International Journal of Intelligent Computing and CyberneticsTable of Contents for International Journal of Intelligent Computing and Cybernetics. List of articles from the current issue, including Just Accepted (EarlyCite)https://www.emerald.com/insight/publication/issn/1756-378X/vol/17/iss/1?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestInternational Journal of Intelligent Computing and CyberneticsEmerald Publishing LimitedInternational Journal of Intelligent Computing and CyberneticsInternational Journal of Intelligent Computing and Cyberneticshttps://www.emerald.com/insight/proxy/containerImg?link=/resource/publication/journal/403d9a726c0f8a8f8352664fac544cab/urn:emeraldgroup.com:asset:id:binary:ijicc.cover.jpghttps://www.emerald.com/insight/publication/issn/1756-378X/vol/17/iss/1?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestA novel consensus reaching method for the preference-approval structure based on regret theory and its application in evaluating pension institutionshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-02-2023-0023/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals. First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method. The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation. This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently. A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.A novel consensus reaching method for the preference-approval structure based on regret theory and its application in evaluating pension institutions
Qinggang Shi, Peng Li, Zhiwei Xu
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.1-19

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

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A novel consensus reaching method for the preference-approval structure based on regret theory and its application in evaluating pension institutions10.1108/IJICC-02-2023-0023International Journal of Intelligent Computing and Cybernetics2023-08-18© 2023 Emerald Publishing LimitedQinggang ShiPeng LiZhiwei XuInternational Journal of Intelligent Computing and Cybernetics1712023-08-1810.1108/IJICC-02-2023-0023https://www.emerald.com/insight/content/doi/10.1108/IJICC-02-2023-0023/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objectshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-03-2023-0053/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestHigh-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features. There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen. In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process. The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objects
Yandong Hou, Zhengbo Wu, Xinghua Ren, Kaiwen Liu, Zhengquan Chen
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.20-37

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

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BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objects10.1108/IJICC-03-2023-0053International Journal of Intelligent Computing and Cybernetics2023-08-03© 2023 Emerald Publishing LimitedYandong HouZhengbo WuXinghua RenKaiwen LiuZhengquan ChenInternational Journal of Intelligent Computing and Cybernetics1712023-08-0310.1108/IJICC-03-2023-0053https://www.emerald.com/insight/content/doi/10.1108/IJICC-03-2023-0053/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Multiobjective network security dynamic assessment method based on Bayesian network attack graphhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-05-2023-0121/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestWith the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed. Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained. The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense. A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.Multiobjective network security dynamic assessment method based on Bayesian network attack graph
Jialiang Xie, Shanli Zhang, Honghui Wang, Mingzhi Chen
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.38-60

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

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Multiobjective network security dynamic assessment method based on Bayesian network attack graph10.1108/IJICC-05-2023-0121International Journal of Intelligent Computing and Cybernetics2023-08-16© 2023 Emerald Publishing LimitedJialiang XieShanli ZhangHonghui WangMingzhi ChenInternational Journal of Intelligent Computing and Cybernetics1712023-08-1610.1108/IJICC-05-2023-0121https://www.emerald.com/insight/content/doi/10.1108/IJICC-05-2023-0121/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Stock price prediction using a novel approach in Gaussian mixture model-hidden Markov modelhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-03-2023-0050/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN). The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005). Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases. The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.Stock price prediction using a novel approach in Gaussian mixture model-hidden Markov model
Kala Nisha Gopinathan, Punniyamoorthy Murugesan, Joshua Jebaraj Jeyaraj
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.61-100

This study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).

The study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).

Comparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.

