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Cross-sectional prevalence and pattern of non-anaemia severe malaria among 2–10 year olds in Sokoto in Northwestern Nigeria

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Frontiers of Medicine 2022年第6期16卷 969-974页

作者： usman Nasir Nakakana Ridwan Muhammad Jega Aliyu Mamman Nauzo Yahya Mohammed Ismaila Mohammed ahmed Medical Research Council The Gambia Unit at London School of Hygiene and Tropical MedicineFajara220The Gambia Department of Paediatrics Usmanu Danfodiyo University Teaching HospitalSokoto234Nigeria Department of Paediatrics Federal Medical CentreBirnin Kebbi234Nigeria Department of Microbiology Usmanu Danfodiyo UniversitySokoto234Nigeria Department of Community Medicine Usmanu Danfodiyo University Teaching HospitalSokoto234Nigeria

Malaria is highly prevalent in Nigeria and accounts for approximately 40%of global malaria ***,most reports on severe malaria in Nigeria are from hospital-based studies without accurate information from communities;thus,malaria-related deaths in the community are left *** study aimed to describe the prevalence and pattern of severe malaria in a community in Northwestern Nigeria.A cross-sectional study was conducted among 2–10-year-old children in Sokoto,in August and December 2016,to determine the endemicity of malaria based on Plasmodium falciparum prevalence rate(PfPR2-10)and to describe the disease *** malaria was diagnosed according to the World Health Organisation *** were described using Stata version *** prevalence of non-anaemia severe malaria was higher than expected(2.6%),considering the endemicity pattern which was mesoendemic based on a PfPR2-10 of 34.8%.The mean age of children with severe malaria was 3.73 years,and the male—female ratio was 2:***,54.0%of the patients had hyperparasitaemia.A relatively high prevalence of non-anaemia severe malaria was found in *** finding suggests the need to identify and treat cases in the community using modifications of current strategies,particularly seasonal malaria chemoprophylaxis.

关键词： severe malaria nigeria malaria mortality PfPR2-10 intermediate malaria transmission

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad usman Ashraf ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

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Computers, Materials & Continua 2022年第11期73卷 2555-2576页

作者： Muhammad Naeem Akbar Farhan Riaz ahmed Bilal Awan Muhammad Attique Khan usman Tariq Saad Rehman National University of Sciences and Technology(NUST) Islamabad46000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj 11942Saudi Arabia

Human Action Recognition(HAR)is a current research topic in the field of computer vision that is based on an important application known as video *** in computer vision have introduced various intelligent methods based on deep learning and machine learning,but they still face many challenges such as similarity in various actions and redundant *** proposed a framework for accurate human action recognition(HAR)based on deep learning and an improved features optimization algorithm in this *** deep learning feature extraction to feature classification,the proposed framework includes several critical *** training fine-tuned deep learning models–MobileNet-V2 and Darknet53–the original video frames are *** feature extraction,pre-trained deep models are used,which are fused using the canonical correlation *** that,an improved particle swarm optimization(IPSO)-based algorithm is used to select the best *** that,the selected features were used to classify actions using various *** experimental process was performed on six publicly available datasets such as KTH,UT-Interaction,UCF Sports,Hollywood,IXMAS,and UCF YouTube,which attained an accuracy of 98.3%,98.9%,99.8%,99.6%,98.6%,and 100%,*** comparison with existing techniques,it is observed that the proposed framework achieved improved accuracy.

关键词： Action recognition deep learning features fusion features selection recognition

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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Computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.usman Ashraf Rabia Zafar ahmed M.Alghamdi ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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Computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Topological Evaluation of Certain Computer Networks by Contraharmonic-Quadratic Indices

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Computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.usman Ashraf Abdullah Alshahrani Adel Alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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Computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad usman Ashraf ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Classification of Citrus Plant Diseases Using Deep Transfer Learning

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Computers, Materials & Continua 2022年第1期70卷 1401-1417页

作者： Muhammad Zia Ur Rehman Fawad ahmed Muhammad Attique Khan usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia School of Computing Edinburgh Napier UniversityUK Department of Mathematics Statisticsand PhysicsQatar UniversityDoha2713Qatar

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and *** in turn has helped in improving the quality and production of vegetables and *** fruits arewell known for their taste and nutritional *** are one of the natural and well known sources of vitamin C and planted *** are several diseases which severely affect the quality and yield of citrus *** this paper,a new deep learning based technique is proposed for citrus disease *** different pre-trained deep learning models have been used in this *** increase the size of the citrus dataset used in this paper,image augmentation techniques are ***,to improve the visual quality of images,hybrid contrast stretching has been *** addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature *** 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 *** proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

关键词： Citrus plant disease classification deep learning feature fusion deep transfer learning

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Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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Computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.usman Ashraf Muhammad Waseem Iqbal ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key *** the polarity of an opinion is much more useful if we also know the targeted Aspect or *** based approaches,like dependency-based rules,are quite popular and effective for this ***,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency *** popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s ***,in general,the sequential rule-based approaches have poor generalization ***,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb *** this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun ***,after rigorous analysis,we have also constructed rules for the extraction of verb *** verb rules primarily based on the association between verb and opinion *** proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review *** F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing *** improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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Recognition and Tracking of Objects in a Clustered Remote Scene Environment

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Computers, Materials & Continua 2022年第1期70卷 1699-1719页

作者： Haris Masood Amad Zafar Muhammad Umair Ali Muhammad Attique Khan Salman ahmed usman Tariq Byeong-Gwon Kang Yunyoung Nam Wah Engineering College University of WahWah CanttPakistan Department of Electrical Engineering University of LahoreIslamabad CampusPakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer *** vision is one of the most active research fields that lies at the intersection of deep learning and machine *** paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shapemoving objects while accommodating the shift and scale invariances that the object may *** first part uses the Maximum Average Correlation Height(MACH)filter for object recognition and determines the bounding box *** case the correlation based MACH filter fails,the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e.,affine scale invariant feature transform(ASIFT).ASIFT is used to accommodate object shift and scale object *** extracts certain features from the object of interest,providing invariance in up to six affine parameters,namely translation(two parameters),zoom,rotation and two camera axis ***,in this paper,only the shift and scale invariances are *** second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient(APG)technique to periodically update the coordinates of the object encapsulated in the bounding *** the end,a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented,which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.

关键词： Object racking MACH filter ASIFT particle filter recognition

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