Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.
{"title":"TSO clustered protocol to extend lifetime of IoT based mobile wireless sensor networks","authors":"Giji Kiruba Dasebenezer, Benita Joselin","doi":"10.34028/iajit/20/4/1","DOIUrl":"https://doi.org/10.34028/iajit/20/4/1","url":null,"abstract":"Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"90 1","pages":"559-566"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88272965","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}
Marko Grebovic, Luka Filipović, Ivana Katnic, M. Vukotić, Tomo Popović
Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results
{"title":"Machine learning models for statistical analysis","authors":"Marko Grebovic, Luka Filipović, Ivana Katnic, M. Vukotić, Tomo Popović","doi":"10.34028/iajit/20/3a/8","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/8","url":null,"abstract":"Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"69 1","pages":"505-514"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74331188","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}
Dheya Ghazi Mustafa, I. Mustafa, Samah Zriqat, Q. Althebyan
The global transition from traditional classroom instruction to online learning has been hastened through COVID-19. Notwithstanding its benefits, students were unable to quickly adjust to the difficulties of change. This research offers MIRNA, an assistive 3D instructional tool for slow learners in primary school, mostly for Arabic, English, and Math topics, to address academic issues that develop with online education. The proposed tool can be fully integrated with remedial programs to assist students who struggled to adjust to distant learning during the pandemic, slow learners, or even those who are unable to attend school. This application categorizes students based on academic performance rather than age and automatically adjusts to their limitations. Additionally, MIRNA offers a simple interface that allows teachers to personalize it with their own content and game scenarios. We carried out an empirical investigation to assess teachers' intentions to use MIRNA as an online learning platform in the learning process. The findings of the study show that teachers’ attitude towards the game was positive, and they intended to use the game in the learning process in the future
{"title":"MIRNA: adaptive 3D game to assist children's distance learning difficulties; design and teachers' intention to use","authors":"Dheya Ghazi Mustafa, I. Mustafa, Samah Zriqat, Q. Althebyan","doi":"10.34028/iajit/20/3a/10","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/10","url":null,"abstract":"The global transition from traditional classroom instruction to online learning has been hastened through COVID-19. Notwithstanding its benefits, students were unable to quickly adjust to the difficulties of change. This research offers MIRNA, an assistive 3D instructional tool for slow learners in primary school, mostly for Arabic, English, and Math topics, to address academic issues that develop with online education. The proposed tool can be fully integrated with remedial programs to assist students who struggled to adjust to distant learning during the pandemic, slow learners, or even those who are unable to attend school. This application categorizes students based on academic performance rather than age and automatically adjusts to their limitations. Additionally, MIRNA offers a simple interface that allows teachers to personalize it with their own content and game scenarios. We carried out an empirical investigation to assess teachers' intentions to use MIRNA as an online learning platform in the learning process. The findings of the study show that teachers’ attitude towards the game was positive, and they intended to use the game in the learning process in the future","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"29 1","pages":"527-535"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81717739","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}
{"title":"Human facial emotion recognition using deep neural networks","authors":"S. Benisha, T. MirnalineeT.","doi":"10.34028/iajit/20/3/2","DOIUrl":"https://doi.org/10.34028/iajit/20/3/2","url":null,"abstract":"","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"50 1","pages":"303-309"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83035492","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}
Data hiding in Voice over Internet Protocol (VoIP) using coverless approach improves the undetectability by preserving the cover bits from modification. This paper focuses on hiding the secret message in VoIP streams using Deoxyribonucleic Acid (DNA) steganography approach. DNA steganography is known for its low cracking probability. The embedding process is done in two steps. The first step converts the VoIP sample, secret message and a user generated key (for Authentication) into m-RNA pattern during transcription and the second step converts the m-RNA to form a triplet during translation process to create a protein array, where the secret message is embedded. The secret message is extracted from the protein array by applying reverse translation and Transcription. The proposed approach improves the undetectability by leaving the cover bits unmodified with PESQ values 84% comparatively greater than the state of art techniques.
