Pub Date : 2021-01-01DOI: 10.11648/j.mlr.20210602.11
Jimi Asmara, Gregorius Rinduh Iriane, Edwin Ariesto Umbu Malahina
{"title":"Design to Build E-learning Application in SMP N 2 Busalangga","authors":"Jimi Asmara, Gregorius Rinduh Iriane, Edwin Ariesto Umbu Malahina","doi":"10.11648/j.mlr.20210602.11","DOIUrl":"https://doi.org/10.11648/j.mlr.20210602.11","url":null,"abstract":"","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82304583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.11648/j.mlr.20210602.12
A. Adegboye, Imianvan Anthony Agboizebeta
{"title":"A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression","authors":"A. Adegboye, Imianvan Anthony Agboizebeta","doi":"10.11648/j.mlr.20210602.12","DOIUrl":"https://doi.org/10.11648/j.mlr.20210602.12","url":null,"abstract":"","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75186040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.
{"title":"Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset","authors":"Masoud Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh","doi":"10.11648/j.mlr.20210601.11","DOIUrl":"https://doi.org/10.11648/j.mlr.20210601.11","url":null,"abstract":": With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74669781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-30DOI: 10.11648/J.MLR.20190404.11
S. Nada, A. Elrayes, A. Elrokh, A. Rabie
A simple graph is said to be signed product cordial if it admits ±1 labeling that satisfies certain conditions. Our aim in this paper is to contribute some new results on signed product cordial labeling and present necessary and sufficient conditions for signed product cordial of the sum and union of two fourth power of paths. We also study the signed product cordiality of the sum and union of fourth power cycles The residue classes modulo 4 are accustomed to find suitable labelings for each class to achieve our task. We have shown that the union and the join of any two fourth power of paths are always signed product cordial. Howover, the join and union of fourth power of cycles are only signed codial with some expectional situations.
{"title":"Signed Product Cordial of the Sum and Union of Two Fourth Power of Paths and Cycles","authors":"S. Nada, A. Elrayes, A. Elrokh, A. Rabie","doi":"10.11648/J.MLR.20190404.11","DOIUrl":"https://doi.org/10.11648/J.MLR.20190404.11","url":null,"abstract":"A simple graph is said to be signed product cordial if it admits ±1 labeling that satisfies certain conditions. Our aim in this paper is to contribute some new results on signed product cordial labeling and present necessary and sufficient conditions for signed product cordial of the sum and union of two fourth power of paths. We also study the signed product cordiality of the sum and union of fourth power cycles The residue classes modulo 4 are accustomed to find suitable labelings for each class to achieve our task. We have shown that the union and the join of any two fourth power of paths are always signed product cordial. Howover, the join and union of fourth power of cycles are only signed codial with some expectional situations.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75982562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.11648/J.MLR.20190402.12
S. U. Gulumbe, S. Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, U. Usman
Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.
{"title":"Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study","authors":"S. U. Gulumbe, S. Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, U. Usman","doi":"10.11648/J.MLR.20190402.12","DOIUrl":"https://doi.org/10.11648/J.MLR.20190402.12","url":null,"abstract":"Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81863464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-25DOI: 10.11648/J.MLR.20190402.11
Deepak Choudhary
This paper presents machine learning algorithms based on back-propagation neural network (BPNN) that employs sequential feature selection (SFS) for predicting the compressive strength of Ultra-High Performance Concrete (UHPC). A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. The BPNN and SFS were used interchangeably to identify the relevant features that contributed with the response variable. As a result, the BPNN with the selected features was able to interpret more accurate results (r = 0.991) than the model with all the features (r2 = 0.816). The utilization of ANN modelling made its way into the prediction of fresh and hardened properties of concrete based on given experimental input parameters, whereby several authors developed AI models to predict the compressive strength of normal weight, light weight and recycled concrete. The steps that were are followed in developing a robust and accurate numerical model using SFS include (1) design and validation of ANN model by manipulating the number of neurons and hidden layers; (2) execution of SFS using ANN as a wrapper; and (3) analysis of selected features using both ANN and nonlinear regression. It is concluded that the usage of ANN with SFS provided an improvement to the prediction model’s accuracy, making it a viable tool for machine learning approaches in civil engineering case studies.
