Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structure...Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.展开更多
In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the ...In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the migration performance of the virtual machine(VM)in the cloud environment.VMs allow cloud customers to store essential data and resources.However,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating expenses.Multiple VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application bandwidth.Inmulti-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data transfer.VMmigration speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration iterations.As a result,evaluating VM migration performance while considering all of these factors becomes a difficult task.Themain effort of this research is to assess migration problems on performance.The simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders ofmagnitude.The dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is completed.All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.展开更多
From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand res...From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.展开更多
This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers du...This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.展开更多
Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but earl...Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches.展开更多
Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and...Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.展开更多
In this work, the authors proposed a four parameter potentiated lifetime model named as Transmuted Exponentiated Moment Pareto (TEMP) distribution and discussed numerous characteristic measures of proposed model. Para...In this work, the authors proposed a four parameter potentiated lifetime model named as Transmuted Exponentiated Moment Pareto (TEMP) distribution and discussed numerous characteristic measures of proposed model. Parameters are estimated by the method of maximum likelihood and performance of these estimates is also assessed by simulations study. Four suitable lifetime datasets are modeled by the TEMP distribution and the results support that the proposed model provides much better results as compared to its sub-models.展开更多
Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progre...Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progression to its last stages makes diagnosis and treatment more difficult.In this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C.The dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C virus.Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main stages.These stages have proven helpful for doctors to analyze the liver’s condition.The traditional way to check the staging is the biopsy,which is a painful and time-consuming process.This article aims to provide an effective and efficient approach to predict hepatitis C staging.For this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results.展开更多
The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the...The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.展开更多
Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However...Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction.展开更多
Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for ...Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.展开更多
Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart...Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of medication.Emerging technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular disease.Cloud computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample data.ML is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and prediction.Accordingly,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms.Our recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve Bayes.Real-time patient data were extracted using the fuzzy-based model stored in the cloud.展开更多
In the agricultural industry,rice infections have resulted in significant productivity and economic losses.The infections must be recognized early on to regulate and mitigate the effects of the attacks.Early diagnosis...In the agricultural industry,rice infections have resulted in significant productivity and economic losses.The infections must be recognized early on to regulate and mitigate the effects of the attacks.Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s economy.Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise.In this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet Model.Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any country.Farmers would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment available.By Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.展开更多
In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest ...In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user.Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities.Then,the browsing process can be performed quickly and easily.This research aims to design a system to automatically organize files based on their similarities in terms of content.The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system.The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files.The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content.The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.展开更多
The techniques to find appropriate new models for data sets are very popular nowadays among the researchers of this area where existed models in the literature are not suitable. In this paper, a new distribution, gene...The techniques to find appropriate new models for data sets are very popular nowadays among the researchers of this area where existed models in the literature are not suitable. In this paper, a new distribution, generalized inverted Kumaraswamy (GIKum) distribution is introduced. The main aims of this research are to develop a general form of inverted Kumaraswamy (IKum) distribution which is flexible than the IKum distribution and all of its related and sub models. Some properties of GIKum distribution such as measures of central tendency and dispersion, models of stress-strength, limiting distributions, characterization of GIKum distribution and related probability distributions through some specific transformations are derived. The mathematical expressions of reliability function (r.f) and the hazard rate function (hrf) of the GIKum distribution are found and presented through their graphs. The parameters estimation through the maximum likelihood (ML) estimation method is used and the results are applied to the data set of prices of wooden toys of 31 children.展开更多
