasion than Chinese isolates. Most of the isolates were phenotypically resistant to tetracycline, erythromycin, and clindamycin and genotypic resistance was confirmed by the presence of ermA, ermB, tetM and tetO genes. Our study highlights the antimicrobial resistance profile and virulence-related factors contributing to the epidemiological spread of mastitis-causing S. agalactiae in China and Pakistan. The findings may facilitate future studies designed to develop improved treatment and control str

关键词： Streptococcus agalactiae mastitis epidemiology virulence characterization antibiotic resistance

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Bio-Inspired Modelling of Disease Through Delayed Strategies

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Computers, Materials & Continua 2022年第12期73卷 5717-5734页

作者： Arooj Nasir Dumitru Baleanu Ali Raza Pervez Anwar Nauman ahmed muhammad Rafiq Tahir Nawaz Cheema Baqai Medical University Karachi75340Pakistan Shalamar Medical and Dental College Lahore54000Pakistan Department of Mathematics Cankaya UniversityBalgatAnkara06530Turkey Department of Medical Research China Medical UniversityTaichung40402Taiwan Institute of Space Sciences Magurele-Bucharest077125Romania Department of Mathematics Govt.Maulana Zafar Ali Khan Graduate College WazirabadPunjab Higher Education Department(PHED)Lahore54000Pakistan Department of Biochemistry University of SialkotSialkot51310Pakistan Department of Mathematics and Statistics The University of LahoreLahore54590Pakistan Department of Mathematics Faculty of SciencesUniversity of Central PunjabLahore54000Pakistan Department of Mathematics University of GujratGujrat52000Pakistan

In 2020,the reported cases were 0.12 million in the six regions to the official report of the World Health Organization(WHO).For most children infected with leprosy,0.008629 million cases were detected under *** total infected ratio of the children population is approximately 4.4 *** to theCOVID-19 pandemic,the awareness programs implementation has been *** disease still has a threat and puts people in *** delayed modeling is critical in various allied sciences,including computational biology,computational chemistry,computational physics,and computational economics,to name a *** time delay effect in treating leprosy delayed epidemic model is *** whole population is divided into four groups:those who are susceptible,those who have been exposed,those who have been infected,and those who have been *** local and global stability of well-known conclusions like the Routh Hurwitz criterion and the Lyapunov function has been *** parameters’sensitivity is also *** analytical analysis is supported by computer results that are presented in a variety of *** proposed approach in this paper preserves equilibrium points and their stabilities,the existence and uniqueness of solutions,and the computational ease of implementation.

关键词： Leprosy disease delayed model stability analysis computer results

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Application of Zinc, Iron and Boron Enhances Productivity and Grain Biofortification of Mungbean

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Phyton-International Journal of Experimental Botany 2023年第4期92卷 983-999页

作者： muhammad Zafar Siraj ahmed muhammad Kashif Munir Nawal Zafar muhammad Saqib muhammad Aleem Sarwar Saba Iqbal Baber Ali Naveed Akhtar Basharat Ali Sadam Hussain muhammad Saeed Mohammad Khalid Al-Sadoon Aneela Gulnaz Agronomic Research Institute Ayub Agricultural Research Institute FaisalabadFaisalabad38000Pakistan Soil and Water Testing Laboratory Ayub Agricultural Research Institute FaisalabadFaisalabad38000Pakistan Department of Plant Sciences Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Agricultural Engineering Khwaja Fareed University of Engineering and TechnologyRahim Yar Khan64200Pakistan College of Agronomy Northwest A&F UniversityYangling712100China Plant Pathology Research Institute Faisalabad38000Pakistan Department of Zoology College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Biotechnology Yeungnam UniversityGyeongsan38641Korea

Deficiencies of essential vitamins,iron(Fe),and zinc(Zn)affect over one-half of the world’s population.A significant progress has been made to control micronutrient deficiencies through supplementation,but new approaches are needed,especially to reach the rural *** biofortification of pulses with Zn,Fe,and boron(B)offers a pragmatic solution to combat hidden hunger instead of food fortification and ***,it also has positive effects on crop production as ***,we conducted three separate field experiments for two consecutive years to evaluate the impact of soil and foliar application of the aforementioned nutrients on the yield and seed biofortification of *** application of Zn at 0,4.125,8.25,Fe at 0,2.5,5.0 and B at 0,0.55,1.1 kg ha−1 was done in the first,second and third experiment,*** application in these experiments was done at 0.3%Zn,0.2%Fe and 0.1%B respectively one week after flowering *** revealed that soil-applied Zn,Fe and B at 8.25,5.0 and 1.1 kg ha−1,respectively,enhanced the grain yield of mungbean;however,this increase in yield was statistically similar to that recorded with Zn,Fe and B at 4.125,2.5 and 0.55 kg ha−1,*** application of these nutrients at flower initiation significantly enhanced the Zn contents by 28%and 31%,Fe contents by 80%and 78%,while B contents by 98%and 116%over control during 2019 and 2020,*** was concluded from the results that soil application of Zn,Fe,and B enhanced the yield performance of mungbean;while significant improvements in seed Zn,Fe,and B contents were recorded with foliar application of these nutrients.

关键词： Biofortification seed biofortification mungbean iron zinc boron foliar application

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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Computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed muhammad Shoaib Waqas ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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A Hybrid Feature Selection Framework for Predicting Students Performance

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Computers, Materials & Continua 2022年第1期70卷 1893-1920页

作者： Maryam Zaffar Manzoor ahmed Hashmani Raja Habib KS Quraishi muhammad Irfan Samar Alqhtani Mohammed Hamdi High Performance Cloud Computing(HPC3) Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information Technology University of LahorePakistan Department of Process Engineering Pakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information Systems Najran University61441NajranSaudi Arabia

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha

关键词： Educational data mining feature selection hybrid feature selection

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