Pub Date : 2023-06-26DOI: 10.1108/ijicc-02-2023-0020
Somia Boubedra, C. Tolba, P. Manzoni, Djamila Beddiar, Y. Zennir
PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.
{"title":"Urban traffic flow management on large scale using an improved ACO for a road transportation system","authors":"Somia Boubedra, C. Tolba, P. Manzoni, Djamila Beddiar, Y. Zennir","doi":"10.1108/ijicc-02-2023-0020","DOIUrl":"https://doi.org/10.1108/ijicc-02-2023-0020","url":null,"abstract":"PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44387467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1108/ijicc-02-2023-0034
Liang Gong, Hang Dong, Xin Cheng, Zhenghui Ge, Liangchao Guo
PurposeThe purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.Design/methodology/approachThis study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.FindingsThe experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.Originality/valueThis study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.
{"title":"Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism","authors":"Liang Gong, Hang Dong, Xin Cheng, Zhenghui Ge, Liangchao Guo","doi":"10.1108/ijicc-02-2023-0034","DOIUrl":"https://doi.org/10.1108/ijicc-02-2023-0034","url":null,"abstract":"PurposeThe purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.Design/methodology/approachThis study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.FindingsThe experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.Originality/valueThis study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45012670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.34133/icomputing.0040
Hongbin Zhang, Mingjie Xing, Y. Wu, Chen Zhao
{"title":"Compiler Technologies in Deep Learning Co-Design: A Survey","authors":"Hongbin Zhang, Mingjie Xing, Y. Wu, Chen Zhao","doi":"10.34133/icomputing.0040","DOIUrl":"https://doi.org/10.34133/icomputing.0040","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"40 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90203978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1108/ijicc-02-2023-0027
Maas Sherina Sally
PurposeThe motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings. Thus, these causes lead to the dissatisfaction of customers. Additionally, these insights reflect the overall rating of the app and it is a source of information to the executive management to contemplate on their services and take timely and effective decisions to improve their mobile app.Design/methodology/approachThis research was conducted on ten reputed Sri Lankan mobile banking apps to analyze the textual opinions of the customers. Data were collected from the Google Play Store considering the higher Android consumers in Sri Lanka. Each review was automatically classified into a relevant sentiment (positive, negative or neutral). These classified reviews were examined along with its rating to identify any discrepancies. The trends of the positive and negative reviews of each app were observed separately along with time. Topic modeling techniques were used to identify the causes of such behavior.FindingsAlthough banks expect to perpetuate good customer reviews all the time, there were aberrant negative trends observed during certain time ranges. The results revealed that unstable versions after recent updates, bad customer service, erroneous functional and nonfunctional features are the root causes toward the dissatisfaction of the customers.Originality/valueNo previous study has been done on the textual reviews of Sri Lankan mobile banking apps. Most studies had considered analyzing the reviews of the app on the entire period of its usage, whereas this research finds the trends where negative reviews surpass the positive reviews and analyze the causes of such behavior.
