Pub Date : 2025-08-01DOI: 10.1016/j.icte.2025.05.004
Hyein Seo , Jae-Ho Park , Jangho Lee , Byung Chang Chung
Identifying informative features in bioinformatics is challenging due to their small proportion within large datasets. We propose a scalable and interpretable feature selection framework for cancer RNA-seq by transforming non-image bio-data into 2D formats and applying convolutional neural networks (CNNs) with transfer learning for efficient classification. Explainable artificial intelligence (XAI) techniques identify and prioritize important features, while principal component analysis (PCA) determines the optimal number of selected features, ensuring transparency and reliability. Comparative analysis of CNN and XAI highlights the effectiveness of our approach, providing a robust framework for high-dimensional genomic data analysis with applications in cancer diagnosis and prognosis.
{"title":"Explainable AI based feature selection in cancer RNA-seq","authors":"Hyein Seo , Jae-Ho Park , Jangho Lee , Byung Chang Chung","doi":"10.1016/j.icte.2025.05.004","DOIUrl":"10.1016/j.icte.2025.05.004","url":null,"abstract":"<div><div>Identifying informative features in bioinformatics is challenging due to their small proportion within large datasets. We propose a scalable and interpretable feature selection framework for cancer RNA-seq by transforming non-image bio-data into 2D formats and applying convolutional neural networks (CNNs) with transfer learning for efficient classification. Explainable artificial intelligence (XAI) techniques identify and prioritize important features, while principal component analysis (PCA) determines the optimal number of selected features, ensuring transparency and reliability. Comparative analysis of CNN and XAI highlights the effectiveness of our approach, providing a robust framework for high-dimensional genomic data analysis with applications in cancer diagnosis and prognosis.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 603-610"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.icte.2025.05.006
Qin Yang, Sang-Jo Yoo
Task offloading in multi-access edge computing (MEC) systems is critical for managing computational tasks in dynamic urban environments. Existing strategies face challenges such as high communication overheads and regional performance deviations including centralized and distributed methods. Clustering approaches have been explored to address these issues, yet they often rely on physical proximity to form clusters, overlooking the variability in task rate distributions across edges. To overcome these limitations, this paper proposes a graph-driven inter-cluster resource distribution (GIRD) clustering scheme that clusters edge nodes based on task request distribution and computing resource status, ensuring similar resource utilization across clusters. Building on this, a proximal policy optimization (PPO)-enabled intra-cluster task offloading algorithm (PITO) is introduced to determine one execution server for task offloading—either an edge server within a cluster or a cloud server—using various network state information. This dynamic decision-making process optimizes a multi-objective function that includes task processing delay, consumed energy, success rate, and cloud cost. Simulation results demonstrate the proposed GIRD-PITO framework achieves superior task success rates, reduced delays, and improved regional performance fairness, making it a promising solution for large-scale MEC systems. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
{"title":"Multi-objective task offloading optimization using deep reinforcement learning with resource distribution clustering","authors":"Qin Yang, Sang-Jo Yoo","doi":"10.1016/j.icte.2025.05.006","DOIUrl":"10.1016/j.icte.2025.05.006","url":null,"abstract":"<div><div>Task offloading in multi-access edge computing (MEC) systems is critical for managing computational tasks in dynamic urban environments. Existing strategies face challenges such as high communication overheads and regional performance deviations including centralized and distributed methods. Clustering approaches have been explored to address these issues, yet they often rely on physical proximity to form clusters, overlooking the variability in task rate distributions across edges. To overcome these limitations, this paper proposes a graph-driven inter-cluster resource distribution (GIRD) clustering scheme that clusters edge nodes based on task request distribution and computing resource status, ensuring similar resource utilization across clusters. Building on this, a proximal policy optimization (PPO)-enabled intra-cluster task offloading algorithm (PITO) is introduced to determine one execution server for task offloading—either an edge server within a cluster or a cloud server—using various network state information. This dynamic decision-making process optimizes a multi-objective function that includes task processing delay, consumed energy, success rate, and cloud cost. Simulation results demonstrate the proposed GIRD-PITO framework achieves superior task success rates, reduced delays, and improved regional performance fairness, making it a promising solution for large-scale MEC systems. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (<span><span>http://creativecommons.org/licenses/by-nc-nd/4.0/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 734-742"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.icte.2025.06.003
Keunho Byeon, Jeewoo Lim, Jin Tae Kwak
Blurred images often result from camera shake or object motion, complicating the visual inspection and recognition of objects. To address this issue, we propose DeLECA, a dual-branch Transformer architecture that leverages the complementary nature of the paired blurred images, obtained with long-exposure times, and noisy images, captured with short exposure times, to improve the quality and sharpness of the blurred images. We evaluate DeLECA using two public datasets, GoPro and HIDE. Experimental results show that DeLECA outperforms existing methods, achieving PSNR of 36.08 dB and SSIM of 0.965 on the GoPro dataset, and 40.05 dB and 0.972 on the HIDE dataset.
