Pub Date : 2024-07-03DOI: 10.37394/23205.2024.23.16
K. K. Akula, Maura Marcucci, Romain Jouffroy, Farzad Arabikhan, Raheleh Jafari, Monica Akula, Alexander E. Gegov
One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.
{"title":"Medical Image Classification using a Many to Many Relation, Multilayered Fuzzy Systems and AI","authors":"K. K. Akula, Maura Marcucci, Romain Jouffroy, Farzad Arabikhan, Raheleh Jafari, Monica Akula, Alexander E. Gegov","doi":"10.37394/23205.2024.23.16","DOIUrl":"https://doi.org/10.37394/23205.2024.23.16","url":null,"abstract":"One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"82 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682963","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 : 2024-07-01DOI: 10.37394/23205.2024.23.14
Ioannis Syllaidopoulos
The present research aims to investigate whether Chaos Theory can be combined with Machine Learning and Natural Language Processing to apply these techniques to Open Source Intelligence (OSINT) analysis. Describing the role of OSINT in different domains and highlighting chaos as a valuable resource for information gathering, the study highlights that the substantial volume, swift velocity, and extensive variety of open-source data pose significant challenges. To address these challenges it is proposed to apply elements of Chaos Theory and advanced computational methods to open-source data. Key concepts from Chaos Theory that will be explored are the ‘Butterfly Effect’, and ‘Strange Attractors’, attempting to demonstrate that chaotic aspects of data can be exploited and transformed into dynamic and powerful sources of information. To support the above, the research includes a case study that exploits and analyses data from Reddit posts and concludes that recognizing and exploiting the dynamic interaction between order and chaos places Chaos Theory not only complementary but as a foundational stone of the overall OSINT toolkit, in the hands of intelligence analysts.
{"title":"Chaos in Order: Applying ML, NLP, and Chaos Theory in Open Source Intelligence for Counter-Terrorism","authors":"Ioannis Syllaidopoulos","doi":"10.37394/23205.2024.23.14","DOIUrl":"https://doi.org/10.37394/23205.2024.23.14","url":null,"abstract":"The present research aims to investigate whether Chaos Theory can be combined with Machine Learning and Natural Language Processing to apply these techniques to Open Source Intelligence (OSINT) analysis. Describing the role of OSINT in different domains and highlighting chaos as a valuable resource for information gathering, the study highlights that the substantial volume, swift velocity, and extensive variety of open-source data pose significant challenges. To address these challenges it is proposed to apply elements of Chaos Theory and advanced computational methods to open-source data. Key concepts from Chaos Theory that will be explored are the ‘Butterfly Effect’, and ‘Strange Attractors’, attempting to demonstrate that chaotic aspects of data can be exploited and transformed into dynamic and powerful sources of information. To support the above, the research includes a case study that exploits and analyses data from Reddit posts and concludes that recognizing and exploiting the dynamic interaction between order and chaos places Chaos Theory not only complementary but as a foundational stone of the overall OSINT toolkit, in the hands of intelligence analysts.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696136","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 : 2024-07-01DOI: 10.37394/23205.2024.23.13
V. Riznyk
The objectives of the combinatorial optimization of engineering systems based on diagrammatic design are enhancing technical indices of the systems with spatially or temporally distributed elements (e.g., radio-antenna arrays) concerning resolving ability, positioning precision, transmission speed, and performance reliability, using the graphical performance of appropriate algebraic models of the system, such as cyclic difference sets, Galois fields and “Ideal Ring Bundles”. The diagrammatic design provides configuring systems with a smaller number of elements than at present, while upholding or improving on the other significant operating quality indices of the system.
