Pub Date : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043201
S. Idris, Usman Mohammed, Jaafaru Sanusi, Sadiq Thomas
The fifth-generation (5G) mobile network is the next paradigm shift in the revolutionary era of the wireless communication technologies that will break the backward compatibility of today’s communication systems. Visible Light Communication (VLC) and Light Fidelity (LiFi) technologies are among the potential candidates that are expected to be utilized in the future 5G networks due to their indoor energy-efficient communications. Realized by Light Emitting Diodes (LEDs), VLC and LiFi possesses a number of prominent features to meet the highly demanding requirements of ultrahigh-speed, massive Multiple-Input Multiple-Output (MIMO) device connectivity, ultra-low-latency, ultra-high reliable and low energy consumption for 5G networks. This paper provides an overview contributions of VLC and LiFi towards 5G networks. Furthermore, we explain how VLC and LiFi can successfully provide effective solutions for the emerging 5G networks.
{"title":"Visible Light Communication: A potential 5G and beyond Communication Technology","authors":"S. Idris, Usman Mohammed, Jaafaru Sanusi, Sadiq Thomas","doi":"10.1109/ICECCO48375.2019.9043201","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043201","url":null,"abstract":"The fifth-generation (5G) mobile network is the next paradigm shift in the revolutionary era of the wireless communication technologies that will break the backward compatibility of today’s communication systems. Visible Light Communication (VLC) and Light Fidelity (LiFi) technologies are among the potential candidates that are expected to be utilized in the future 5G networks due to their indoor energy-efficient communications. Realized by Light Emitting Diodes (LEDs), VLC and LiFi possesses a number of prominent features to meet the highly demanding requirements of ultrahigh-speed, massive Multiple-Input Multiple-Output (MIMO) device connectivity, ultra-low-latency, ultra-high reliable and low energy consumption for 5G networks. This paper provides an overview contributions of VLC and LiFi towards 5G networks. Furthermore, we explain how VLC and LiFi can successfully provide effective solutions for the emerging 5G networks.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958920","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043257
Onyedikachi Vincent Okereke, Fatima Aliyu, Jonathan Dangwaran, Sadiq Thomas, Biliyok Akawu Shekari, Hussein U. Suleiman
As the world develops it looks for a greener way to produce energy. Here we take a look at the present and previous ways in which Nigeria produces energy and we compare with a particular alternative renewable source, solar photovoltaic system. Solar photovoltaic system uses a method of photoelectric effect in order to convert the energy from the sun into electricity by absorbing and utilizing it. We go further in this project by reviewing some calculations to see how solar energy compares to other forms of electricity supply over a period of 20 years. Finally, reasons were given why it is preferable to use solar PV systems as compared to other forms.
{"title":"Using Solar Photovoltaic Systems to Significantly Reduce Power Production Problems in Nigeria and Create a Greener Environment","authors":"Onyedikachi Vincent Okereke, Fatima Aliyu, Jonathan Dangwaran, Sadiq Thomas, Biliyok Akawu Shekari, Hussein U. Suleiman","doi":"10.1109/ICECCO48375.2019.9043257","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043257","url":null,"abstract":"As the world develops it looks for a greener way to produce energy. Here we take a look at the present and previous ways in which Nigeria produces energy and we compare with a particular alternative renewable source, solar photovoltaic system. Solar photovoltaic system uses a method of photoelectric effect in order to convert the energy from the sun into electricity by absorbing and utilizing it. We go further in this project by reviewing some calculations to see how solar energy compares to other forms of electricity supply over a period of 20 years. Finally, reasons were given why it is preferable to use solar PV systems as compared to other forms.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788562","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043289
H. Bello, O. Oyeleke, A. D. Usman, T. Bello, Idris Muhammad, O. S. Zakariyya
The two dimensional monolithic microwave integrated circuits (2D MMIC) are mainly implemented in a planar fashion and use microstrip design based technology. At microwave frequency and above, they would require a large amount of passive circuitry therefore occupying a great deal of space (area). Furthermore the 2D MMIC is associated with some disadvantages ranging from the use of very thin substrate which makes it less reliable, to very delicate substrate due to the use of via-hole technology, coupling issue and high cost due to large area it occupies. To solve these problems a three-dimensional multilayer technique 3D MMIC was used. The design of the 3D MMIC is based on coplanar waveguide (CPW), in this design the signal is protected by the two grounds on both side, the circuit becomes more compact, cost-effective and with improved performance. This research work was aimed at the design, modelling and investigation of a GaAs based multilayer compact 3D MMIC transmission line. Different transmission lines were designed and modelled using Agilent’s Advanced Design System (ADS) and their Sparameters were extracted using Electromagnetic (EM) simulator momentum.