This paper introduces a new distribution based on the exponential distribution, known as Size-biased Double Weighted Exponential Distribution (SDWED). Some characteristics of the new distribution are obtained. Plots f...This paper introduces a new distribution based on the exponential distribution, known as Size-biased Double Weighted Exponential Distribution (SDWED). Some characteristics of the new distribution are obtained. Plots for the cumulative distribution function, pdf and hazard function, tables with values of skewness and kurtosis are provided. As a motivation, the statistical application of the results to a problem of ball bearing data has been provided. It is observed that the new distribution is skewed to the right and bears most of the properties of skewed distribution. It is found that our newly proposed distribution fits better than size-biased Rayleigh and Maxwell distributions and many other distributions. Since many researchers have studied the procedure of the weighted distributions in the estates of forest, biomedicine and biostatistics etc., we hope in numerous fields of theoretical and applied sciences, the findings of this paper will be useful for the practitioners.展开更多
Single-atom catalysts(SACs)have gained substantial attention because of their exceptional catalytic properties.However,the high surface energy limits their synthesis,thus creating significant challenges for further de...Single-atom catalysts(SACs)have gained substantial attention because of their exceptional catalytic properties.However,the high surface energy limits their synthesis,thus creating significant challenges for further development.In the last few years,metal–organic frameworks(MOFs)have received significant consideration as ideal candidates for synthesizing SACs due to their tailorable chemistry,tunable morphologies,high porosity,and chemical/thermal stability.From this perspective,this review thoroughly summarizes the previously reported methods and possible future approaches for constructing MOF-based(MOF-derived-supported and MOF-supported)SACs.Then,MOF-based SAC's identification techniques are briefly assessed to understand their coordination environments,local electronic structures,spatial distributions,and catalytic/electrochemical reaction mechanisms.This review systematically highlights several photocatalytic and electrocatalytic applications of MOF-based SACs for energy conversion and storage,including hydrogen evolution reactions,oxygen evolution reactions,O_(2)/CO_(2)/N_(2) reduction reactions,fuel cells,and rechargeable batteries.Some light is also shed on the future development of this highly exciting field by highlighting the advantages and limitations of MOF-based SACs.展开更多
In most arid and semiarid soils, naturally occurring phosphorus(P) is a major yield-limiting plant nutrient. In this study, to investigate the effects of organic(OP) and inorganic P(IP) sources on P fractionation, a c...In most arid and semiarid soils, naturally occurring phosphorus(P) is a major yield-limiting plant nutrient. In this study, to investigate the effects of organic(OP) and inorganic P(IP) sources on P fractionation, a calcareous sandy loam alkaline soil was fertilized with OP and IP fertilizers at low(80 mg P kg^(-1) soil) and high(160 mg P kg^(-1) soil) application rates. Three combinations of OP and IP(i.e., 75% OP + 25% IP, 50% OP + 50% IP, and 25% OP + 75% IP) were applied at low and high application rates,respectively, followed by soil aging for 21 d. Soil samples were collected after 1, 2, 3, 7, and 21 d and subjected to sequential extraction to analyze soluble and exchangeable, Fe-and Al-bound, Ca-bound, and residual P fractions. The soluble and exchangeable P fraction significantly increased up to 24.3%, whereas the Ca-bound fraction decreased up to 40.7% in the soils receiving 75% OP + 25% IP and 50% OP + 50% IP, respectively, compared with the control(receiving no P fertilizer). However, the transformation of P fractions was influenced by aging time. Addition of P sources caused instant changes in different P fractions, which then tended to decline with aging time. Change in soil p H was the limiting factor in controlling P availability. At high application rate, the OP source significantly increased soil P availability compared with the IP source with soil aging. Depending on P fractionation, a proper combination of OP and IP fertilizers, as long-term slow and instant P-releasing sources for plant uptake, respectively, may be a sustainable strategy to meet crop P requirements in the arid and semiarid soils.展开更多
There are numerous studies conducted on biochar for its carbon (C) sequestration potential;however,there are limited studies available on the behavior of salt-affected soils related to biochar application.Therefore,mo...There are numerous studies conducted on biochar for its carbon (C) sequestration potential;however,there are limited studies available on the behavior of salt-affected soils related to biochar application.Therefore,more studies are needed to elucidate the mechanisms through which biochar affects saline soil properties.In this study,biochars were produced from solid waste at pyrolysis temperatures of 300,500,and 700?C (BC300,BC500,and BC700,respectively)and applied to a saline soil to evaluate their impacts on soil carbon dioxide (CO_(2)) efflux,C sequestration,and soil quality.A soil incubation experiment lasting for 107 d was conducted.The results showed that soil CO_(2) efflux rate,cumulative CO_(2) emission,active organic C (AOC),and organic matter (OM)significantly increased with BC300 application to a greater extent than those with BC500 and BC700 as compared to those in the no-biochar control (CK).However,soil C non-lability did not significantly increase in the treatments with biochars,except BC700,as compared to that in CK.Besides improving the soil quality by increasing the soil AOC and OM,BC300 showed positive impacts in terms of increasing CO_(2) emission from the saline soil,while BC500 and BC700 showed greater potentials of sequestering C in the saline soil by increasing the soil non-labile C fraction.The recalcitrance index (R50) values of BC500 and BC700 were>0.8,indicating their high stability in the saline soil.It could be concluded that biochars pyrolyzed at high temperatures (?500?C)could be suitable in terms of C sequestration,while biochars pyrolyzed at low temperatures (?300?C) could be suitable for improving saline soil quality.展开更多
文摘Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.
