Pub Date : 2022-12-04DOI: 10.1080/0952813X.2022.2149861
Shivashankar Mohana, Chandrasekaran Shyamala, E. S. Rani, M. Ambika
ABSTRACT This work introduces a novel privacy preservation scheme. In large databases, the data sanitisation process preserves the stored sensitive data safely from unauthorised access and users by hiding it. Moreover, the statistical features are extracted. Further, the normalised data and features are processed under the data sanitisation process. For the sanitisation process, the optimal key is produced by utilising the Deep Belief Network (DBN) with Chaotic Map-adopted Poor and Rich Optimisation (CMPRO) model. It is the modified version of the classical PRO algorithm. As a novelty, chaotic map and cycle crossover operation is included in the CMPRO algorithm. Privacy, modification degree, data preservation ratio, and hiding failure are considered as the objectives for the key generation process. Then, the data restoration process restores or recovers the sanitised data, and it is the reverse process. Then, the outcomes of the adopted scheme are analysed over the traditional systems based on certain measures. Especially, the sanitisation effectiveness of the proposed approach for data 1 in test case 2 and it is 54.56%, 51.82%, 47.94%, 49.59%, 18.17%, 43.32%, 47.03%, 47.03%, 55.79%, 21.84%, 47.33%, and 32.13% better than the existing CNN+CMPRO, RNN+CMPRO, LSTM+CMPRO, BiLSTM+CMPRO, DBN+PRO, DBN+SSA, DBN+SMO, DBN+LA, DBN+SSO, DBN+J-SSO, DBN+BS-WOA, and DBN+R-GDA schemes.
{"title":"Preserving sensitive data with deep learning assisted sanitisation process","authors":"Shivashankar Mohana, Chandrasekaran Shyamala, E. S. Rani, M. Ambika","doi":"10.1080/0952813X.2022.2149861","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2149861","url":null,"abstract":"ABSTRACT This work introduces a novel privacy preservation scheme. In large databases, the data sanitisation process preserves the stored sensitive data safely from unauthorised access and users by hiding it. Moreover, the statistical features are extracted. Further, the normalised data and features are processed under the data sanitisation process. For the sanitisation process, the optimal key is produced by utilising the Deep Belief Network (DBN) with Chaotic Map-adopted Poor and Rich Optimisation (CMPRO) model. It is the modified version of the classical PRO algorithm. As a novelty, chaotic map and cycle crossover operation is included in the CMPRO algorithm. Privacy, modification degree, data preservation ratio, and hiding failure are considered as the objectives for the key generation process. Then, the data restoration process restores or recovers the sanitised data, and it is the reverse process. Then, the outcomes of the adopted scheme are analysed over the traditional systems based on certain measures. Especially, the sanitisation effectiveness of the proposed approach for data 1 in test case 2 and it is 54.56%, 51.82%, 47.94%, 49.59%, 18.17%, 43.32%, 47.03%, 47.03%, 55.79%, 21.84%, 47.33%, and 32.13% better than the existing CNN+CMPRO, RNN+CMPRO, LSTM+CMPRO, BiLSTM+CMPRO, DBN+PRO, DBN+SSA, DBN+SMO, DBN+LA, DBN+SSO, DBN+J-SSO, DBN+BS-WOA, and DBN+R-GDA schemes.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"25 1","pages":"589 - 616"},"PeriodicalIF":2.2,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82577075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1080/0952813X.2022.2072004
Alexandre Salle, Aline Villavicencio
ABSTRACT Despite the recent popularity of contextual word embeddings, static word embeddings still dominate lexical semantic tasks, making their study of continued relevance. A widely adopted family of such static word embeddings is derived by explicitly factorising the Pointwise Mutual Information (PMI) weighting of the co-occurrence matrix. As unobserved co-occurrences lead PMI to negative infinity, a common workaround is to clip negative PMI at 0. However, it is unclear what information is lost by collapsing negative PMI values to 0. To answer this question, we isolate and study the effects of negative (and positive) PMI on the semantics and geometry of models adopting factorisation of different PMI matrices. Word and sentence-level evaluations show that only accounting for positive PMI in the factorisation strongly captures both semantics and syntax, whereas using only negative PMI captures little of semantics but a surprising amount of syntactic information. Results also reveal that incorporating negative PMI induces stronger rank invariance of vector norms and directions, as well as improved rare word representations.