{"title":"Modelling And Realization of a Compact CPW Transmission Lines Using 3D Mmics Technology in ADS Momentum","authors":"H. Bello, O. Oyeleke, A. D. Usman, T. Bello, Idris Muhammad, O. S. Zakariyya","doi":"10.1109/ICECCO48375.2019.9043289","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043289","url":null,"abstract":"The two dimensional monolithic microwave integrated circuits (2D MMIC) are mainly implemented in a planar fashion and use microstrip design based technology. At microwave frequency and above, they would require a large amount of passive circuitry therefore occupying a great deal of space (area). Furthermore the 2D MMIC is associated with some disadvantages ranging from the use of very thin substrate which makes it less reliable, to very delicate substrate due to the use of via-hole technology, coupling issue and high cost due to large area it occupies. To solve these problems a three-dimensional multilayer technique 3D MMIC was used. The design of the 3D MMIC is based on coplanar waveguide (CPW), in this design the signal is protected by the two grounds on both side, the circuit becomes more compact, cost-effective and with improved performance. This research work was aimed at the design, modelling and investigation of a GaAs based multilayer compact 3D MMIC transmission line. Different transmission lines were designed and modelled using Agilent’s Advanced Design System (ADS) and their Sparameters were extracted using Electromagnetic (EM) simulator momentum.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"2 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386560","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043234
M. Muhammad, R. Prasad, M. Fonkam, H. Umar
Protein secondary structure prediction plays a fundamental role in bioinformatics. Extracting valuable information from big biological data that can give an insight into understanding the 3-dimensional protein structure and later learn its biological function is quit challenging. In the past decade, many machine learning approaches have been applied in bioinformatics to extract knowledge from protein data. In this paper, a critical review on the recent development in machine learning based protein secondary structure prediction methods are presented. Next generation method (Deep learning) is also introduced to provide interested researchers with first-hand information on the future trend in this field. Although many approaches have yielded an appreciable prediction performance, machine learning approaches are far from fulfilling its potentials in biological research because of the difficulty in interpreting how particular model feature correlate with input features to yield that desired output in biological perspective. Therefore, this study has found that several further improvements are possible with the emergence of deep learning techniques.
{"title":"Review of Advances in Machine Learning Based Protein Secondary Structure Prediction","authors":"M. Muhammad, R. Prasad, M. Fonkam, H. Umar","doi":"10.1109/ICECCO48375.2019.9043234","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043234","url":null,"abstract":"Protein secondary structure prediction plays a fundamental role in bioinformatics. Extracting valuable information from big biological data that can give an insight into understanding the 3-dimensional protein structure and later learn its biological function is quit challenging. In the past decade, many machine learning approaches have been applied in bioinformatics to extract knowledge from protein data. In this paper, a critical review on the recent development in machine learning based protein secondary structure prediction methods are presented. Next generation method (Deep learning) is also introduced to provide interested researchers with first-hand information on the future trend in this field. Although many approaches have yielded an appreciable prediction performance, machine learning approaches are far from fulfilling its potentials in biological research because of the difficulty in interpreting how particular model feature correlate with input features to yield that desired output in biological perspective. Therefore, this study has found that several further improvements are possible with the emergence of deep learning techniques.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114567182","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043276
Sunday Barde Danladi, Faruku Umar Ambursa
Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.