文摘In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the migration performance of the virtual machine(VM)in the cloud environment.VMs allow cloud customers to store essential data and resources.However,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating expenses.Multiple VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application bandwidth.Inmulti-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data transfer.VMmigration speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration iterations.As a result,evaluating VM migration performance while considering all of these factors becomes a difficult task.Themain effort of this research is to assess migration problems on performance.The simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders ofmagnitude.The dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is completed.All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.
文摘From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.
文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.
文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches.
文摘Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.
文摘In this work, the authors proposed a four parameter potentiated lifetime model named as Transmuted Exponentiated Moment Pareto (TEMP) distribution and discussed numerous characteristic measures of proposed model. Parameters are estimated by the method of maximum likelihood and performance of these estimates is also assessed by simulations study. Four suitable lifetime datasets are modeled by the TEMP distribution and the results support that the proposed model provides much better results as compared to its sub-models.
文摘Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progression to its last stages makes diagnosis and treatment more difficult.In this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C.The dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C virus.Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main stages.These stages have proven helpful for doctors to analyze the liver’s condition.The traditional way to check the staging is the biopsy,which is a painful and time-consuming process.This article aims to provide an effective and efficient approach to predict hepatitis C staging.For this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results.
文摘The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.
文摘Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction.
文摘Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.
文摘Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of medication.Emerging technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular disease.Cloud computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample data.ML is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and prediction.Accordingly,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms.Our recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve Bayes.Real-time patient data were extracted using the fuzzy-based model stored in the cloud.
文摘In the agricultural industry,rice infections have resulted in significant productivity and economic losses.The infections must be recognized early on to regulate and mitigate the effects of the attacks.Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s economy.Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise.In this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet Model.Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any country.Farmers would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment available.By Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.
文摘In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user.Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities.Then,the browsing process can be performed quickly and easily.This research aims to design a system to automatically organize files based on their similarities in terms of content.The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system.The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files.The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content.The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.
文摘The techniques to find appropriate new models for data sets are very popular nowadays among the researchers of this area where existed models in the literature are not suitable. In this paper, a new distribution, generalized inverted Kumaraswamy (GIKum) distribution is introduced. The main aims of this research are to develop a general form of inverted Kumaraswamy (IKum) distribution which is flexible than the IKum distribution and all of its related and sub models. Some properties of GIKum distribution such as measures of central tendency and dispersion, models of stress-strength, limiting distributions, characterization of GIKum distribution and related probability distributions through some specific transformations are derived. The mathematical expressions of reliability function (r.f) and the hazard rate function (hrf) of the GIKum distribution are found and presented through their graphs. The parameters estimation through the maximum likelihood (ML) estimation method is used and the results are applied to the data set of prices of wooden toys of 31 children.
文摘This paper introduces a new distribution based on the exponential distribution, known as Size-biased Double Weighted Exponential Distribution (SDWED). Some characteristics of the new distribution are obtained. Plots for the cumulative distribution function, pdf and hazard function, tables with values of skewness and kurtosis are provided. As a motivation, the statistical application of the results to a problem of ball bearing data has been provided. It is observed that the new distribution is skewed to the right and bears most of the properties of skewed distribution. It is found that our newly proposed distribution fits better than size-biased Rayleigh and Maxwell distributions and many other distributions. Since many researchers have studied the procedure of the weighted distributions in the estates of forest, biomedicine and biostatistics etc., we hope in numerous fields of theoretical and applied sciences, the findings of this paper will be useful for the practitioners.
基金support from the Shenzhen Science and Technology Program(No.KQTD20190929173914967,ZDSYS20220527171401003,and JCYJ20200109110416441).
