Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data.the literature on the field offers a wide spectrum of algorithms and applications.however, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application. This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning.for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms.the research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.
{"title":"Analysis of Common Supervised Learning Algorithms Through Application","authors":"Palak Narula","doi":"10.5121/acii.2023.10303","DOIUrl":"https://doi.org/10.5121/acii.2023.10303","url":null,"abstract":"Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data.the literature on the field offers a wide spectrum of algorithms and applications.however, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application. This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning.for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms.the research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.","PeriodicalId":337615,"journal":{"name":"Advanced Computational Intelligence: An International Journal (ACII)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133787210","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}
NeuMAN represents a new model for computational cognition synthesizing important results across AI, psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize reactions to expected events, and increase responsiveness to high-value information. These systems exhibit both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human intelligence. It will likely become the principal mechanism for engineering intelligent systems.
{"title":"Neural Model-Applying Network (Neuman): A New Basis for Computational Cognition","authors":"Frederick Roth","doi":"10.5121/acii.2023.10301","DOIUrl":"https://doi.org/10.5121/acii.2023.10301","url":null,"abstract":"NeuMAN represents a new model for computational cognition synthesizing important results across AI, psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize reactions to expected events, and increase responsiveness to high-value information. These systems exhibit both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human intelligence. It will likely become the principal mechanism for engineering intelligent systems.","PeriodicalId":337615,"journal":{"name":"Advanced Computational Intelligence: An International Journal (ACII)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122178576","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}
Lyndel Jean L. Pagaduan, Jhobert G.Portolazo, Jounariz Xavier D. Delfin, John Pablo O. Dela Cruz, Micah Bambie O. Estanda
Energy shortage is a global challenge with significant implications for economies, societies, and the environment, including the Philippines. Promoting energy conservation in households is an effective approach to address this issue. In the Municipality of Midsayap, North Cotabato, Philippines, unmonitored energy consumption leads to excessive energy usage in households. To address this problem, this paper aims to research, build, test and implement a Real-Time Energy Consumption Monitoring (RECM) device using IoT technology. The RECM device, equipped with an SCT013 current sensor, enables real-time 24/7 monitoring of energy consumption. The monitored data is displayed in graphical and numerical formats using the Thing Speak cloud storage service. The RECM device was deployed in households, and a survey was conducted to evaluate its functionality and effectiveness. The results indicate that the design of the RECM device is a highly useful and efficient tool for real-time energy consumption monitoring. This paper provides circuit diagrams, wiring diagrams, and the list of materials used to develop the Real-Time Energy Consumption Monitoring (RECM) device using IoT.
{"title":"The Development of Real-Time Energy Consumption Monitoring using IoT","authors":"Lyndel Jean L. Pagaduan, Jhobert G.Portolazo, Jounariz Xavier D. Delfin, John Pablo O. Dela Cruz, Micah Bambie O. Estanda","doi":"10.5121/acii.2023.10302","DOIUrl":"https://doi.org/10.5121/acii.2023.10302","url":null,"abstract":"Energy shortage is a global challenge with significant implications for economies, societies, and the environment, including the Philippines. Promoting energy conservation in households is an effective approach to address this issue. In the Municipality of Midsayap, North Cotabato, Philippines, unmonitored energy consumption leads to excessive energy usage in households. To address this problem, this paper aims to research, build, test and implement a Real-Time Energy Consumption Monitoring (RECM) device using IoT technology. The RECM device, equipped with an SCT013 current sensor, enables real-time 24/7 monitoring of energy consumption. The monitored data is displayed in graphical and numerical formats using the Thing Speak cloud storage service. The RECM device was deployed in households, and a survey was conducted to evaluate its functionality and effectiveness. The results indicate that the design of the RECM device is a highly useful and efficient tool for real-time energy consumption monitoring. This paper provides circuit diagrams, wiring diagrams, and the list of materials used to develop the Real-Time Energy Consumption Monitoring (RECM) device using IoT.","PeriodicalId":337615,"journal":{"name":"Advanced Computational Intelligence: An International Journal (ACII)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127262","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}
In this paper, an accurate numerical method is presented to find the numerical solution of the singular initial value problems. The second-order singular initial value problem under consideration is transferred into a first-order system of initial value problems, and then it can be solved by using the fifth-order Runge Kutta method. The stability and convergence analysis is studied. The effectiveness of the proposed methods is confirmed by solving three model examples, and the obtained approximate solutions are compared with the existing methods in the literature. Thus, the fifth-order Runge-Kutta method is an accurate numerical method for solving the singular initial value problems.
{"title":"Accurate Numerical Method for Singular Initial-Value Problems","authors":"T. A. Bullo, G. Duressa, G. G. Kiltu","doi":"10.5121/acii.2021.8301","DOIUrl":"https://doi.org/10.5121/acii.2021.8301","url":null,"abstract":"In this paper, an accurate numerical method is presented to find the numerical solution of the singular initial value problems. The second-order singular initial value problem under consideration is transferred into a first-order system of initial value problems, and then it can be solved by using the fifth-order Runge Kutta method. The stability and convergence analysis is studied. The effectiveness of the proposed methods is confirmed by solving three model examples, and the obtained approximate solutions are compared with the existing methods in the literature. Thus, the fifth-order Runge-Kutta method is an accurate numerical method for solving the singular initial value problems.","PeriodicalId":337615,"journal":{"name":"Advanced Computational Intelligence: An International Journal (ACII)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133790940","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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