{"title":"DyRED: An Enhanced Random Early Detection Based on a new Adaptive Congestion Control","authors":"Sunday Barde Danladi, Faruku Umar Ambursa","doi":"10.1109/ICECCO48375.2019.9043276","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043276","url":null,"abstract":"Over the years congestion has been a major issue affecting the internet leading to an increase in packet loss and delay. Researchers have proposed different algorithms to address the issue of congestion from Drop Tail, Early Random Drop to Active Queue Management (AQM). Random Early Detection (RED) is the first Active Queue Management (AQM) technique that was developed to support transport-layer congestion and decrease the impacts of network congestion on the router buffer. The idea behind RED is to sense and detect incipient congestion early and notify connections of congestion either by dropping packets arriving or by reducing its sending rate. Although various other AQM techniques have been proposed by researchers, RED is still the most commonly used algorithm for congestion avoidance and researches is still ongoing to enhance the performance of RED. In this paper, we have developed an extension to RED to address the limitation of RED and the algorithm is then compared with RED under various network scenarios. The results of the evaluation shows that the new method has outperformed RED.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302565","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043199
Cemil Turan, A. Aitimov, B. Kynabay, Aimoldir Aldabergen
One of the most popular tool implemented in face recognition issues is Principal Component Analysis (PCA) which is successfully used in machine learning and data analysis. However, if the images are not regular with some factors that affect the image recognition accuracy such as variation of facial expressions, different poses or lighting problems, this technique may show some deficiencies. In this work, different kinds of methods were implemented by combining different preprocessing techniques to evaluate and compare them under different lighting conditions of images. In order to have the same lighting conditions for every image, the methods were applied to them after PCA processing. As a result, the face recognition accuracy was improved by means of implementing the techniques separately or in combination.
{"title":"An Enhanced Face Recognition Method for Lighting Problem","authors":"Cemil Turan, A. Aitimov, B. Kynabay, Aimoldir Aldabergen","doi":"10.1109/ICECCO48375.2019.9043199","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043199","url":null,"abstract":"One of the most popular tool implemented in face recognition issues is Principal Component Analysis (PCA) which is successfully used in machine learning and data analysis. However, if the images are not regular with some factors that affect the image recognition accuracy such as variation of facial expressions, different poses or lighting problems, this technique may show some deficiencies. In this work, different kinds of methods were implemented by combining different preprocessing techniques to evaluate and compare them under different lighting conditions of images. In order to have the same lighting conditions for every image, the methods were applied to them after PCA processing. As a result, the face recognition accuracy was improved by means of implementing the techniques separately or in combination.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123467664","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043235
R. Jantayev, Y. Amirgaliyev
One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).
{"title":"Improved Handwritten Digit Recognition method using Deep Learning Algorithm","authors":"R. Jantayev, Y. Amirgaliyev","doi":"10.1109/ICECCO48375.2019.9043235","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043235","url":null,"abstract":"One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129598435","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043243
O. Gurbuz, Hatice Gurbuz, Isa Muslu
This paper furnishes a general solution about the linear equation system $boldsymbol{Ax}=boldsymbol{g}$. The analytic solutions to the problem of finding the vector $boldsymbol{x}$, from among the general solution set of the system if it is consistent, and from among the least squares solution set of the system if it is inconsistent, such that the norm of $boldsymbol{x}-boldsymbol{x}_{mathbf{0}}$ is minimum for a given vector $boldsymbol{x}_{mathbf{0}}$ are established. For inverse matrix of A, it is used generalized inverse (Moore-Penrose inverse) by using algorithm and Maple. Analytic results, we obtained are satisfied by using algorithm with numerical examples.
{"title":"General Solutions of Consistent and Inconsistent Linear Equation Systems Via Maple","authors":"O. Gurbuz, Hatice Gurbuz, Isa Muslu","doi":"10.1109/ICECCO48375.2019.9043243","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043243","url":null,"abstract":"This paper furnishes a general solution about the linear equation system $boldsymbol{Ax}=boldsymbol{g}$. The analytic solutions to the problem of finding the vector $boldsymbol{x}$, from among the general solution set of the system if it is consistent, and from among the least squares solution set of the system if it is inconsistent, such that the norm of $boldsymbol{x}-boldsymbol{x}_{mathbf{0}}$ is minimum for a given vector $boldsymbol{x}_{mathbf{0}}$ are established. For inverse matrix of A, it is used generalized inverse (Moore-Penrose inverse) by using algorithm and Maple. Analytic results, we obtained are satisfied by using algorithm with numerical examples.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130956127","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043226
Buhari U. Umar, M. B. Muazu, J. Kolo, J. Agajo, I. D. Matthew
Epilepsy affects about 1 % of the contemporary population and sternly reduces the wellbeing of its patients. It is a neurological disorder of the central nervous system that is usually characterized by sudden seizure. The possibility of detecting and predicting epileptic seizure has engrossed mankind already for over 35 years. One of the main tools in detecting and predicting the Epilepsy seizures are the Electroencephalograms (EEG), which record the brain activity by measuring the extracellular field potentials due to neuronal discharges. This EEG is quite difficult and complex to interpret even by an expert neurologist, even so, it is time-consuming, often challenging, sets in human error as well as delay in treatment. In this research, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and Artificial Neural Network (ANN) for automatic seizure detection in EEG is proposed called GOA-ANN approach. Nine parameters (mean value, variance value, Standard deviation value, energy value, entropy value and maximum value, RMS value, kurtosis and skewness) were extracted and used as the features to train the ANN classifiers. GOA was used for selecting the best features in order to obtain an effective EEG classification. In comparison with other research, the result was able to detect epilepsy and enhance the diagnosis of epilepsy with an accuracy of 98.4%. The research was also compared with Artificial Neural Network using Feed-Forward network, the result shows that GOA_ANN approach performed better.