{"title":"Understanding the effects of negative (and positive) pointwise mutual information on word vectors","authors":"Alexandre Salle, Aline Villavicencio","doi":"10.1080/0952813X.2022.2072004","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2072004","url":null,"abstract":"ABSTRACT Despite the recent popularity of contextual word embeddings, static word embeddings still dominate lexical semantic tasks, making their study of continued relevance. A widely adopted family of such static word embeddings is derived by explicitly factorising the Pointwise Mutual Information (PMI) weighting of the co-occurrence matrix. As unobserved co-occurrences lead PMI to negative infinity, a common workaround is to clip negative PMI at 0. However, it is unclear what information is lost by collapsing negative PMI values to 0. To answer this question, we isolate and study the effects of negative (and positive) PMI on the semantics and geometry of models adopting factorisation of different PMI matrices. Word and sentence-level evaluations show that only accounting for positive PMI in the factorisation strongly captures both semantics and syntax, whereas using only negative PMI captures little of semantics but a surprising amount of syntactic information. Results also reveal that incorporating negative PMI induces stronger rank invariance of vector norms and directions, as well as improved rare word representations.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"113 1","pages":"1161 - 1199"},"PeriodicalIF":2.2,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80655386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-06DOI: 10.1080/0952813X.2022.2078888
Jimson L, Dr. J. P. Ananth
ABSTRACT The video summarisation is an advanced mechanism for enabling users to handle and browse large videos in an effective manner. Various video summarisation methods are developed in recent days, in which handling of synchronisation and timing issues remain as the important challenge. The proposed video summarisation technique produces a short summary from the huge video stream. Initially, from an input database, the cricket videos containing number of frames are fed to keyframe extraction unit. Here, the keyframe extraction is done by the Euclidean distance and discrete cosine transform, and the best keyframes are selected based on the Euclidean distance. The residual frame is obtained by passing the input frames through deep convolutional neural network. Then, the similarity is calculated by Bhattacharyya distance. For video summarisation process, the optimal frameset is evaluated by matching residual keyframe with obtained keyframes. Here, input queries consisting of face object are subjected to object matching process, which is performed using the proposed mutual probability-based k-nearest neighbour (MP-KNN) to obtain relevant frames based on texture features. The performance of the proposed MP-KNN is superior based on precision, recall, and F-measure with values 0.963, 0.960, and 0.909, respectively.
{"title":"Video Summarization using Deep Convolutional Neural Networks and Mutual Probability-based K-Nearest Neighbour","authors":"Jimson L, Dr. J. P. Ananth","doi":"10.1080/0952813X.2022.2078888","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2078888","url":null,"abstract":"ABSTRACT The video summarisation is an advanced mechanism for enabling users to handle and browse large videos in an effective manner. Various video summarisation methods are developed in recent days, in which handling of synchronisation and timing issues remain as the important challenge. The proposed video summarisation technique produces a short summary from the huge video stream. Initially, from an input database, the cricket videos containing number of frames are fed to keyframe extraction unit. Here, the keyframe extraction is done by the Euclidean distance and discrete cosine transform, and the best keyframes are selected based on the Euclidean distance. The residual frame is obtained by passing the input frames through deep convolutional neural network. Then, the similarity is calculated by Bhattacharyya distance. For video summarisation process, the optimal frameset is evaluated by matching residual keyframe with obtained keyframes. Here, input queries consisting of face object are subjected to object matching process, which is performed using the proposed mutual probability-based k-nearest neighbour (MP-KNN) to obtain relevant frames based on texture features. The performance of the proposed MP-KNN is superior based on precision, recall, and F-measure with values 0.963, 0.960, and 0.909, respectively.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"26 1","pages":"1251 - 1267"},"PeriodicalIF":2.2,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78982344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-30DOI: 10.1080/0952813X.2022.2078887
L. Moreira, C. G. Ralha
ABSTRACT Plan execution in dynamic environments can be affected by unexpected events leading to failures. Research on multi-agent planning area presents recovery strategies with replanning and repairing with evaluation based simply on average values. Thus, in this work, we propose a statistical method to evaluate plan recovery strategies in dynamic environments using a domain-independent approach. To validate the proposed method, we conducted simulated experiments with varying the number of agents, goals, actions, failure probability, and agents’ coupling levels. The evaluation metrics include plan length and planning time. The results highlight with at least 94% certainty that repairing planning time is lower than replanning, and replanning builds plans with fewer actions than repairing. Considering plan recovery strategies in dynamic multi-agent environments, we demonstrate that repairing presents better results as it is faster, but replanning builds better plans as the final plan length is strongly correlated to failure occurrence.