{"title":"DeLECA: Deblurring for Long and short Exposure images with a dual-branch multimodal cross attention mechanism","authors":"Keunho Byeon, Jeewoo Lim, Jin Tae Kwak","doi":"10.1016/j.icte.2025.06.003","DOIUrl":"10.1016/j.icte.2025.06.003","url":null,"abstract":"<div><div>Blurred images often result from camera shake or object motion, complicating the visual inspection and recognition of objects. To address this issue, we propose DeLECA, a dual-branch Transformer architecture that leverages the complementary nature of the paired blurred images, obtained with long-exposure times, and noisy images, captured with short exposure times, to improve the quality and sharpness of the blurred images. We evaluate DeLECA using two public datasets, GoPro and HIDE. Experimental results show that DeLECA outperforms existing methods, achieving PSNR of 36.08 dB and SSIM of 0.965 on the GoPro dataset, and 40.05 dB and 0.972 on the HIDE dataset.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 611-617"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Vehicles (IoV) requires secure and efficient authentication. This study proposes a decentralized protocol leveraging edge nodes and consortium blockchain to enhance security while reducing cloud dependency. A mathematical model evaluates performance and scalability, while simulations validate resilience against network failures, attacks, and topology changes. The protocol integrates with IoT infrastructure and considers implementation costs. Results demonstrate improved efficiency, security, and feasibility for large-scale vehicular networks.
{"title":"Edge-enhanced decentralized vehicle authentication protocol for IoV","authors":"Nai-Wei Lo , Wen-Hsien Yu , Jheng-Jia Huang , Yu-Chi Chen","doi":"10.1016/j.icte.2025.06.013","DOIUrl":"10.1016/j.icte.2025.06.013","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) requires secure and efficient authentication. This study proposes a decentralized protocol leveraging edge nodes and consortium blockchain to enhance security while reducing cloud dependency. A mathematical model evaluates performance and scalability, while simulations validate resilience against network failures, attacks, and topology changes. The protocol integrates with IoT infrastructure and considers implementation costs. Results demonstrate improved efficiency, security, and feasibility for large-scale vehicular networks.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 624-630"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, reconfigurable intelligent surface (RIS) technology has provided new opportunities to rescale the performance metric and boost network coverage for sixth-generation (6G) wireless communications. In RIS-assisted wireless communication systems, traditional analog beamforming requires a huge number of phase shifters, which increases exponential and computational complexity. In order to accomplish computationally efficient hybrid beamforming for 6G communications, we therefore propose a branchwise phase shifters (BPS) architecture of analog beamforming in this paper. To establish the best baseband configuration for wireless 6G communications, we first consider an RIS element and a transmitter antenna branchwise millimeter wideband (B-MW) channel model. Following that, we design a BPS-based analog beamforming architecture using a power splitting technique, which splits the transmit power associated with each RF chain and allows each RF chain to utilize two PS branches. The B-MW channel can easily provide a low-dimensional hybrid beamforming solution with the BPS architecture, which allows easy rescaling of the beamforming gain, thus enhancing the achievable rate. We then present an iterative algorithm with zero-forcing to tackle a joint optimization problem of RIS-assisted hybrid beamforming. Lastly, the beam coverage and achievable rate of the proposed BPS architecture are verified by the simulation results, which outperform the traditional single, double, and combined phase-shifter architecture.