{"title":"Epilepsy Detection Using Artificial Neural Network and Grasshopper Optimization Algorithm (GOA)","authors":"Buhari U. Umar, M. B. Muazu, J. Kolo, J. Agajo, I. D. Matthew","doi":"10.1109/ICECCO48375.2019.9043226","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043226","url":null,"abstract":"Epilepsy affects about 1 % of the contemporary population and sternly reduces the wellbeing of its patients. It is a neurological disorder of the central nervous system that is usually characterized by sudden seizure. The possibility of detecting and predicting epileptic seizure has engrossed mankind already for over 35 years. One of the main tools in detecting and predicting the Epilepsy seizures are the Electroencephalograms (EEG), which record the brain activity by measuring the extracellular field potentials due to neuronal discharges. This EEG is quite difficult and complex to interpret even by an expert neurologist, even so, it is time-consuming, often challenging, sets in human error as well as delay in treatment. In this research, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and Artificial Neural Network (ANN) for automatic seizure detection in EEG is proposed called GOA-ANN approach. Nine parameters (mean value, variance value, Standard deviation value, energy value, entropy value and maximum value, RMS value, kurtosis and skewness) were extracted and used as the features to train the ANN classifiers. GOA was used for selecting the best features in order to obtain an effective EEG classification. In comparison with other research, the result was able to detect epilepsy and enhance the diagnosis of epilepsy with an accuracy of 98.4%. The research was also compared with Artificial Neural Network using Feed-Forward network, the result shows that GOA_ANN approach performed better.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131354062","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 : 2019-12-01DOI: 10.1109/ICECCO48375.2019.9043247
A. P. Adedigba, A. R. Zubair, A. Aibinu, Steve A. Adeshina, Olumide Okubadejo, T. A. Folorunso
Diabetes Mellitus (DM) is a disease of the glucose-insulin regulatory system where the insulin producing beta-cells has been damaged thereby producing none to very little insulin leaving the body with no means of regulating glucose. DM has high socioeconomic costs because it needs long term monitoring and individual care to prevent or decrease complications. Uncontrolled or poorly controlled diabetes lead to evolution or development of microvascular and macrovascular complications. It has been shown that adequate or even tight glycaemic control can prevent or delay complications and finally can reduce these complications. One of this glycaemic control is insulin therapy, meanwhile, non-adherence to the therapy due to its sever pain is prevalent among patients. In this paper, a review of research efforts towards the development of automatic insulin injection from control engineering perspective is presented. The reviewed techniques are basically closed loop approach, which include PID controllers, Model Predictive Controllers and Adaptive Controller techniques using machine learning approaches.
{"title":"Towards the Development of Intelligent Insulin Injection Controller For Diabetic Patients","authors":"A. P. Adedigba, A. R. Zubair, A. Aibinu, Steve A. Adeshina, Olumide Okubadejo, T. A. Folorunso","doi":"10.1109/ICECCO48375.2019.9043247","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043247","url":null,"abstract":"Diabetes Mellitus (DM) is a disease of the glucose-insulin regulatory system where the insulin producing beta-cells has been damaged thereby producing none to very little insulin leaving the body with no means of regulating glucose. DM has high socioeconomic costs because it needs long term monitoring and individual care to prevent or decrease complications. Uncontrolled or poorly controlled diabetes lead to evolution or development of microvascular and macrovascular complications. It has been shown that adequate or even tight glycaemic control can prevent or delay complications and finally can reduce these complications. One of this glycaemic control is insulin therapy, meanwhile, non-adherence to the therapy due to its sever pain is prevalent among patients. In this paper, a review of research efforts towards the development of automatic insulin injection from control engineering perspective is presented. The reviewed techniques are basically closed loop approach, which include PID controllers, Model Predictive Controllers and Adaptive Controller techniques using machine learning approaches.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131379219","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：