{"title":"Method for evaluating plan recovery strategies in dynamic multi-agent environments","authors":"L. Moreira, C. G. Ralha","doi":"10.1080/0952813X.2022.2078887","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2078887","url":null,"abstract":"ABSTRACT Plan execution in dynamic environments can be affected by unexpected events leading to failures. Research on multi-agent planning area presents recovery strategies with replanning and repairing with evaluation based simply on average values. Thus, in this work, we propose a statistical method to evaluate plan recovery strategies in dynamic environments using a domain-independent approach. To validate the proposed method, we conducted simulated experiments with varying the number of agents, goals, actions, failure probability, and agents’ coupling levels. The evaluation metrics include plan length and planning time. The results highlight with at least 94% certainty that repairing planning time is lower than replanning, and replanning builds plans with fewer actions than repairing. Considering plan recovery strategies in dynamic multi-agent environments, we demonstrate that repairing presents better results as it is faster, but replanning builds better plans as the final plan length is strongly correlated to failure occurrence.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"92 1","pages":"1225 - 1249"},"PeriodicalIF":2.2,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85860186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-04DOI: 10.1080/0952813X.2022.2061606
Muhammad Qiyas, S. Abdullah, Saifullah Khan
ABSTRACT In decision support systems, linguistic fuzzy information played an important role and the linguistic fuzzy aggregation operators (AOs) worked in group decision support systems. Recently, we proposed the linguistic picture fuzzy (LPF) sets, which is the extension of the linguistic intuitionist fuzzy sets, to reflect the ambiguity and vagueness of knowledge in decision-making (DM) problem. The goal of this research work is to define a new family of LPF AOs through the use of Dombi operations and Heronian mean (HM) operator. In addition to fusing individual attribute values, the evolved operators are good ability to handle the common association between the attributes, making them more appropriate to effectively solve difficult multi-attribute DM (MADM) problems. Therefore, we developed an approach for MADM problem based on LPF Dombi HM operators and solved an emergency programme selection problem. The comparison section provides the effectiveness, reliability and practicality.
在群体决策支持系统中，语言模糊信息起着重要的作用，语言模糊聚合算子在群体决策支持系统中起着重要的作用。最近，我们提出了语言图像模糊集(LPF)，它是语言直觉模糊集的扩展，用来反映决策问题中知识的模糊性和模糊性。本研究工作的目标是通过使用Dombi操作和Heronian mean (HM)算子来定义一个新的LPF AOs族。进化的算子除了融合单个属性值外，还具有处理属性之间共同关联的能力，使其更适合于有效解决多属性DM (MADM)难题。为此，提出了一种基于LPF Dombi HM算子的MADM问题求解方法，解决了一个应急方案选择问题。对比部分给出了算法的有效性、可靠性和实用性。
{"title":"A Novel Approach of Linguistic Picture Fuzzy Dombi Heronian Mean Operators and their Application to Emergency Program Selection","authors":"Muhammad Qiyas, S. Abdullah, Saifullah Khan","doi":"10.1080/0952813X.2022.2061606","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2061606","url":null,"abstract":"ABSTRACT In decision support systems, linguistic fuzzy information played an important role and the linguistic fuzzy aggregation operators (AOs) worked in group decision support systems. Recently, we proposed the linguistic picture fuzzy (LPF) sets, which is the extension of the linguistic intuitionist fuzzy sets, to reflect the ambiguity and vagueness of knowledge in decision-making (DM) problem. The goal of this research work is to define a new family of LPF AOs through the use of Dombi operations and Heronian mean (HM) operator. In addition to fusing individual attribute values, the evolved operators are good ability to handle the common association between the attributes, making them more appropriate to effectively solve difficult multi-attribute DM (MADM) problems. Therefore, we developed an approach for MADM problem based on LPF Dombi HM operators and solved an emergency programme selection problem. The comparison section provides the effectiveness, reliability and practicality.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"57 1","pages":"445 - 472"},"PeriodicalIF":2.2,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73550268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1080/0952813X.2022.2067249
Chen Han, Xuanyin Wang
ABSTRACT This paper investigates how agents learn and perform efficient strategies by trying different actions in asymmetric confrontation setting. Firstly, we use Gomoku as an example to analyse the causes and impacts of asymmetric confrontation: the first mover gains higher power than the second mover. We find that the first mover learns how to attack quickly while it is difficult for the second mover to learn how to defend since it cannot win the first mover and always receives negative rewards. As such, the game is stuck at a deadlock in which the first mover cannot make further advances to learn how to defend, and the second mover learns nothing. Secondly, we propose an ancillary mechanism (AM) to add two principles to the agent’s actions to overcome this difficulty. AM is a guidance for the agents to reduce the learning difficulty and to improve their behavioural quality. To the best of our knowledge, this is the first study to define asymmetric confrontation in reinforcement learning and propose approaches to tackle such problems. In the numerical tests, we first conduct a simple human vs AI experiment to calibrate the learning process in asymmetric confrontation. Then, an experiment of 15*15 Gomoku game by letting two agents (with AM and without AM) compete is applied to check the potential of AM. Results show that adding AM can make both the first and the second movers become stronger in almost the same amount of calculation.
