检索结果-南通市图书馆

Cervical Cancer Prediction Empowered with Federated Machine Learning

Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Suitable energy platform significantly improves charge separation of g-C3N4 for CO2 reduction and pollutant oxidation under visible-light

引用

Progress in Natural Science:Materials International 2019年第2期29卷 138-144页

作者： Amir Zada Nauman Ali Fazle Subhan Natasha Anwar muhammad Ishaq Ali Shah Muhammad Ateeq Zahid Hussain Khair Zaman Momin Khan Department of Chemistry Abdul Wali Khan University Key Laboratory of Functional Inorganic Materials Chemistry (Heilongjiang University) Ministry of Education School of Chemistry and Materials Science International Joint Research Center for Catalytic Technology Institute of Chemical Sciences University of Peshawar

The photocatalytic activities of g-C3N4can be significantly improved by increasing life time of the photogenerated charges. Here, in this work we introduced TiO2as proper energy platform to accept the photogenerated electrons from g-C3N4during photocatalysis. The nanophotocatalysts formed from the combination of a suitable amount of TiO2nanoparticles and g-C3N4nanosheets showed 8.75 and 4.22% enhancement in photocatalytic activities for CO2reduction and 2-chlorophenol(2-CP) degradation under visible light illumination as compared to bare g-C3N4. Based on the surface photovoltage spectra, photoluminescence spectra and examination of formed hydroxyl radicals, it was confirmed that these enhanced photoactivities were attributed to the much-improved charge separation via the electron transfer from g-C3N4to TiO2. From trapping experiments,it was found that hydroxyl radicals were the major species involved in the photocatalytic degradation of 2-CP.This study is helpful to synthesize efficient photocatalysts to cope with energy and environmental issues.

关键词： Nanophotocatalyst Charge separation CO2 reduction Pollutant degradation

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

引用

Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath ateeq Bassam SaleemAllah Almogadwy muhammad Adnan Khan muhammad Hasnain Azam Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems

引用

Computers, Materials & Continua 2022年第3期70卷 5889-5905页

作者： Rao muhammad Asif Mustafa Shakir Jamel Nebhen ateeq Ur Rehman muhammad Shafiq Jin-Ghoo Choi Department of Electrical Engineering Superior UniversityLahore54000Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency.The research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model scenario.The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for optimization.In this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput.The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions.It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.

关键词： Energy efficiency spectral efficiency throughput massive MIMO downlink uplink base stations power consumption

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

引用

Computers, Materials & Continua 2022年第4期71卷 1153-1169页

作者： Jehangir Arshad Ayesha Khan Mariam Aftab Mujtaba Hussain ateeq Ur Rehman Shafiq Ahmad Adel M.Al-Shayea muhammad Shafiq Department of Electrical&Computer Engineering COMSATS University IslamabadLahore Campus54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Controlling feedback control systems in continuous action spaces has always been a challenging problem.Nevertheless,reinforcement learning is mainly an area of artificial intelligence(AI)because it has been used in process control for more than a decade.However,the existing algorithms are unable to provide satisfactory results.Therefore,this research uses a reinforcement learning(RL)algorithm to manage the control system.We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient(DDPG).The actor-critic scenario using DDPG is implemented to build the RL agent.In addition,a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.The RL algorithms are robust and proficient in using trial and error to search for the best strategy.Our proposed algorithm is a deep deterministic policy gradient,in which a large amount of training data trains the agent.Once the system is trained,the agent can automatically adjust the control parameters.The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment.The performance of the proposed RL algorithm is compared with a proportional integral derivative(PID)controller and a linear quadratic regulator(LQR)controller.The simulation results of the proposed scheme are promising for the feedback control problems.

关键词： Feedback control systems reinforcement learning artificial intelligence

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Role of Organic Amendments to Mitigate Cd Toxicity and Its Assimilation in Triticum aestivum L.