{"title":"Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps","authors":"Maas Sherina Sally","doi":"10.1108/ijicc-02-2023-0027","DOIUrl":"https://doi.org/10.1108/ijicc-02-2023-0027","url":null,"abstract":"PurposeThe motivation of this study is to identify whether the overall rating of a banking app actually reflects the customer opinion and to find the causes for reduced ratings. Thus, these causes lead to the dissatisfaction of customers. Additionally, these insights reflect the overall rating of the app and it is a source of information to the executive management to contemplate on their services and take timely and effective decisions to improve their mobile app.Design/methodology/approachThis research was conducted on ten reputed Sri Lankan mobile banking apps to analyze the textual opinions of the customers. Data were collected from the Google Play Store considering the higher Android consumers in Sri Lanka. Each review was automatically classified into a relevant sentiment (positive, negative or neutral). These classified reviews were examined along with its rating to identify any discrepancies. The trends of the positive and negative reviews of each app were observed separately along with time. Topic modeling techniques were used to identify the causes of such behavior.FindingsAlthough banks expect to perpetuate good customer reviews all the time, there were aberrant negative trends observed during certain time ranges. The results revealed that unstable versions after recent updates, bad customer service, erroneous functional and nonfunctional features are the root causes toward the dissatisfaction of the customers.Originality/valueNo previous study has been done on the textual reviews of Sri Lankan mobile banking apps. Most studies had considered analyzing the reviews of the app on the entire period of its usage, whereas this research finds the trends where negative reviews surpass the positive reviews and analyze the causes of such behavior.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44631394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-14DOI: 10.1108/ijicc-12-2022-0306
Fatima Saeedi Aval Noughabia, N. Malekmohammadi, F. Hosseinzadeh lotfi, S. Razavyan
PurposeThe purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.Design/methodology/approachIn this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.FindingsA new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.Practical implicationsThe implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.Originality/valueIn some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.
{"title":"Efficiency decomposition in three-stage network with fuzzy desirable and undesirable output and fuzzy input in data envelopment analysis","authors":"Fatima Saeedi Aval Noughabia, N. Malekmohammadi, F. Hosseinzadeh lotfi, S. Razavyan","doi":"10.1108/ijicc-12-2022-0306","DOIUrl":"https://doi.org/10.1108/ijicc-12-2022-0306","url":null,"abstract":"PurposeThe purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.Design/methodology/approachIn this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.FindingsA new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.Practical implicationsThe implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.Originality/valueIn some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49464418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-04DOI: 10.34133/icomputing.0021
Tingting Jiang, Chenyang Bu, Yi Zhu, Xin Wu
{"title":"Integrating symbol similarities with knowledge graph embedding for entity alignment: an unsupervised framework","authors":"Tingting Jiang, Chenyang Bu, Yi Zhu, Xin Wu","doi":"10.34133/icomputing.0021","DOIUrl":"https://doi.org/10.34133/icomputing.0021","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"122 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90869081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-30DOI: 10.1108/ijicc-12-2022-0312
W. Chanhemo, M. H. Mohsini, Mohamedi M. Mjahidi, Florence Rashidi
PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.
{"title":"Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions","authors":"W. Chanhemo, M. H. Mohsini, Mohamedi M. Mjahidi, Florence Rashidi","doi":"10.1108/ijicc-12-2022-0312","DOIUrl":"https://doi.org/10.1108/ijicc-12-2022-0312","url":null,"abstract":"PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46471677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.34133/icomputing.0017
Guokai Zhang, Ning Xu, C. Yan, Bolun Zheng, Yulong Duan, Bo Lv, Anjin Liu
{"title":"CD-GAN: Commonsense-driven Generative Adversarial Network with Hierarchical Refinement for Text-to-Image Synthesis","authors":"Guokai Zhang, Ning Xu, C. Yan, Bolun Zheng, Yulong Duan, Bo Lv, Anjin Liu","doi":"10.34133/icomputing.0017","DOIUrl":"https://doi.org/10.34133/icomputing.0017","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"150 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75097099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.34133/icomputing.0012
Taoping Liu, L. Guo, Mou Wang, Chen Su, Di Wang, Hao Dong, Jingdong Chen, Weiwei Wu
{"title":"Review on Algorithm Design in Electronic Noses: Challenges, Status, and Trends","authors":"Taoping Liu, L. Guo, Mou Wang, Chen Su, Di Wang, Hao Dong, Jingdong Chen, Weiwei Wu","doi":"10.34133/icomputing.0012","DOIUrl":"https://doi.org/10.34133/icomputing.0012","url":null,"abstract":"","PeriodicalId":45291,"journal":{"name":"International Journal of Intelligent Computing and Cybernetics","volume":"57 6","pages":""},"PeriodicalIF":4.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72482904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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