{"title":"Combinatorial Optimization of Engineering Systems based on Diagrammatic Design","authors":"V. Riznyk","doi":"10.37394/23205.2024.23.13","DOIUrl":"https://doi.org/10.37394/23205.2024.23.13","url":null,"abstract":"The objectives of the combinatorial optimization of engineering systems based on diagrammatic design are enhancing technical indices of the systems with spatially or temporally distributed elements (e.g., radio-antenna arrays) concerning resolving ability, positioning precision, transmission speed, and performance reliability, using the graphical performance of appropriate algebraic models of the system, such as cyclic difference sets, Galois fields and “Ideal Ring Bundles”. The diagrammatic design provides configuring systems with a smaller number of elements than at present, while upholding or improving on the other significant operating quality indices of the system.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706205","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 : 2024-07-01DOI: 10.37394/23205.2024.23.15
A. Staines
Symmetry is a fundamental mathematical property applicable to the description of various shapes both geometrical and representational. Symmetry is central to understanding the nature of various objects. It can be used as a simplifying principle when structures are created. Petri nets are widely covered formalisms, useful for modeling different types of computer systems or computer configurations. Different forms of Petri nets exist along with several forms of representation. Petri nets are useful for i) deterministic and ii) non-deterministic modeling. The aspect of symmetry in Petri nets requires in-depth treatment that is often overlooked. Symmetry is a fundamental property found in Petri nets. This work tries to briefly touch on these properties and explain them with simple examples. Hopefully, readers will be inspired to carry out more work in this direction.
对称是一种基本的数学特性，适用于描述各种几何形状和表象形状。对称是理解各种物体性质的核心。在创建结构时，它可以用作简化原则。Petri 网是一种广泛应用的形式主义，可用于对不同类型的计算机系统或计算机配置进行建模。不同形式的 Petri 网有多种表示方法。Petri 网适用于 i) 确定性建模和 ii) 非确定性建模。Petri 网中的对称性需要深入处理，而这一点往往被忽视。对称性是 Petri 网的一个基本属性。本著作试图简要地介绍这些特性，并通过简单的例子加以解释。希望读者能从中受到启发，在这方面开展更多的工作。
{"title":"Aspects of Symmetry in Petri Nets","authors":"A. Staines","doi":"10.37394/23205.2024.23.15","DOIUrl":"https://doi.org/10.37394/23205.2024.23.15","url":null,"abstract":"Symmetry is a fundamental mathematical property applicable to the description of various shapes both geometrical and representational. Symmetry is central to understanding the nature of various objects. It can be used as a simplifying principle when structures are created. Petri nets are widely covered formalisms, useful for modeling different types of computer systems or computer configurations. Different forms of Petri nets exist along with several forms of representation. Petri nets are useful for i) deterministic and ii) non-deterministic modeling. The aspect of symmetry in Petri nets requires in-depth treatment that is often overlooked. Symmetry is a fundamental property found in Petri nets. This work tries to briefly touch on these properties and explain them with simple examples. Hopefully, readers will be inspired to carry out more work in this direction.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690646","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 : 2024-06-12DOI: 10.37394/23205.2024.23.10
Dimitris Karydas, Helen C. Leligou
Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficienc
{"title":"Federated Learning: Attacks and Defenses, Rewards, Energy Efficiency: Past, Present and Future","authors":"Dimitris Karydas, Helen C. Leligou","doi":"10.37394/23205.2024.23.10","DOIUrl":"https://doi.org/10.37394/23205.2024.23.10","url":null,"abstract":"Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficienc","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"51 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355337","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 : 2024-05-13DOI: 10.37394/23205.2024.23.9
Elif Akkaya, Safiye Turgay
The importance of data mining is growing rapidly, so the comparison of data mining tools has become important. Data mining is the process of extracting valuable data from large data to meet the need to see relationships between data and to make predictions when necessary. This study delves into the dynamic realm of data mining, presenting a comprehensive comparison of prominent data mining tools through the lens of the decision tree algorithm. The research focuses on the application of these tools to the BankMarketing dataset, a rich repository of financial interactions. The objective is to unveil the efficacy and nuances of each tool in the context of predictive modelling, emphasizing key metrics such as accuracy, precision, recall, and F1-score. Through meticulous experimentation and evaluation, this analysis sheds light on the distinct strengths and limitations of each data-mining tool, providing valuable insights for practitioners and researchers in the field. The findings contribute to a deeper understanding of tool selection considerations and pave the way for enhanced decision-making in data mining applications. Classification is a data mining task that learns from a collection of data in order to accurately predict new cases. The dataset used in this study is the Bank Marketing dataset from the UCI machine-learning repository. The bank marketing dataset contains 45211 instances and 17 features. The bank marketing dataset is related to the direct marketing campaigns (phone calls) of a Portuguese banking institution and the classification objective is to predict whether customers will subscribe to a deposit (variable y) in a period of time. To make the classification, the machine learning technique can be used. In this study, the Decision Tree classification algorithm is used. Knime, Orange, Tanagra, Rapidminerve, Weka yield mining tools are used to analyse the classification algorithm.