{"title":"Ancillary mechanism for autonomous decision-making process in asymmetric confrontation: a view from Gomoku","authors":"Chen Han, Xuanyin Wang","doi":"10.1080/0952813X.2022.2067249","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2067249","url":null,"abstract":"ABSTRACT This paper investigates how agents learn and perform efficient strategies by trying different actions in asymmetric confrontation setting. Firstly, we use Gomoku as an example to analyse the causes and impacts of asymmetric confrontation: the first mover gains higher power than the second mover. We find that the first mover learns how to attack quickly while it is difficult for the second mover to learn how to defend since it cannot win the first mover and always receives negative rewards. As such, the game is stuck at a deadlock in which the first mover cannot make further advances to learn how to defend, and the second mover learns nothing. Secondly, we propose an ancillary mechanism (AM) to add two principles to the agent’s actions to overcome this difficulty. AM is a guidance for the agents to reduce the learning difficulty and to improve their behavioural quality. To the best of our knowledge, this is the first study to define asymmetric confrontation in reinforcement learning and propose approaches to tackle such problems. In the numerical tests, we first conduct a simple human vs AI experiment to calibrate the learning process in asymmetric confrontation. Then, an experiment of 15*15 Gomoku game by letting two agents (with AM and without AM) compete is applied to check the potential of AM. Results show that adding AM can make both the first and the second movers become stronger in almost the same amount of calculation.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"94 1","pages":"1141 - 1159"},"PeriodicalIF":2.2,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76076433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-21DOI: 10.1080/0952813X.2022.2067247
Mohammad Eshaghnezhad, S. Effati, A. Mansoori
ABSTRACT We develop a projection recurrent neural network (RNN) to obtain the solution of the shortest path problem (SPP). Our focus on the paper is to give a compact single-layer structure RNN model to solve the SPP. To present the RNN model, we utilise a mixed linear complementarity problem (MLCP). Moreover, the developed RNN is proved to be globally stable. Finally, some numerical simulations are stated to show the performance of the presented approach. We compare the results with some other methods.
{"title":"A compact MLCP-based projection recurrent neural network model to solve shortest path problem","authors":"Mohammad Eshaghnezhad, S. Effati, A. Mansoori","doi":"10.1080/0952813X.2022.2067247","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2067247","url":null,"abstract":"ABSTRACT We develop a projection recurrent neural network (RNN) to obtain the solution of the shortest path problem (SPP). Our focus on the paper is to give a compact single-layer structure RNN model to solve the SPP. To present the RNN model, we utilise a mixed linear complementarity problem (MLCP). Moreover, the developed RNN is proved to be globally stable. Finally, some numerical simulations are stated to show the performance of the presented approach. We compare the results with some other methods.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"18 1","pages":"1101 - 1119"},"PeriodicalIF":2.2,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75779580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-19DOI: 10.1080/0952813X.2022.2062456
Riccardo Buscaroli, F. Chesani, Giulia Giuliani, Daniela Loreti, P. Mello
ABSTRACT Despite the indisputable progresses of artificial intelligence, some tasks that are rather easy for a human being are still challenging for a machine. An emblematic example is the resolution of mathematical puzzles with diagrams. Sub-symbolical approaches have proven successful in fields like image recognition and natural language processing, but the combination of these techniques into a multimodal approach towards the identification of the puzzle’s answer appears to be a matter of reasoning, more suitable for the application of a symbolic technique. In this work, we employ logic programming to perform spatial reasoning on the puzzle’s diagram and integrate the deriving knowledge into the solving process. Analysing the resolution strategies required by the puzzles of an international competition for humans, we draw the design principles of a Prolog reasoning library, which interacts with image processing software to formulate the puzzle’s constraints. The library integrates the knowledge from different sources, and relies on the Prolog inference engine to provide the answer. This work can be considered as a first step towards the ambitious goal of a machine autonomously solving a problem in a generic context starting from its textual-graphical presentation. An ability that can help potentially every human–machine interaction.