The study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

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Stock price prediction using a novel approach in Gaussian mixture model-hidden Markov model10.1108/IJICC-03-2023-0050International Journal of Intelligent Computing and Cybernetics2023-08-11© 2023 Emerald Publishing LimitedKala Nisha GopinathanPunniyamoorthy MurugesanJoshua Jebaraj JeyarajInternational Journal of Intelligent Computing and Cybernetics1712023-08-1110.1108/IJICC-03-2023-0050https://www.emerald.com/insight/content/doi/10.1108/IJICC-03-2023-0050/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
A robust twin support vector machine based on fuzzy systemshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-08-2023-0208/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestTwin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM). This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets. The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise. This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.A robust twin support vector machine based on fuzzy systems
Jianxiang Qiu, Jialiang Xie, Dongxiao Zhang, Ruping Zhang
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.101-125

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

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A robust twin support vector machine based on fuzzy systems10.1108/IJICC-08-2023-0208International Journal of Intelligent Computing and Cybernetics2023-09-18© 2023 Emerald Publishing LimitedJianxiang QiuJialiang XieDongxiao ZhangRuping ZhangInternational Journal of Intelligent Computing and Cybernetics1712023-09-1810.1108/IJICC-08-2023-0208https://www.emerald.com/insight/content/doi/10.1108/IJICC-08-2023-0208/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Improving social interaction of the visually impaired individuals through conversational assistive technologyhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-06-2023-0147/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAssistive technology has been developed to assist the visually impaired individuals in their social interactions. Specifically designed to enhance communication skills, facilitate social engagement and improve the overall quality of life, conversational assistive technologies include speech recognition APIs, text-to-speech APIs and various communication tools that are real. Enable real-time interaction. Using natural language processing (NLP) and machine learning algorithms, the technology analyzes spoken language and provides appropriate responses, offering an immersive experience through voice commands, audio feedback and vibration alerts. These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social interactions. Moreover, they promise to improve social competence and foster better relationships. In short, assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals, elevating the quality of their social engagement. The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social contexts. This technology helps them communicate effectively with acquaintances, family, co-workers and even strangers in public places. By enabling smoother and more natural communication, it works to reduce feelings of isolation and increase overall quality of life. Research findings include successful activity recognition, aligning with activities on which the VGG-16 model was trained, such as hugging, shaking hands, talking, walking, waving and more. The originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern technology. Research adds to the body of knowledge in the area of assistive technologies, which contribute to the empowerment and social inclusion of the visually impaired individuals.Improving social interaction of the visually impaired individuals through conversational assistive technology
Komal Ghafoor, Tauqir Ahmad, Muhammad Aslam, Samyan Wahla
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.126-142

Assistive technology has been developed to assist the visually impaired individuals in their social interactions. Specifically designed to enhance communication skills, facilitate social engagement and improve the overall quality of life, conversational assistive technologies include speech recognition APIs, text-to-speech APIs and various communication tools that are real. Enable real-time interaction. Using natural language processing (NLP) and machine learning algorithms, the technology analyzes spoken language and provides appropriate responses, offering an immersive experience through voice commands, audio feedback and vibration alerts.

These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social interactions. Moreover, they promise to improve social competence and foster better relationships. In short, assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals, elevating the quality of their social engagement.

The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social contexts. This technology helps them communicate effectively with acquaintances, family, co-workers and even strangers in public places. By enabling smoother and more natural communication, it works to reduce feelings of isolation and increase overall quality of life.

Research findings include successful activity recognition, aligning with activities on which the VGG-16 model was trained, such as hugging, shaking hands, talking, walking, waving and more. The originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern technology. Research adds to the body of knowledge in the area of assistive technologies, which contribute to the empowerment and social inclusion of the visually impaired individuals.

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Improving social interaction of the visually impaired individuals through conversational assistive technology10.1108/IJICC-06-2023-0147International Journal of Intelligent Computing and Cybernetics2023-10-20© 2023 Emerald Publishing LimitedKomal GhafoorTauqir AhmadMuhammad AslamSamyan WahlaInternational Journal of Intelligent Computing and Cybernetics1712023-10-2010.1108/IJICC-06-2023-0147https://www.emerald.com/insight/content/doi/10.1108/IJICC-06-2023-0147/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
An adaptive dynamic community detection algorithm based on multi-objective evolutionary clusteringhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0188/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestCommunity detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability. This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting. Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets. To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering
Wenxue Wang, Qingxia Li, Wenhong Wei
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.143-160