{"title":"Coverless data hiding in VoIP based on DNA steganography with authentication","authors":"Deepikaa Soundararajan, Saravanan Ramakrishnan","doi":"10.34028/iajit/20/2/5","DOIUrl":"https://doi.org/10.34028/iajit/20/2/5","url":null,"abstract":"Data hiding in Voice over Internet Protocol (VoIP) using coverless approach improves the undetectability by preserving the cover bits from modification. This paper focuses on hiding the secret message in VoIP streams using Deoxyribonucleic Acid (DNA) steganography approach. DNA steganography is known for its low cracking probability. The embedding process is done in two steps. The first step converts the VoIP sample, secret message and a user generated key (for Authentication) into m-RNA pattern during transcription and the second step converts the m-RNA to form a triplet during translation process to create a protein array, where the secret message is embedded. The secret message is extracted from the protein array by applying reverse translation and Transcription. The proposed approach improves the undetectability by leaving the cover bits unmodified with PESQ values 84% comparatively greater than the state of art techniques.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"15 1","pages":"190-198"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91265839","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}
A codebook is a combination of vectors that represents a digital image best and very useful tool for compression. Besides the well-known techniques such as Linde-Buzo-Gray, C-Means, and Fuzzy C-Means the nature-inspired metaheuristic algorithms have also become alternate techniques for solving the codebook generation problem. Fruit Fly Optimization Algorithm (FFA) is a simple and efficient algorithm, but the capturing of an agent by a local minimum point is the main problem. Therefore, the fruit flies generally do not reach the global solution at the end of the iterations. In this study, the FFA is empowered with a smart exponential flight approach to finding out a global optimum codebook. In this approach, if a fruit fly agent is captured by a local minimum point accidentally, the smart exponential flight steps provide an opportunity to escape from it easily. In the experimental studies, successful compression results have been taken in terms of lower error rates. The numerical results prove that the proposed Smart Exponential flight-based Fruit Fly Algorithm (SE-FFA) is better than the variations of convolutional FFA by providing a global optimum codebook.
{"title":"A novel codebook generation by smart fruit fly algorithm based on exponential flight","authors":"I. Kilic","doi":"10.34028/iajit/20/4/4","DOIUrl":"https://doi.org/10.34028/iajit/20/4/4","url":null,"abstract":"A codebook is a combination of vectors that represents a digital image best and very useful tool for compression. Besides the well-known techniques such as Linde-Buzo-Gray, C-Means, and Fuzzy C-Means the nature-inspired metaheuristic algorithms have also become alternate techniques for solving the codebook generation problem. Fruit Fly Optimization Algorithm (FFA) is a simple and efficient algorithm, but the capturing of an agent by a local minimum point is the main problem. Therefore, the fruit flies generally do not reach the global solution at the end of the iterations. In this study, the FFA is empowered with a smart exponential flight approach to finding out a global optimum codebook. In this approach, if a fruit fly agent is captured by a local minimum point accidentally, the smart exponential flight steps provide an opportunity to escape from it easily. In the experimental studies, successful compression results have been taken in terms of lower error rates. The numerical results prove that the proposed Smart Exponential flight-based Fruit Fly Algorithm (SE-FFA) is better than the variations of convolutional FFA by providing a global optimum codebook.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"24 1","pages":"584-591"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85717606","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}
Distribution of chronological land cover modifications has attained a vibrant concern in contemporary sustainability research. Information delivered by satellite remote sensing imagery plays momentous role in enumerating and discovering the expected land cover for vegetation. Fuzzy clustering has been found successful in implementing a significant number of optimization problems associated with machine learning due to its fractional membership degrees in several neighbouring constellations. This research establishes a framework on land cover classification for agricultural development. The approach is focused on object-oriented classification and is organized with a Fuzzy c-means clustering over segmentation on CIE L*a*b* colour scheme which provides analysis of vegetation coverage and enhances land planning for sustainable developments. This research investigates the land cover variations of the eastern province of Saudi Arabia throughout an elongated span of period from 1984 to 2018 to recognize the possible roles of the land cover alterations on farming. The Landsat satellite imagery and Geographical Information System (GIS), in tandem with Google Earth chronological imagery are employed for land use variation analysis. Experimental results exhibit a reasonable spread in the cultivated zones and reveal that this Colour Segmented Fuzzy Clustering (CSFC) strategy achieves better than the relevant counterpart approaches considering classification accuracy.