{"title":"Hybrid beamforming with branchwise phase shifters for RIS-Assisted 6G wireless communications","authors":"Md Abdul Latif Sarker , Poongundran Selvaprabhu , Vinoth Babu Kumaravelu , Sunil Chinnadurai , Benaoumeur Senouci , Dong Seog Han","doi":"10.1016/j.icte.2025.06.014","DOIUrl":"10.1016/j.icte.2025.06.014","url":null,"abstract":"<div><div>Recently, reconfigurable intelligent surface (RIS) technology has provided new opportunities to rescale the performance metric and boost network coverage for sixth-generation (6G) wireless communications. In RIS-assisted wireless communication systems, traditional analog beamforming requires a huge number of phase shifters, which increases exponential and computational complexity. In order to accomplish computationally efficient hybrid beamforming for 6G communications, we therefore propose a branchwise phase shifters (BPS) architecture of analog beamforming in this paper. To establish the best baseband configuration for wireless 6G communications, we first consider an RIS element and a transmitter antenna branchwise millimeter wideband (B-MW) channel model. Following that, we design a BPS-based analog beamforming architecture using a power splitting technique, which splits the transmit power associated with each RF chain and allows each RF chain to utilize two PS branches. The B-MW channel can easily provide a low-dimensional hybrid beamforming solution with the BPS architecture, which allows easy rescaling of the beamforming gain, thus enhancing the achievable rate. We then present an iterative algorithm with zero-forcing to tackle a joint optimization problem of RIS-assisted hybrid beamforming. Lastly, the beam coverage and achievable rate of the proposed BPS architecture are verified by the simulation results, which outperform the traditional single, double, and combined phase-shifter architecture.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 618-623"},"PeriodicalIF":4.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.icte.2025.04.002
Jasiya Fairiz Raisa , Md. Sazzadur Rahman , Imtiaz Mahmud , M. Shamim Kaiser , Dong Seog Han
The COVID-19 pandemic has prompted healthcare advancements, enhanced security and efficiency, and reduced disease transmission. The industry rapidly addresses technological gaps and growing service demands. The pandemic has driven a pivotal shift from Healthcare 4.0 to Healthcare 5.0. Healthcare 4.0 emphasizes technology-centered care, whereas Healthcare 5.0 prioritizes patient-centric, personalized, and participatory approaches. This review assesses COVID-19’s impact on this transition, comparing pre-pandemic and pandemic literature. It examines how digital tools enable healthcare providers to deliver patient-centered and personalized care, fostering a shift from Healthcare 4.0 to Healthcare 5.0.
{"title":"Transition toward Healthcare 5.0: Impact of COVID-19 in the healthcare industry","authors":"Jasiya Fairiz Raisa , Md. Sazzadur Rahman , Imtiaz Mahmud , M. Shamim Kaiser , Dong Seog Han","doi":"10.1016/j.icte.2025.04.002","DOIUrl":"10.1016/j.icte.2025.04.002","url":null,"abstract":"<div><div>The COVID-19 pandemic has prompted healthcare advancements, enhanced security and efficiency, and reduced disease transmission. The industry rapidly addresses technological gaps and growing service demands. The pandemic has driven a pivotal shift from Healthcare 4.0 to Healthcare 5.0. Healthcare 4.0 emphasizes technology-centered care, whereas Healthcare 5.0 prioritizes patient-centric, personalized, and participatory approaches. This review assesses COVID-19’s impact on this transition, comparing pre-pandemic and pandemic literature. It examines how digital tools enable healthcare providers to deliver patient-centered and personalized care, fostering a shift from Healthcare 4.0 to Healthcare 5.0.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 3","pages":"Pages 371-389"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.icte.2025.02.004
Igbafe Orikumhi , Nakyung Lee , Jungsook Bae , Sunwoo Kim
This paper proposes a selective sensing information update scheme for communication between a base station (BS) and multiple unmanned aerial vehicles (UAVs) in a relaying network. The UAVs, equipped with integrated sensing and communication (ISAC) capabilities, utilize environmental sensing measurements to facilitate communication in blockage-prone scenarios, such as disaster recovery and remote connectivity. Specifically, user parameters are estimated from the sensing measurements at the UAVs. Given the non-linear relationship between the sensing measurements and the communication parameters, we propose a two-stage design approach. In the first stage, the measurements are transformed into a range-and-angle-based model to determine the optimal beam between the UAVs and multiple mobile user equipment (UEs). In the second stage, a selective update mechanism, guided by a predictive rate criterion, is used to optimize the frequency of sensing information updates between the BS and the UAVs. Simulation results demonstrate the superiority of the proposed method and show an improvement in the system throughput compared to baseline schemes, while significantly reducing sensing overhead.
