The technology of knowledge base remote design of the smart fuzzy controllers with the application of the "Soft / quantum computing optimizer" toolkit software developed. The possibility of the transmission and communication the knowledge base using remote connection to the control object considered. Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies. Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk. As examples, two different models of robots described (mobile manipulator and (“cart-pole” system) inverted pendulum). A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented. The ability to connect and work with a physical model of control object without using than mathematical model demonstrated. The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers. It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge. Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA (at the first stage for the cooling system of superconducted magnets) is discussed. The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft / quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems. The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.
{"title":"Quantum Algorithm of Imperfect KB Self-organization. Pt II: Robotic Control with Remote Knowledge Base Exchange","authors":"A. Reshetnikov, S. Ulyanov","doi":"10.30564/aia.v3i2.3849","DOIUrl":"https://doi.org/10.30564/aia.v3i2.3849","url":null,"abstract":"The technology of knowledge base remote design of the smart fuzzy controllers with the application of the \"Soft / quantum computing optimizer\" toolkit software developed. The possibility of the transmission and communication the knowledge base using remote connection to the control object considered. Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies. Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk. As examples, two different models of robots described (mobile manipulator and (“cart-pole” system) inverted pendulum). A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented. The ability to connect and work with a physical model of control object without using than mathematical model demonstrated. The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers. It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge. Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA (at the first stage for the cooling system of superconducted magnets) is discussed. The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft / quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems. The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"23 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81104068","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}
What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms.
{"title":"A New Approach of Intelligent Data Retrieval Paradigm","authors":"F. Al-akashi, D. Inkpen","doi":"10.30564/aia.v3i2.3219","DOIUrl":"https://doi.org/10.30564/aia.v3i2.3219","url":null,"abstract":"What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"11 5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91201402","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 : 2021-04-26DOI: 10.14201/adcaij2021102197208
Pervez Ahmad
Blockchain Technology (BCT) is one of many other emerging technologies that were introduced in the past several years & carried loads of potential utilizing technological development. This paper describes in detail the progress made in Blockchain Technology. Keeping this in mind, some fields have been determined in which their efficiency and modernization can be promoted by using Blockchain Technology. It also describes the problems and challenges faced in implementing Blockchain Technology. Researchers are performing studies vigorously to discover all the possible proficiencies of Blockchain Technology with some of them having faith in the Blockchain being vital for a de-centralized civilization. This paper provides an overview of Blockchain’s applications.
{"title":"A Review on Blockchain’s Applications and Implementations","authors":"Pervez Ahmad","doi":"10.14201/adcaij2021102197208","DOIUrl":"https://doi.org/10.14201/adcaij2021102197208","url":null,"abstract":"Blockchain Technology (BCT) is one of many other emerging technologies that were introduced in the past several years & carried loads of potential utilizing technological development. This paper describes in detail the progress made in Blockchain Technology. Keeping this in mind, some fields have been determined in which their efficiency and modernization can be promoted by using Blockchain Technology. It also describes the problems and challenges faced in implementing Blockchain Technology. Researchers are performing studies vigorously to discover all the possible proficiencies of Blockchain Technology with some of them having faith in the Blockchain being vital for a de-centralized civilization. This paper provides an overview of Blockchain’s applications.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88057652","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 : 2021-03-26DOI: 10.14201/adcaij2021102183196
A. Khan, S. Zubair, Samreen Khan
Neurodegenerative diseases such as Alzheimer’s disease and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning is revolu-tionising almost all domains of our life, including the clinical system. The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of Alzheimer’s and demen-tia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area. The ob-jective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having Alzheimer’s disease and dementia. The study was based on two diverse datasets. The results have been discussed employing seven perfor-mance evaluation measures i.e. accuracy, precision, recall, F-measure, Re-ceiver Operating Characteristic (ROC) area, Kappa statistic, and Root Mean Squared Error (RMSE). Also, a comprehensive performance analysis has been carried out later in the study.
{"title":"A Comprehensive Performance Analysis of Neurodegenerative diseases Incidence based on Epidemiological Study in the Female subjects over varied data","authors":"A. Khan, S. Zubair, Samreen Khan","doi":"10.14201/adcaij2021102183196","DOIUrl":"https://doi.org/10.14201/adcaij2021102183196","url":null,"abstract":"Neurodegenerative diseases such as Alzheimer’s disease and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning is revolu-tionising almost all domains of our life, including the clinical system. The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of Alzheimer’s and demen-tia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area. The ob-jective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having Alzheimer’s disease and dementia. The study was based on two diverse datasets. The results have been discussed employing seven perfor-mance evaluation measures i.e. accuracy, precision, recall, F-measure, Re-ceiver Operating Characteristic (ROC) area, Kappa statistic, and Root Mean Squared Error (RMSE). Also, a comprehensive performance analysis has been carried out later in the study.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"32 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84855580","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 : 2021-03-24DOI: 10.14201/adcaij2021102165182
MuaadhAbdo Al Sabri
In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity. However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.
{"title":"Hybrid Measuring the Similarity Value Based on Genetic Algorithm for Improving Prediction in A Collaborative Filtering Recommendation System.","authors":"MuaadhAbdo Al Sabri","doi":"10.14201/adcaij2021102165182","DOIUrl":"https://doi.org/10.14201/adcaij2021102165182","url":null,"abstract":"In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity. However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"212 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80231046","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 : 2021-03-01DOI: 10.14201/ADCAIJ2021102123136
Zehra Karapinar Senturk, M. Bakay
Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG data obtained from arm through sensors helps to understand hand gestures. For this work, hand gesture data were taken from UCI2019 EMG dataset obtained from MYO thalmic armband were classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were preferred for comparison based on several performance metrics which are accuracy, precision, sensitivity, specicity, classication error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. The splitting ratio in the classication was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in testing phase of the classier. NB was found to be the best among other methods because of high accuracy (96.43%) and sensitivity (96.43%) and the lowest RMSE (0.189). Considering the results of the performance parameters, it can be said that this study recognizes and classies seven hand gestures successfully in comparison with the literature.
{"title":"Machine Learning Based Hand Gesture Recognition via EMG Data","authors":"Zehra Karapinar Senturk, M. Bakay","doi":"10.14201/ADCAIJ2021102123136","DOIUrl":"https://doi.org/10.14201/ADCAIJ2021102123136","url":null,"abstract":"Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG data obtained from arm through sensors helps to understand hand gestures. For this work, hand gesture data were taken from UCI2019 EMG dataset obtained from MYO thalmic armband were classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were preferred for comparison based on several performance metrics which are accuracy, precision, sensitivity, specicity, classication error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. The splitting ratio in the classication was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in testing phase of the classier. NB was found to be the best among other methods because of high accuracy (96.43%) and sensitivity (96.43%) and the lowest RMSE (0.189). Considering the results of the performance parameters, it can be said that this study recognizes and classies seven hand gestures successfully in comparison with the literature.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"8 4 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78356637","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}
The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.
{"title":"Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds","authors":"S. Ulyanov","doi":"10.30564/aia.v3i2.3171","DOIUrl":"https://doi.org/10.30564/aia.v3i2.3171","url":null,"abstract":"The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83029849","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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