{"title":"Unveiling the Power: A Comparative Analysis of Data Mining Tools through Decision Tree Classification on the Bank Marketing Dataset","authors":"Elif Akkaya, Safiye Turgay","doi":"10.37394/23205.2024.23.9","DOIUrl":"https://doi.org/10.37394/23205.2024.23.9","url":null,"abstract":"The importance of data mining is growing rapidly, so the comparison of data mining tools has become important. Data mining is the process of extracting valuable data from large data to meet the need to see relationships between data and to make predictions when necessary. This study delves into the dynamic realm of data mining, presenting a comprehensive comparison of prominent data mining tools through the lens of the decision tree algorithm. The research focuses on the application of these tools to the BankMarketing dataset, a rich repository of financial interactions. The objective is to unveil the efficacy and nuances of each tool in the context of predictive modelling, emphasizing key metrics such as accuracy, precision, recall, and F1-score. Through meticulous experimentation and evaluation, this analysis sheds light on the distinct strengths and limitations of each data-mining tool, providing valuable insights for practitioners and researchers in the field. The findings contribute to a deeper understanding of tool selection considerations and pave the way for enhanced decision-making in data mining applications. Classification is a data mining task that learns from a collection of data in order to accurately predict new cases. The dataset used in this study is the Bank Marketing dataset from the UCI machine-learning repository. The bank marketing dataset contains 45211 instances and 17 features. The bank marketing dataset is related to the direct marketing campaigns (phone calls) of a Portuguese banking institution and the classification objective is to predict whether customers will subscribe to a deposit (variable y) in a period of time. To make the classification, the machine learning technique can be used. In this study, the Decision Tree classification algorithm is used. Knime, Orange, Tanagra, Rapidminerve, Weka yield mining tools are used to analyse the classification algorithm.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"33 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984054","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 : 2024-04-15DOI: 10.37394/23205.2024.23.7
V. Riznyk
In this paper, we regard designing systems based on remarkable geometric properties of space, namely valuable rotational symmetry and asymmetry harmonious, using schematic and diagrammatic presentations of the systems. Moreover, the relationships are a way to comprehend original information to serve as a source of research and designing the systems. The objective of the future methodology is the advanced study of spatial geometric harmony as profiting information for expansion fundamental and applied researches for optimal solutions of technological problems in systems engineering. These systems engineering designs make it possible to improve the quality indices of devices or systems concerning performance reliability, code immunity, and the other operating indices of the systems. As examples, both up to 25% errors of lengths correcting code and high-speed self-error-correcting vector data code formed under a toroidal coordinate system are presented.
{"title":"System Engineering based on Remarkable Geometric Properties of Space","authors":"V. Riznyk","doi":"10.37394/23205.2024.23.7","DOIUrl":"https://doi.org/10.37394/23205.2024.23.7","url":null,"abstract":"In this paper, we regard designing systems based on remarkable geometric properties of space, namely valuable rotational symmetry and asymmetry harmonious, using schematic and diagrammatic presentations of the systems. Moreover, the relationships are a way to comprehend original information to serve as a source of research and designing the systems. The objective of the future methodology is the advanced study of spatial geometric harmony as profiting information for expansion fundamental and applied researches for optimal solutions of technological problems in systems engineering. These systems engineering designs make it possible to improve the quality indices of devices or systems concerning performance reliability, code immunity, and the other operating indices of the systems. As examples, both up to 25% errors of lengths correcting code and high-speed self-error-correcting vector data code formed under a toroidal coordinate system are presented.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700989","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 : 2024-04-15DOI: 10.37394/23205.2024.23.8
C. Pacol
Evaluating customer satisfaction is very significant in all organizations to get the perspective of users/customers/stakeholders on products and/or services. Part of the data obtained during the evaluation are observations and comments of respondents and these are very rich in insights as they provide information on the strengths as well as the areas needing improvement. As the volume of textual data increases, the difficulty of analyzing them manually also increases. With these concerns, text analytics tools should be used to save time and effort in analyzing and interpreting the data. The textual data being processed in sentiment analysis problems vary in so many ways. For instance, the context of textual data and the language used vary when data are sourced from different locations and areas or fields. Thus, machine learning was utilized in this study to customize text analysis depending on the context and language used in the dataset. This research aimed to produce a prototype that can be used to explore three vectorization techniques and selected machine learning algorithms. The prototype was evaluated in the context of features for the application of machine learning in sentiment analysis. Results of the prototype development and the feedback and suggestions during the evaluation were presented. In future work, the prototype shall be improved, and the evaluators' feedback will be considered.
{"title":"Prototyping a Reusable Sentiment Analysis Tool for Machine Learning and Visualization","authors":"C. Pacol","doi":"10.37394/23205.2024.23.8","DOIUrl":"https://doi.org/10.37394/23205.2024.23.8","url":null,"abstract":"Evaluating customer satisfaction is very significant in all organizations to get the perspective of users/customers/stakeholders on products and/or services. Part of the data obtained during the evaluation are observations and comments of respondents and these are very rich in insights as they provide information on the strengths as well as the areas needing improvement. As the volume of textual data increases, the difficulty of analyzing them manually also increases. With these concerns, text analytics tools should be used to save time and effort in analyzing and interpreting the data. The textual data being processed in sentiment analysis problems vary in so many ways. For instance, the context of textual data and the language used vary when data are sourced from different locations and areas or fields. Thus, machine learning was utilized in this study to customize text analysis depending on the context and language used in the dataset. This research aimed to produce a prototype that can be used to explore three vectorization techniques and selected machine learning algorithms. The prototype was evaluated in the context of features for the application of machine learning in sentiment analysis. Results of the prototype development and the feedback and suggestions during the evaluation were presented. In future work, the prototype shall be improved, and the evaluators' feedback will be considered.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"44 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701709","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 : 2024-04-09DOI: 10.37394/23205.2024.23.5
M. Mahendran, R. Kavitha
A subset T ⊆ V is a detourmonophonic set of G if each node (vertex) x in G contained in an p-q detourmonophonic path where p, q ∈ T.. The number of points in a minimum detourmonophonic set of G is called as the detourmonophonic number of G, dm(G). A subset T ⊆ V of a connected graph G is said to be a split detourmonophonic set of G if the set T of vertices is either T = V or T is detoumonophonic set and V – T induces a subgraph in which is disconnected. The minimum split detourmonophonic set is split detourmonophonic set with minimum cardinality and it is called a split detourmonophonic number, denoted by dms(G). For certain standard graphs, defined new parameter was identified. Some of the realization results on defined new parameters were established.
如果 G 中的每个节点（顶点）x 都包含在一条 pq 非单音路径中（其中 p，q∈T），则子集 T ⊆ V 是 G 的非单音集合。G 的最小失谐集合中的点数称为 G 的失谐数 dm(G)。如果顶点集 T 要么是 T = V，要么 T 是去单音集，并且 V - T 引发了一个断开的子图，则称连通图 G 的子集 T ⊆ V 为 G 的分裂去单音集。最小分裂失单音集是具有最小心数的分裂失单音集，称为分裂失单音数，用 dms(G) 表示。对于某些标准图形，确定了定义的新参数。建立了一些关于定义新参数的实现结果。
{"title":"Split Detour Monophonic Sets in Graph","authors":"M. Mahendran, R. Kavitha","doi":"10.37394/23205.2024.23.5","DOIUrl":"https://doi.org/10.37394/23205.2024.23.5","url":null,"abstract":"A subset T ⊆ V is a detourmonophonic set of G if each node (vertex) x in G contained in an p-q detourmonophonic path where p, q ∈ T.. The number of points in a minimum detourmonophonic set of G is called as the detourmonophonic number of G, dm(G). A subset T ⊆ V of a connected graph G is said to be a split detourmonophonic set of G if the set T of vertices is either T = V or T is detoumonophonic set and V – T induces a subgraph in which is disconnected. The minimum split detourmonophonic set is split detourmonophonic set with minimum cardinality and it is called a split detourmonophonic number, denoted by dms(G). For certain standard graphs, defined new parameter was identified. Some of the realization results on defined new parameters were established.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"111 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724519","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}
Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.
{"title":"Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency","authors":"Safiye Turgay, Metehan Han, Suat Erdoğan, Esma Sedef Kara, Recep Yilmaz","doi":"10.37394/23205.2024.23.6","DOIUrl":"https://doi.org/10.37394/23205.2024.23.6","url":null,"abstract":"Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140721479","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：