{"title":"A selective update sensing approach for UAV-ISAC enabled relaying network","authors":"Igbafe Orikumhi , Nakyung Lee , Jungsook Bae , Sunwoo Kim","doi":"10.1016/j.icte.2025.02.004","DOIUrl":"10.1016/j.icte.2025.02.004","url":null,"abstract":"<div><div>This paper proposes a selective sensing information update scheme for communication between a base station (BS) and multiple unmanned aerial vehicles (UAVs) in a relaying network. The UAVs, equipped with integrated sensing and communication (ISAC) capabilities, utilize environmental sensing measurements to facilitate communication in blockage-prone scenarios, such as disaster recovery and remote connectivity. Specifically, user parameters are estimated from the sensing measurements at the UAVs. Given the non-linear relationship between the sensing measurements and the communication parameters, we propose a two-stage design approach. In the first stage, the measurements are transformed into a range-and-angle-based model to determine the optimal beam between the UAVs and multiple mobile user equipment (UEs). In the second stage, a selective update mechanism, guided by a predictive rate criterion, is used to optimize the frequency of sensing information updates between the BS and the UAVs. Simulation results demonstrate the superiority of the proposed method and show an improvement in the system throughput compared to baseline schemes, while significantly reducing sensing overhead.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 3","pages":"Pages 481-485"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.icte.2025.03.004
Joeun Kim , Youngil Jeon , Junhwan Lee , Moon-Sik Lee , Taesoo Kwon
In recent wireless networks, the integrated access and backhaul (IAB) network provides a cost-effective solution to enhance network performance and has emerged as a key technology not only for beyond 5G but also for 6G. Because of the inherent nature of IAB, access and backhaul links share the same resource pool causing cross-link interference. To address this challenge, this paper investigates an algorithm based on reinforcement learning (RL) for joint scheduling and resource allocation (RA) problem, aiming to mitigate interference and enhance user data rates. However, the scale of this joint problem is too large to solve using RL alone. Therefore, this paper proposes decomposing the joint problem into virtual scheduling and RL-based RA (RL-RA), and then solving them collaboratively. Simulation results also show that the proposed algorithm significantly improves performance and can be applied comprehensively to various duplex modes, including half and full duplex types, and different frequency bands, such as sub-6GHz and mmWave.
{"title":"Joint scheduling and resource allocation based on reinforcement learning in integrated access and backhaul networks","authors":"Joeun Kim , Youngil Jeon , Junhwan Lee , Moon-Sik Lee , Taesoo Kwon","doi":"10.1016/j.icte.2025.03.004","DOIUrl":"10.1016/j.icte.2025.03.004","url":null,"abstract":"<div><div>In recent wireless networks, the integrated access and backhaul (IAB) network provides a cost-effective solution to enhance network performance and has emerged as a key technology not only for beyond 5G but also for 6G. Because of the inherent nature of IAB, access and backhaul links share the same resource pool causing cross-link interference. To address this challenge, this paper investigates an algorithm based on reinforcement learning (RL) for joint scheduling and resource allocation (RA) problem, aiming to mitigate interference and enhance user data rates. However, the scale of this joint problem is too large to solve using RL alone. Therefore, this paper proposes decomposing the joint problem into virtual scheduling and RL-based RA (RL-RA), and then solving them collaboratively. Simulation results also show that the proposed algorithm significantly improves performance and can be applied comprehensively to various duplex modes, including half and full duplex types, and different frequency bands, such as sub-6GHz and mmWave.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 3","pages":"Pages 536-541"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.icte.2025.03.009
Yunus Emre Ekici , Teoman Karadağ , Ozan Akdağ , Ahmet Arif Aydin , Hüseyin Ozan Tekin
Electrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in Türkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in Türkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO2, NO, and NO2 emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.
{"title":"Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach","authors":"Yunus Emre Ekici , Teoman Karadağ , Ozan Akdağ , Ahmet Arif Aydin , Hüseyin Ozan Tekin","doi":"10.1016/j.icte.2025.03.009","DOIUrl":"10.1016/j.icte.2025.03.009","url":null,"abstract":"<div><div>Electrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in Türkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in Türkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO<sub>2</sub>, NO, and NO<sub>2</sub> emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 3","pages":"Pages 547-558"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.icte.2025.03.005
Woong-Shin Kwak, Jae-Won Kim
In this paper, we propose a new design metric called modified outage probability (MOP) for protograph-based low-density parity-check (LDPC) codes over the uncorrelated Rayleigh fading channel. In contrast to the conventional threshold metric, the MOP predicts the frame error rate (FER) performance accurately over the fading channel. Based on MOPs, we optimize protograph-based LDPC codes while considering the trade-off between accuracy and computational complexity. It is shown that the optimized LDPC codes outperform the conventional threshold-based LDPC codes at the FER of for the both medium and high code-rate regions.
{"title":"Design of LDPC code for uncorrelated Rayleigh fading channel using modified outage probability","authors":"Woong-Shin Kwak, Jae-Won Kim","doi":"10.1016/j.icte.2025.03.005","DOIUrl":"10.1016/j.icte.2025.03.005","url":null,"abstract":"<div><div>In this paper, we propose a new design metric called modified outage probability (MOP) for protograph-based low-density parity-check (LDPC) codes over the uncorrelated Rayleigh fading channel. In contrast to the conventional threshold metric, the MOP predicts the frame error rate (FER) performance accurately over the fading channel. Based on MOPs, we optimize protograph-based LDPC codes while considering the trade-off between accuracy and computational complexity. It is shown that the optimized LDPC codes outperform the conventional threshold-based LDPC codes at the FER of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span> for the both medium and high code-rate regions.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 3","pages":"Pages 442-447"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号