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

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An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering10.1108/IJICC-07-2023-0188International Journal of Intelligent Computing and Cybernetics2023-10-13© 2023 Emerald Publishing LimitedWenxue WangQingxia LiWenhong WeiInternational Journal of Intelligent Computing and Cybernetics1712023-10-1310.1108/IJICC-07-2023-0188https://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0188/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Deep learning architectures in dental diagnostics: a systematic comparison of techniques for accurate prediction of dental disease through x-ray imaginghttps://www.emerald.com/insight/content/doi/10.1108/IJICC-08-2023-0230/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically. This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant). Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively. The present study can benefit dentists from using the DL model to more accurately diagnose dental problems. This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.Deep learning architectures in dental diagnostics: a systematic comparison of techniques for accurate prediction of dental disease through x-ray imaging
Muhammad Adnan Hasnain, Hassaan Malik, Muhammad Mujtaba Asad, Fahad Sherwani
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.161-180

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

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Deep learning architectures in dental diagnostics: a systematic comparison of techniques for accurate prediction of dental disease through x-ray imaging10.1108/IJICC-08-2023-0230International Journal of Intelligent Computing and Cybernetics2023-10-30© 2023 Emerald Publishing LimitedMuhammad Adnan HasnainHassaan MalikMuhammad Mujtaba AsadFahad SherwaniInternational Journal of Intelligent Computing and Cybernetics1712023-10-3010.1108/IJICC-08-2023-0230https://www.emerald.com/insight/content/doi/10.1108/IJICC-08-2023-0230/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
MBC-Net: long-range enhanced feature fusion for classifying remote sensing imageshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0198/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestClassification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements. This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI. Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature. MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.MBC-Net: long-range enhanced feature fusion for classifying remote sensing images
Huaxiang Song
International Journal of Intelligent Computing and Cybernetics, Vol. 17, No. 1, pp.181-209

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

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MBC-Net: long-range enhanced feature fusion for classifying remote sensing images10.1108/IJICC-07-2023-0198International Journal of Intelligent Computing and Cybernetics2023-10-19© 2023 Emerald Publishing LimitedHuaxiang SongInternational Journal of Intelligent Computing and Cybernetics1712023-10-1910.1108/IJICC-07-2023-0198https://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0198/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Improved particle swarm optimization based on multi-strategy fusion for UAV path planninghttps://www.emerald.com/insight/content/doi/10.1108/IJICC-06-2023-0140/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestPath planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path. Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning. Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality. Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.Improved particle swarm optimization based on multi-strategy fusion for UAV path planning
Zijing Ye, Huan Li, Wenhong Wei
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

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Improved particle swarm optimization based on multi-strategy fusion for UAV path planning10.1108/IJICC-06-2023-0140International Journal of Intelligent Computing and Cybernetics2023-10-24© 2023 Emerald Publishing LimitedZijing YeHuan LiWenhong WeiInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2023-10-2410.1108/IJICC-06-2023-0140https://www.emerald.com/insight/content/doi/10.1108/IJICC-06-2023-0140/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Fatal structure fire classification from building fire data using machine learninghttps://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0167/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019. Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with. The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%). Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models. The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.Fatal structure fire classification from building fire data using machine learning
Vimala Balakrishnan, Aainaa Nadia Mohammed Hashim, Voon Chung Lee, Voon Hee Lee, Ying Qiu Lee
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

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Fatal structure fire classification from building fire data using machine learning10.1108/IJICC-07-2023-0167International Journal of Intelligent Computing and Cybernetics2023-11-03© 2023 Emerald Publishing LimitedVimala BalakrishnanAainaa Nadia Mohammed HashimVoon Chung LeeVoon Hee LeeYing Qiu LeeInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2023-11-0310.1108/IJICC-07-2023-0167https://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0167/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fieldshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0174/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestTaking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase. The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system. The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility. By expanding the dimensions of the problem, the model verification space grows exponentially using automata. Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields
Majid Rahi, Ali Ebrahimnejad, Homayun Motameni
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

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Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields10.1108/IJICC-07-2023-0174International Journal of Intelligent Computing and Cybernetics2023-12-21© 2023 Emerald Publishing LimitedMajid RahiAli EbrahimnejadHomayun MotameniInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2023-12-2110.1108/IJICC-07-2023-0174https://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0174/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Exploring the differentiated elderly service subsidies considering consumer word-of-mouth preferenceshttps://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0189/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government. Evolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted. The findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies. Compared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.Exploring the differentiated elderly service subsidies considering consumer word-of-mouth preferences
Keqing Li, Xiaojia Wang, Changyong Liang, Wenxing Lu
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

The elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government.

Evolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted.

The findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies.

Compared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.

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Exploring the differentiated elderly service subsidies considering consumer word-of-mouth preferences10.1108/IJICC-07-2023-0189International Journal of Intelligent Computing and Cybernetics2023-11-20© 2023 Emerald Publishing LimitedKeqing LiXiaojia WangChangyong LiangWenxing LuInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2023-11-2010.1108/IJICC-07-2023-0189https://www.emerald.com/insight/content/doi/10.1108/IJICC-07-2023-0189/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter datahttps://www.emerald.com/insight/content/doi/10.1108/IJICC-09-2023-0251/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet. This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM. Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected. This study implements in Indonesia region. (a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data
Abba Suganda Girsang, Bima Krisna Noveta
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

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Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data10.1108/IJICC-09-2023-0251International Journal of Intelligent Computing and Cybernetics2024-01-03© 2023 Emerald Publishing LimitedAbba Suganda GirsangBima Krisna NovetaInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2024-01-0310.1108/IJICC-09-2023-0251https://www.emerald.com/insight/content/doi/10.1108/IJICC-09-2023-0251/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithmhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-09-2023-0269/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestA cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods. Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm
Miao Ye, Lin Qiang Huang, Xiao Li Wang, Yong Wang, Qiu Xiang Jiang, Hong Bing Qiu
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

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A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm10.1108/IJICC-09-2023-0269International Journal of Intelligent Computing and Cybernetics2024-01-03© 2023 Emerald Publishing LimitedMiao YeLin Qiang HuangXiao Li WangYong WangQiu Xiang JiangHong Bing QiuInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2024-01-0310.1108/IJICC-09-2023-0269https://www.emerald.com/insight/content/doi/10.1108/IJICC-09-2023-0269/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrievalhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-10-2023-0302/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency. To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence. Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval. This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval
Jinchao Huang
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

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Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval10.1108/IJICC-10-2023-0302International Journal of Intelligent Computing and Cybernetics2023-12-19© 2023 Emerald Publishing LimitedJinchao HuangInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2023-12-1910.1108/IJICC-10-2023-0302https://www.emerald.com/insight/content/doi/10.1108/IJICC-10-2023-0302/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular usershttps://www.emerald.com/insight/content/doi/10.1108/IJICC-11-2023-0329/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestSince the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS). An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm. The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE. There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users
Lin Kang, Jie Wang, Junjie Chen, Di Yang
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

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Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users10.1108/IJICC-11-2023-0329International Journal of Intelligent Computing and Cybernetics2024-01-25© 2024 Emerald Publishing LimitedLin KangJie WangJunjie ChenDi YangInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2024-01-2510.1108/IJICC-11-2023-0329https://www.emerald.com/insight/content/doi/10.1108/IJICC-11-2023-0329/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptronhttps://www.emerald.com/insight/content/doi/10.1108/IJICC-11-2023-0385/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem. The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately. The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components. Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron
Mohd Mustaqeem, Suhel Mustajab, Mahfooz Alam
International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print, No. ahead-of-print, pp.-

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

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A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron10.1108/IJICC-11-2023-0385International Journal of Intelligent Computing and Cybernetics2024-03-22© 2024 Emerald Publishing LimitedMohd MustaqeemSuhel MustajabMahfooz AlamInternational Journal of Intelligent Computing and Cyberneticsahead-of-printahead-of-print2024-03-2210.1108/IJICC-11-2023-0385https://www.emerald.com/insight/content/doi/10.1108/IJICC-11-2023-0385/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited