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Schistosomiasis transmission in Zimbabwe:Modelling based on machine learning

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Infectious Disease Modelling 2024年第4期9卷 1081-1094页

作者： Hong-Mei Li Jin-Xin Zheng Nicholas Midzi Masceline Jenipher Mutsaka-Makuvaza Shan Lv Shang Xia Ying-jun Qian Ning Xiao Robert Berguist Xiao-Nong Zhou National Institute of Parasitic Diseases Chinese Center for Disease Control and Prevention(Chinese Center for Tropical Diseases Research)NHC Key Laboratory of Parasite and Vector BiologyWHO Collaborating Centre for Tropical DiseasesNational Center for International Research on Tropical DiseasesShanghai200025China National Institute of health Research Ministry of Health and Child CareHarareZimbabwe University of Rwanda College of Medicine and Health SciencesSchool of Medicine and PharmacyDepartment of Microbiology and ParasitologyRwanda Ingerod 407 SE-45494BrastadSweden

Zimbabwe,located in Southern Africa,faces a significant public health challenge due to *** investigated this issue with emphasis on risk prediction of schistosomiasis for the entire *** this end,we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public *** studied the population requiring praziquantel with regard to whether or not mass drug administration(MDA)had been regularly *** machinelearning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error(MAE),the root mean squared error(RMSE)and the coefficient of determination(R2).The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and *** *** found that the top-five correlation factors,such as the past(rather than current)time,unsettled MDA implementation,constrained economy,high rainfall during the warmest season,and high annual precipitation were closely associated with higher *** prevalence,while lower elevation,high rainfall during the warmest season,steeper slope,past(rather than current)time,and higher minimum temperature in the coldest month were rather related to higher *** *** random forest(RF)algorithm was considered as the formal best model construction method,with MAE=0.108;RMSE=0.143;and R^(2)=0.517 for ***,and with the corresponding figures for *** being 0.053;0.082;and *** on this optimal model,the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%,with that of *** at 13.8% and that of *** at 7.1%,requiring annual MDA based on a population of 3,003,*** MDA,the current total schistosomiasis prevalence would

关键词： Machine-learning Transmission risk model Schistosomiasis Zimbabwe

Exploring the meaning of care among lived experiences of patients with blindness:an interpretive phenomenological study

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Frontiers of Nursing 2024年第3期11卷 319-326页

作者： Mohammad Ali Mohammadi Mehdi Heidarzadeh Saeed Mehri Mahmood Shamshiri department of Medical-Surgical Nursing School of Nursing and MidwiferyArdabil University of Medical SciencesArdabil ***Iran department of Emergency Nursing School of Nursing and MidwiferyArdabil University of Medical SciencesArdabil ***Iran department of Critical Care Nursing School of Nursing and MidwiferyArdabil University of Medical SciencesArdabil ***Iran

Objective:To explore the meaning of care experienced by people with blindness in ***:Interpretive phenomenology along with the 6-step method of van Manen was used to conduct the *** purposeful sampling,15 people with legal blindness were *** analysis was used to isolate the meaning of ***:Five themes emerged:(a)nurses in the eyes of patients with blindness;(b)negligence in the caring moments;(c)being cared for in ambiguity;(d)Uncoordinated care;and(e)psychological discomfor *** sub-themes were condensed into an overarching theme titled as“marginalized patients inside the stereotypical healthcare system.”Conclusions:Lived experiences of patients with blindness revealed that hospitals provide stereotypic or inappropriate care for this minority group in *** professionals par ticularly nurses should be skilled to provide person-centered and coordinated care for patients with blindness.

关键词： hospitalization lived experience meaning of care people with blindness phenomenology

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

An Ingenious IoT Based Crop Prediction System Using ML and EL

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Computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE department of Computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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Computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas department of Computer Engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing *** study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)*** on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from *** adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational *** conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural *** results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP *** of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational *** demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)***,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

Caliper-based precise positioning of the target(CALIPPOT)for transcranial magnetic stimulation without neuronavigation system

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General Psychiatry 2024年第2期37卷 311-314页

作者： Yunsong Hu Rong Zeng Juan Yue Qiu Ge Hongxiao Wang Zijian Feng Jue Wang Yufeng Zang Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments HangzhouZhejiangChina Institutes of psychological sciences Hangzhou Normal UniversityHangzhouZhejiangChina Transcranial Magnetic Stimulation Center Deqing Hospital of Hangzhou Normal UniversityDeqingZhejiangChina Institute of Sports Medicine and health Chengdu Sport UniversityChengduSichuanChina department of Neurology The Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina

To the editor:Transcranial magnetic stimulation(TMS)is a non-invasive brain modulation *** important usage of TMS is the transient interruption of cognitive brain function(also named virtual lesion)for investigating precisely where and when a specific cortical region contributes to a specific cognitive function.1 A more important usage of TMS is the treatment of brain disorders by repetitive TMS(rTMS).

关键词： stimulation technique. precise

DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J department of CSE Karunya Institute of Technology and sciences CoimbatoreTamilnaduIndia department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia department of Computer sciences Kinnaird College forWomenLahorePunjab54700Pakistan department of Information sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan department of Basic sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine learning-Powered Framework for Cyber-Resilient Ecosystems

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Computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine learning variability management cybersecurity digital ecosystems cyber-resilience