{"title":"Learning Algorithms Using BPNN & SFS for Prediction of Compressive Strength of Ultra-High Performance Concrete","authors":"Deepak Choudhary","doi":"10.11648/J.MLR.20190402.11","DOIUrl":"https://doi.org/10.11648/J.MLR.20190402.11","url":null,"abstract":"This paper presents machine learning algorithms based on back-propagation neural network (BPNN) that employs sequential feature selection (SFS) for predicting the compressive strength of Ultra-High Performance Concrete (UHPC). A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. The BPNN and SFS were used interchangeably to identify the relevant features that contributed with the response variable. As a result, the BPNN with the selected features was able to interpret more accurate results (r = 0.991) than the model with all the features (r2 = 0.816). The utilization of ANN modelling made its way into the prediction of fresh and hardened properties of concrete based on given experimental input parameters, whereby several authors developed AI models to predict the compressive strength of normal weight, light weight and recycled concrete. The steps that were are followed in developing a robust and accurate numerical model using SFS include (1) design and validation of ANN model by manipulating the number of neurons and hidden layers; (2) execution of SFS using ANN as a wrapper; and (3) analysis of selected features using both ANN and nonlinear regression. It is concluded that the usage of ANN with SFS provided an improvement to the prediction model’s accuracy, making it a viable tool for machine learning approaches in civil engineering case studies.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88290155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-21DOI: 10.11648/J.MLR.20190401.14
U. K. Thakur, Chandrashekhar Dethe
The recent advances and the convergence of micro electro-mechanical systems technology, integrated circuit technologies, microprocessor hardware and Nano-technology, wireless communications, Ad-hoc networking routing protocols, distributed signal processing, and embedded systems have made the concept of Wireless Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. Even though sensor networks are primarily designed for monitoring and reporting events, since they are application dependent, a single routing protocol cannot be efficient for sensor networks across all applications. In this paper, we analyze the design issues of sensor networks and present a classification and comparison of routing protocols. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks. A reliable transmission of packet data information, with low latency and high energy-efficiency, is truly essential for wireless sensor networks, employed in delay sensitive industrial control applications. The proper selection of the routing protocol to achieve maximum efficiency is a challenging task, since latency, reliability and energy consumption are inter-related with each other. It is observed that, Quality of Service (QoS) of the network can improve by minimizing delay in packet delivery, and life time of the network, can be extend by using suitable energy efficient routing protocol.
{"title":"QoS Aware Cloud Based Routing Protocol for Security Improvement of Hybrid Wireless Network","authors":"U. K. Thakur, Chandrashekhar Dethe","doi":"10.11648/J.MLR.20190401.14","DOIUrl":"https://doi.org/10.11648/J.MLR.20190401.14","url":null,"abstract":"The recent advances and the convergence of micro electro-mechanical systems technology, integrated circuit technologies, microprocessor hardware and Nano-technology, wireless communications, Ad-hoc networking routing protocols, distributed signal processing, and embedded systems have made the concept of Wireless Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. To prolong the lifetime of the sensor nodes, designing efficient routing protocols is critical. Even though sensor networks are primarily designed for monitoring and reporting events, since they are application dependent, a single routing protocol cannot be efficient for sensor networks across all applications. In this paper, we analyze the design issues of sensor networks and present a classification and comparison of routing protocols. This comparison reveals the important features that need to be taken into consideration while designing and evaluating new routing protocols for sensor networks. A reliable transmission of packet data information, with low latency and high energy-efficiency, is truly essential for wireless sensor networks, employed in delay sensitive industrial control applications. The proper selection of the routing protocol to achieve maximum efficiency is a challenging task, since latency, reliability and energy consumption are inter-related with each other. It is observed that, Quality of Service (QoS) of the network can improve by minimizing delay in packet delivery, and life time of the network, can be extend by using suitable energy efficient routing protocol.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84464562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-18DOI: 10.11648/J.MLR.20190401.13