{"title":"On Satellite Imagery of Land Cover Classification for Agricultural Development","authors":"Ali Abdullah M. Alzahrani, Al-Amin Bhuiyan","doi":"10.34028/iajit/20/1/2","DOIUrl":"https://doi.org/10.34028/iajit/20/1/2","url":null,"abstract":"Distribution of chronological land cover modifications has attained a vibrant concern in contemporary sustainability research. Information delivered by satellite remote sensing imagery plays momentous role in enumerating and discovering the expected land cover for vegetation. Fuzzy clustering has been found successful in implementing a significant number of optimization problems associated with machine learning due to its fractional membership degrees in several neighbouring constellations. This research establishes a framework on land cover classification for agricultural development. The approach is focused on object-oriented classification and is organized with a Fuzzy c-means clustering over segmentation on CIE L*a*b* colour scheme which provides analysis of vegetation coverage and enhances land planning for sustainable developments. This research investigates the land cover variations of the eastern province of Saudi Arabia throughout an elongated span of period from 1984 to 2018 to recognize the possible roles of the land cover alterations on farming. The Landsat satellite imagery and Geographical Information System (GIS), in tandem with Google Earth chronological imagery are employed for land use variation analysis. Experimental results exhibit a reasonable spread in the cultivated zones and reveal that this Colour Segmented Fuzzy Clustering (CSFC) strategy achieves better than the relevant counterpart approaches considering classification accuracy.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"41 1","pages":"9-18"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79199003","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 future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.
{"title":"Tree-based multicast routing and channel assignment for enhanced throughout in emerging cognitive radio networks","authors":"H. Salameh, Mustafa Ali","doi":"10.34028/iajit/20/3a/1","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/1","url":null,"abstract":"In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"25 1","pages":"435-445"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82444204","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 dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.
{"title":"New model of feature selection based chaotic firefly algorithm for arabic text categorization","authors":"M. Hadni, Hjiaj Hassane","doi":"10.34028/iajit/20/3a/3","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/3","url":null,"abstract":"The dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"10 1","pages":"461-468"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88206908","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}
As machine learning emerged, it is being used in a variety of applications like speech recognition, image recognition, sequence modeling, etc., Sequence modeling is one type of application where resultant sequences are generated based on historical data inputs provided. These sequences are fairly work in an uncertain environment like games or sports. In the case of a game or a sport, there is a sequence of moves selected by multiple players. There is a statistical uncertainty observed for simple to more complex games. For example, while playing chess, a simple statistical modeled uncertainty would be enough to choose the next possible. This move selection is dependent on available free spaces of pieces or pawns. The sports like tennis, cricket, and other games need a more complex design for uncertainty modeling for next move selection. A Bayesian Network model will work if there is fairly less uncertainty in the selection of the next move. A Bayesian Network-based model will be best fitted if all possible moves are included before training any machine learning or deep learning model. This will be achieved with the usage of the Context-Li model. The proposed Bayesian Network-based Uncertainty Modeling (BNUM) is used to incorporate uncertainty, for next move selection. BNUM is a multi-variable, multi-level association to incubate uncertainty in learning. It helps to predict the next move in an uncertain gaming environment. Different case studies are incorporated to verify the hypothesis and the results are a sequence of moves represented in the context graph.
{"title":"A bayesian network-based uncertainty modeling (BNUM) to analyze and predict next optimal moves in given game scenario","authors":"V. Jagtap, P. Kulkarni","doi":"10.34028/iajit/20/2/6","DOIUrl":"https://doi.org/10.34028/iajit/20/2/6","url":null,"abstract":"As machine learning emerged, it is being used in a variety of applications like speech recognition, image recognition, sequence modeling, etc., Sequence modeling is one type of application where resultant sequences are generated based on historical data inputs provided. These sequences are fairly work in an uncertain environment like games or sports. In the case of a game or a sport, there is a sequence of moves selected by multiple players. There is a statistical uncertainty observed for simple to more complex games. For example, while playing chess, a simple statistical modeled uncertainty would be enough to choose the next possible. This move selection is dependent on available free spaces of pieces or pawns. The sports like tennis, cricket, and other games need a more complex design for uncertainty modeling for next move selection. A Bayesian Network model will work if there is fairly less uncertainty in the selection of the next move. A Bayesian Network-based model will be best fitted if all possible moves are included before training any machine learning or deep learning model. This will be achieved with the usage of the Context-Li model. The proposed Bayesian Network-based Uncertainty Modeling (BNUM) is used to incorporate uncertainty, for next move selection. BNUM is a multi-variable, multi-level association to incubate uncertainty in learning. It helps to predict the next move in an uncertain gaming environment. Different case studies are incorporated to verify the hypothesis and the results are a sequence of moves represented in the context graph.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"28 1","pages":"199-205"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77461059","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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