{"title":"A Prolog application for reasoning on maths puzzles with diagrams","authors":"Riccardo Buscaroli, F. Chesani, Giulia Giuliani, Daniela Loreti, P. Mello","doi":"10.1080/0952813X.2022.2062456","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2062456","url":null,"abstract":"ABSTRACT Despite the indisputable progresses of artificial intelligence, some tasks that are rather easy for a human being are still challenging for a machine. An emblematic example is the resolution of mathematical puzzles with diagrams. Sub-symbolical approaches have proven successful in fields like image recognition and natural language processing, but the combination of these techniques into a multimodal approach towards the identification of the puzzle’s answer appears to be a matter of reasoning, more suitable for the application of a symbolic technique. In this work, we employ logic programming to perform spatial reasoning on the puzzle’s diagram and integrate the deriving knowledge into the solving process. Analysing the resolution strategies required by the puzzles of an international competition for humans, we draw the design principles of a Prolog reasoning library, which interacts with image processing software to formulate the puzzle’s constraints. The library integrates the knowledge from different sources, and relies on the Prolog inference engine to provide the answer. This work can be considered as a first step towards the ambitious goal of a machine autonomously solving a problem in a generic context starting from its textual-graphical presentation. An ability that can help potentially every human–machine interaction.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"45 3 1","pages":"1079 - 1099"},"PeriodicalIF":2.2,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85377524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-06DOI: 10.1080/0952813X.2022.2058618
G. Mohanraj, V. Mohanraj, M. Marimuthu, V. Sathiyamoorthi, A. K. Luhach, Sandeep Kumar
ABSTRACT A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.
{"title":"Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data","authors":"G. Mohanraj, V. Mohanraj, M. Marimuthu, V. Sathiyamoorthi, A. K. Luhach, Sandeep Kumar","doi":"10.1080/0952813X.2022.2058618","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2058618","url":null,"abstract":"ABSTRACT A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"96 1","pages":"377 - 393"},"PeriodicalIF":2.2,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86656964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-25DOI: 10.1080/0952813X.2022.2040598
A. A. Bhandakkar, Lini Mathew
ABSTRACT This manuscript proposes the optimal allocation of hybrid power flow controller (HPFC) using hybrid technique. The proposed technique is the implementation of Integrated Slime Mould Algorithm (ISMA). The searching behaviour of Slime Mould Algorithm (SMA) is enhanced by the position updating behaviour of the whale optimisation algorithm (WOA). HPFC, a hybrid topology, has VAR compensator or an impedance-type FACTS device, most probably obtainable at power system, and two voltage source converters depend on controllers share a general DC link. The novel contributions of allocating HPFC at optimal location for multi-objective fitness functions denote minimal real power loss of system as well as minimal generation cost using ISMA method. Here, ISMA method optimises maximum line of power loss as appropriate location of unified power flow controller (UPFC). The optimal location parameters and dynamic stability restrictions are restored with normal constraints, employing UPFC optimal capacity has been optimised to decreased cost with the help of ISMA technique.
{"title":"Merging slime mould with whale optimization algorithm for optimal allocation of hybrid power flow controller in power system","authors":"A. A. Bhandakkar, Lini Mathew","doi":"10.1080/0952813X.2022.2040598","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2040598","url":null,"abstract":"ABSTRACT This manuscript proposes the optimal allocation of hybrid power flow controller (HPFC) using hybrid technique. The proposed technique is the implementation of Integrated Slime Mould Algorithm (ISMA). The searching behaviour of Slime Mould Algorithm (SMA) is enhanced by the position updating behaviour of the whale optimisation algorithm (WOA). HPFC, a hybrid topology, has VAR compensator or an impedance-type FACTS device, most probably obtainable at power system, and two voltage source converters depend on controllers share a general DC link. The novel contributions of allocating HPFC at optimal location for multi-objective fitness functions denote minimal real power loss of system as well as minimal generation cost using ISMA method. Here, ISMA method optimises maximum line of power loss as appropriate location of unified power flow controller (UPFC). The optimal location parameters and dynamic stability restrictions are restored with normal constraints, employing UPFC optimal capacity has been optimised to decreased cost with the help of ISMA technique.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"2 1","pages":"973 - 1000"},"PeriodicalIF":2.2,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79440552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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