A. S. Telang, P. Bedekar
In recent years, control system problems involving non linearities are important concerns in the framework of automation industries. Actuators with non-linear behavior such as saturation, dead zone, relay, backlash etc. may be responsible for poor control performance in the system. The analysis of these non-linearities is an important task for a control system engineer. Moreover the methods of analyzing these non-linearities are time consuming and non-generic. This paper presents simple and systematic approach for analyzing such kind of non-linearities using user-friendly MATLAB tool “Nonlintool”. This tool saves the time as well as provides visual effects for analysis. Main contribution of this paper is to show how user friendly MATLAB tool “Nonlintool” can extensively be used for quicker and wider interpretation of results based on describing function models. The novelty of this paper lies in analyzing all kinds of non-linearities along with their impact on stability of the nonlinear system. The performance has been evaluated for varying conditions of magnitude and gain of the system as well as on various transfer function models. The results of stability analysis, for which only standard transfer function model is considered, are presented here.
{"title":"Systematic Approach Towards Computer Aided Non-Linear Control System Analysis Using Describing Function Models","authors":"A. S. Telang, P. Bedekar","doi":"10.11648/J.MLR.20190401.13","DOIUrl":"https://doi.org/10.11648/J.MLR.20190401.13","url":null,"abstract":"In recent years, control system problems involving non linearities are important concerns in the framework of automation industries. Actuators with non-linear behavior such as saturation, dead zone, relay, backlash etc. may be responsible for poor control performance in the system. The analysis of these non-linearities is an important task for a control system engineer. Moreover the methods of analyzing these non-linearities are time consuming and non-generic. This paper presents simple and systematic approach for analyzing such kind of non-linearities using user-friendly MATLAB tool “Nonlintool”. This tool saves the time as well as provides visual effects for analysis. Main contribution of this paper is to show how user friendly MATLAB tool “Nonlintool” can extensively be used for quicker and wider interpretation of results based on describing function models. The novelty of this paper lies in analyzing all kinds of non-linearities along with their impact on stability of the nonlinear system. The performance has been evaluated for varying conditions of magnitude and gain of the system as well as on various transfer function models. The results of stability analysis, for which only standard transfer function model is considered, are presented here.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73817617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-23DOI: 10.11648/J.MLR.20190401.11
A. S. Telang, P. Bedekar, Ashish K. Duchakke
Communication network is an integral part of an intelligent based fully automated smart grid system. It plays an important role in the framework of the transition towards distribution side of the smart grid system. Power theft, Fault detection, Overloading etc. are some of the important issues on the power distribution networks. To address these issues, a novel Arduino based prototype model “Smart Electricity System” has been proposed in this paper. It includes global system for mobile communication (GSM) for its effective implementation on the distribution network. Moreover another novel feature, Advanced Metering Infrastructure (AMI) is added to the proposed model. This is the key technology deployed on the distribution side of the smart grid system. The Uniqueness of the proposed model lies in the detection of power theft, where the information is sent to MSEB directly via interactive model of GSM 800 and APR voice kit, in the fault detection and its isolation by proper coordination between relay and Aurdino and in the overloading warning. Doing so, not only electricity is conserved but also the safety of living beings and protection of electrical appliances can be achieved effectively. Modern controllers with effective sensors are used to achieve all these issues for greater accuracy.