引用

Phyton-International Journal of Experimental Botany 2022年第11期91卷 2491-2504页

作者： Tauqeer Ahmad Yasir Sobia Aslam muhammad Shahid Rizwan Allah Wasaya Muhammad Ateeq muhammad Naeem Khan Sikander Khan Tanveer Walid Soufan Basharat Ali Allah Ditta Arpna Kumari Ayman EL Sabagh College of Agriculture Bahauddin Zakariya UniversityBahadur Sub-Campus Layyah31200Pakistan Cholistan Institute of Desert Studies The Islamia University of BahawalpurBahawalpur63100Pakistan Directorate General Soil Survey of Punjab Agriculture DepartmentLahore54780Pakistan National Agricultural Research Centre Islamabad44000Pakistan Plant Production Department College of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Agricultural Engineering Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Biological Sciences The University of Western AustraliaPerthWA 6009Australia Department of Environmental Sciences Shaheed Benazir Bhutto University SheringalDir(U)Khyber Pakhtunkhwa18000Pakistan Academy of Biology and Biotechnology Southern Federal UniversityRostov-on-Don344006Russia Department of Agronomy Faculty of AgricultureKafrelsheikh UniversityKafr El Sheikh33511Egypt

In soil biota,higher and enduring concentration of heavy metals like cadmium(Cd)is hazardous and associated with great loss in growth,yield,and quality parameters of most of the crop plants.Recently,in-situ applications of eco-friendly stabilizing agents in the form of organic modifications have been utilized to mitigate the adverse effects of Cd-toxicity.This controlled experiment was laid down to appraise the imprints of various applied organic amendments namely poultry manure(PM),farmyard manure(FYM),and sugarcane press mud(PS)to immobilize Cd in polluted soil.Moreover,phytoavailability of Cd in wheat was also accessed under an alkaline environment.Results revealed that the addition of FYM(5–10 ton ha^(-1))in Cd-contaminated soil significantly increased germination rate,leaf chlorophyll content,plant height,spike length,biological and grain yield amongst all applied organic amendments.Moreover,the addition of FYM(5–10 ton ha^(-1))also reduced the phytoavailability of Cd by 73–85%in the roots,57–83%in the shoots,and 81–90%in grains of wheat crop.Thus,it is affirmed that incorporation of FYM(5–10 ton ha^(-1))performed better to enhance wheat growth and yield by remediating Cd.Thus,the application of FYM(5–10 ton ha^(-1))reduced the toxicity induced by Cd to plants by declining its uptake and translocation as compared to all other applied organic amendments to immobilize Cd under sandy alkaline polluted soil.

关键词： Cadmium toxicity mitigation chlorophyll attributes morphometry yield attributes cereal

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

引用

Computers, Materials & Continua 2023年第4期75卷 311-329页

作者： Sumbal Zahoor Ishtiaq Ahmad ateeq Ur Rehman Elsayed Tag Eldin Nivin AGhamry muhammad Shafiq Department of Electrical Engineering The University of LahoreLahore54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Computers and Artificial Intelligence Cairo UniversityGiza3750010Egypt Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization(NFV),Software-Defined Networks(SDN),and cloud computing will be crucial in addressing various challenges that 5G networks will face,particularly adaptability,scalability,and reliability.The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms,including hypervisors,clouds,and containers,which will serve as a guide to dealing with the stimulating environment of 5G.This is particularly crucial when implementing network operations at the edge of 5G networks,where limited resources and prompt user responses are mandatory.Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’latency and network throughput at the expense of higher virtualized processor use.In contrast to public clouds,where a set of rules is created to allow only the appropriate traffic,security is still a problem with containers.

关键词： NFV hypervisors cloud computing containers

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Seed Priming and Foliar Supplementation withβ-aminobutyric Acid Alleviates Drought Stress through Mitigation of Oxidative Stress and Enhancement of Antioxidant Defense in Linseed(Linum usitatissimum L.)

引用

Phyton-International Journal of Experimental Botany 2023年第11期92卷 3113-3131页

作者： Tauqeer Ahmad Yasir Muhammad Ateeq Allah Wasaya Mubshar Hussain Naeem Sarwar Khuram Mubeen Mudassir Aziz muhammad Aamir Iqbal Chukwuma COgbaga Ibrahim Al-Ashkar Md Atikur Rahman Ayman El Sabagh Department of Agronomy University of LayyahLayyah31200Pakistan Department of Agronomy Bahauddin Zakariya UniversityMultan60000Pakistan State Key Lab of Crop Stress Biology for Arid Areas College of AgronomyNorthwest A&F UniversityYangling712100China Department of Agronomy MNS University of AgricultureMultan60000Pakistan Department of Agronomy Faculty of AgricultureUniversity of Poonch RawalakotRawalakot10250Pakistan Independent Researcher MiddlesbroughNorth YorkshireUK Plant Production Department College of Food and Agriculture SciencesKing Saud UniversityP.O.Box 2460Riyadh11451Saudi Arabia Grassland and Forage Division National Institute of Animal ScienceRural Development AdministrationCheonan31000Korea Department of Field Crops Faculty of AgricultureSiirt UniversitySiirt56100Turkey Department of Agronomy Faculty of AgricultureKafrelsheikh UniversityKafr El-Shaikh33516Egypt