文摘Single-atom catalysts(SACs)have gained substantial attention because of their exceptional catalytic properties.However,the high surface energy limits their synthesis,thus creating significant challenges for further development.In the last few years,metal–organic frameworks(MOFs)have received significant consideration as ideal candidates for synthesizing SACs due to their tailorable chemistry,tunable morphologies,high porosity,and chemical/thermal stability.From this perspective,this review thoroughly summarizes the previously reported methods and possible future approaches for constructing MOF-based(MOF-derived-supported and MOF-supported)SACs.Then,MOF-based SAC's identification techniques are briefly assessed to understand their coordination environments,local electronic structures,spatial distributions,and catalytic/electrochemical reaction mechanisms.This review systematically highlights several photocatalytic and electrocatalytic applications of MOF-based SACs for energy conversion and storage,including hydrogen evolution reactions,oxygen evolution reactions,O_(2)/CO_(2)/N_(2) reduction reactions,fuel cells,and rechargeable batteries.Some light is also shed on the future development of this highly exciting field by highlighting the advantages and limitations of MOF-based SACs.
基金supported by the International Research Group Project (No. IRG-14-02) from the Deanship of Scientific Research at King Saud University, Saudi Arabia
文摘In most arid and semiarid soils, naturally occurring phosphorus(P) is a major yield-limiting plant nutrient. In this study, to investigate the effects of organic(OP) and inorganic P(IP) sources on P fractionation, a calcareous sandy loam alkaline soil was fertilized with OP and IP fertilizers at low(80 mg P kg^(-1) soil) and high(160 mg P kg^(-1) soil) application rates. Three combinations of OP and IP(i.e., 75% OP + 25% IP, 50% OP + 50% IP, and 25% OP + 75% IP) were applied at low and high application rates,respectively, followed by soil aging for 21 d. Soil samples were collected after 1, 2, 3, 7, and 21 d and subjected to sequential extraction to analyze soluble and exchangeable, Fe-and Al-bound, Ca-bound, and residual P fractions. The soluble and exchangeable P fraction significantly increased up to 24.3%, whereas the Ca-bound fraction decreased up to 40.7% in the soils receiving 75% OP + 25% IP and 50% OP + 50% IP, respectively, compared with the control(receiving no P fertilizer). However, the transformation of P fractions was influenced by aging time. Addition of P sources caused instant changes in different P fractions, which then tended to decline with aging time. Change in soil p H was the limiting factor in controlling P availability. At high application rate, the OP source significantly increased soil P availability compared with the IP source with soil aging. Depending on P fractionation, a proper combination of OP and IP fertilizers, as long-term slow and instant P-releasing sources for plant uptake, respectively, may be a sustainable strategy to meet crop P requirements in the arid and semiarid soils.
基金partially supported by the University Research Fund Program of the Quaid-i-Azam University, Pakistan。
文摘There are numerous studies conducted on biochar for its carbon (C) sequestration potential;however,there are limited studies available on the behavior of salt-affected soils related to biochar application.Therefore,more studies are needed to elucidate the mechanisms through which biochar affects saline soil properties.In this study,biochars were produced from solid waste at pyrolysis temperatures of 300,500,and 700?C (BC300,BC500,and BC700,respectively)and applied to a saline soil to evaluate their impacts on soil carbon dioxide (CO_(2)) efflux,C sequestration,and soil quality.A soil incubation experiment lasting for 107 d was conducted.The results showed that soil CO_(2) efflux rate,cumulative CO_(2) emission,active organic C (AOC),and organic matter (OM)significantly increased with BC300 application to a greater extent than those with BC500 and BC700 as compared to those in the no-biochar control (CK).However,soil C non-lability did not significantly increase in the treatments with biochars,except BC700,as compared to that in CK.Besides improving the soil quality by increasing the soil AOC and OM,BC300 showed positive impacts in terms of increasing CO_(2) emission from the saline soil,while BC500 and BC700 showed greater potentials of sequestering C in the saline soil by increasing the soil non-labile C fraction.The recalcitrance index (R50) values of BC500 and BC700 were>0.8,indicating their high stability in the saline soil.It could be concluded that biochars pyrolyzed at high temperatures (?500?C)could be suitable in terms of C sequestration,while biochars pyrolyzed at low temperatures (?300?C) could be suitable for improving saline soil quality.