{"title":"Emerging Smart Grid Communication Technology for Mitigating Power Distribution Network Problems","authors":"A. S. Telang, P. Bedekar, Ashish K. Duchakke","doi":"10.11648/J.MLR.20190401.11","DOIUrl":"https://doi.org/10.11648/J.MLR.20190401.11","url":null,"abstract":"Communication network is an integral part of an intelligent based fully automated smart grid system. It plays an important role in the framework of the transition towards distribution side of the smart grid system. Power theft, Fault detection, Overloading etc. are some of the important issues on the power distribution networks. To address these issues, a novel Arduino based prototype model “Smart Electricity System” has been proposed in this paper. It includes global system for mobile communication (GSM) for its effective implementation on the distribution network. Moreover another novel feature, Advanced Metering Infrastructure (AMI) is added to the proposed model. This is the key technology deployed on the distribution side of the smart grid system. The Uniqueness of the proposed model lies in the detection of power theft, where the information is sent to MSEB directly via interactive model of GSM 800 and APR voice kit, in the fault detection and its isolation by proper coordination between relay and Aurdino and in the overloading warning. Doing so, not only electricity is conserved but also the safety of living beings and protection of electrical appliances can be achieved effectively. Modern controllers with effective sensors are used to achieve all these issues for greater accuracy.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84984811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.11648/J.MLR.20190404.12
Falana Ayodeji, Alegbeleye Tope, Olabanji Pele
The bioconvection Magneto-Hydrodynamics (MHD) flow of nanofluid over a stretching sheet with velocity slip and viscous dissipation is studied. The governing nonlinear partial differential equations of the flow are transformed into a system of coupled nonlinear ordinary differential equations using similarity transformation. These coupled ordinary differential equations are solved using fourth order Runge Kutta-Fehlberg integration method along with shooting technique. Solutions showing the effects of pertinent parameters on the velocity temperature, nanoparticles concentration, skin friction, Nusselt number and microorganism density are illustrated graphically and discussed. It is observed that there is enhancement of the motile microorganism density as thermal slip and Eckert number increase but microorganism density slip parameter have the opposite effect on the microorganism density. It is also found that an increase in Lewis number results in reduction of the volume fraction of nanoparticles and concentration boundary-layer thickness. Brownian motion, Nb and Eckert number, Ec decrease both local Nusselt number and local motile microorganism density but increases local Sherwood number. In addition, as the values of radiation parameter R increase, the thermal boundary layer thickness increases. Finally, thermophoresis parameter, Nt decreases both local Sherwood number, local Nuseselt number and local motile microorganism density. Comparisons of the present result with the previously published results show good agreement.
{"title":"Magneto-hydrodynamics (MHD) Bioconvection Nanofluid Slip Flow over a Stretching Sheet with Thermophoresis, Viscous Dissipation and Brownian Motion","authors":"Falana Ayodeji, Alegbeleye Tope, Olabanji Pele","doi":"10.11648/J.MLR.20190404.12","DOIUrl":"https://doi.org/10.11648/J.MLR.20190404.12","url":null,"abstract":"The bioconvection Magneto-Hydrodynamics (MHD) flow of nanofluid over a stretching sheet with velocity slip and viscous dissipation is studied. The governing nonlinear partial differential equations of the flow are transformed into a system of coupled nonlinear ordinary differential equations using similarity transformation. These coupled ordinary differential equations are solved using fourth order Runge Kutta-Fehlberg integration method along with shooting technique. Solutions showing the effects of pertinent parameters on the velocity temperature, nanoparticles concentration, skin friction, Nusselt number and microorganism density are illustrated graphically and discussed. It is observed that there is enhancement of the motile microorganism density as thermal slip and Eckert number increase but microorganism density slip parameter have the opposite effect on the microorganism density. It is also found that an increase in Lewis number results in reduction of the volume fraction of nanoparticles and concentration boundary-layer thickness. Brownian motion, Nb and Eckert number, Ec decrease both local Nusselt number and local motile microorganism density but increases local Sherwood number. In addition, as the values of radiation parameter R increase, the thermal boundary layer thickness increases. Finally, thermophoresis parameter, Nt decreases both local Sherwood number, local Nuseselt number and local motile microorganism density. Comparisons of the present result with the previously published results show good agreement.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79909918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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