Drought is one of the critical limitations to agricultural soils and crop plants.Scarcity of water is increasing due to climate change that lead to increasing threats to global food security.Therefore,ecofriendly and cost effective strategies are highly desirable for mitigating drought stress along with sustainable and smart agricultural production.The aim of the study was to mitigate DS using seed priming and exogenous supplementation ofβ-aminobutyric acid(BABA)in linseed(Linum usitatissimum L.).Different doses(0,50,100 and 150μM)of BABA were used for seed priming agent and foliar spraying under three soil moisture levels viz.,25%(SM25),45%(SM45)and 65%(SM65).The response variables of both experiments included different agro-botanical traits and oxidative stress indicators such as melondialdehyde content,free proline accumulation,and antioxidant defense in plants.The linseed plants showed water stress at SM25 that reduced plant height,number of branches per plant,time taken to flower initiation and heading,and root and shoot dry weights.Additionally,the number of capsules and seeds per capsule showed a significant decline at SM25,which led to a drastic reduction in 100-seed weight yield in linseed plants in both experiments.However,seed priming and foliar supplementation with of BABA(50–100μM)significantly improved these morpho-agronomical attributes in linseed plants under DS.The results revealed that the BABA was fully active in linseed plants at SM25.Interestingly,the combination of SM25 with BABA significantly improved the antioxidant enzymes superoxide dismutase(SOD),catalase(CAT),ascorbate peroxidase(APX),and peroxidase(POD)activity,which significantly enhanced DS tolerance in linseed plants.These findings might be useful to oil seed breeders and farmers linseed for breeding program in linseed plants as well as sustainable agricultural production of oil seed crop plants.

关键词： Linseed water stress signaling molecule climate change biostimulant ecofriendly approach sustainable agricultural

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

引用

Computers, Materials & Continua 2022年第3期70卷 6057-6072页

作者： Syed Rizwan Hassan Ishtiaq Ahmad Jamel Nebhen ateeq Ur Rehman muhammad Shafiq Jin-Ghoo Choi Department of Electrical Engineering The University of LahoreLahore54000Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors.

关键词： IoT fog-cloud computing architecture module placement latency sensitive application resource aware placement

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

引用

Computers, Materials & Continua 2022年第7期72卷 1017-1033页

作者： Noman Shabbir Lauri Kütt muhammad Jawad Oleksandr Husev ateeq Ur Rehman Akber Abid Gardezi muhammad Shafiq Jin-Ghoo Choi Department of Electrical Power Engineering&Mechatronics Tallinn University of TechnologyTallinn19086Estonia Department of Electrical and Computer Engineering COMSATS University Islamabad(Lahore Campus)54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Wind energy is featured by instability due to a number of factors,such as weather,season,time of the day,climatic area and so on.Furthermore,instability in the generation of wind energy brings new challenges to electric power grids,such as reliability,flexibility,and power quality.This transition requires a plethora of advanced techniques for accurate forecasting of wind energy.In this context,wind energy forecasting is closely tied to machine learning(ML)and deep learning(DL)as emerging technologies to create an intelligent energy management paradigm.This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set.Moreover,we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast.For the selection of model parameters,a detailed exploratory data analysis is conducted.All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h.The main objective of the study is to foster an efficient forecasting technique for Estonia.The comparative analysis of the results indicates that Support Vector Machine(SVM),Non-linear Autoregressive Neural Networks(NAR),and Recurrent Neural Network-Long-Term Short-Term Memory(RNNLSTM)are respectively 10%,25%,and 32%more efficient compared to TSO’s forecasting algorithm.Therefore,RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

关键词： Wind energy production energy forecast machine learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：