Pub Date : 2026-03-01Epub Date: 2026-01-12DOI: 10.1016/j.eij.2026.100886
Ahmet Okan Arık , Gizem Parlayandemir , Serra Çelik
Political fake news fuels a significant epistemic crisis, yet detection in low-resource languages like Turkish is constrained by data scarcity and class imbalance. This study addresses these challenges by constructing the Turkish Political Fake News Dataset (TPFND) and employing a Turkish LLaMA-3 model to generate synthetic samples for data augmentation. The augmented dataset was used to train an XGBoost classifier, compared against baseline and Random Oversampling methods. Results demonstrate that LLM-based augmentation significantly enhances sensitivity to fake news. While overall accuracy remained high 89–90.5%, the fake news detection rate increased from 91.12% to 97.62%, effectively minimizing false negatives despite a slight precision trade-off. These findings confirm the methodology provides a robust “safety net” for the Turkish digital ecosystem and a scalable framework for other low-resource languages.
{"title":"LLM-based data augmentation for text classification on imbalanced datasets: A case study on fake news detection","authors":"Ahmet Okan Arık , Gizem Parlayandemir , Serra Çelik","doi":"10.1016/j.eij.2026.100886","DOIUrl":"10.1016/j.eij.2026.100886","url":null,"abstract":"<div><div>Political fake news fuels a significant epistemic crisis, yet detection in low-resource languages like Turkish is constrained by data scarcity and class imbalance. This study addresses these challenges by constructing the Turkish Political Fake News Dataset (TPFND) and employing a Turkish LLaMA-3 model to generate synthetic samples for data augmentation. The augmented dataset was used to train an XGBoost classifier, compared against baseline and Random Oversampling methods. Results demonstrate that LLM-based augmentation significantly enhances sensitivity to fake news. While overall accuracy remained high 89–90.5%, the fake news detection rate increased from 91.12% to 97.62%, effectively minimizing false negatives despite a slight precision trade-off. These findings confirm the methodology provides a robust “safety net” for the Turkish digital ecosystem and a scalable framework for other low-resource languages.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100886"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977566","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 : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.eij.2026.100893
Muhammad Rizalul Wahid , Endra Joelianto , Bentang Arief Budiman , Muhamad Praja Dewanata , Muhammad Aziz
The low mass, limited motor capacity, and small battery size of light electric vehicles (LEVs) constrain the regenerative energy recovery process, limiting the driving range of these vehicles and in turn their widespread use. An effective regenerative braking control strategy is required to maximize energy recovery while maintaining brake stability. This paper presents the modeling and experimental validation of three regenerative braking control strategies for LEVs: a baseline (original) controller, an interval type-2 fuzzy logic controller, and a deep deterministic policy gradient reinforcement learning (DDPG-RL) controller. Under the worldwide harmonized light vehicles test cycle (WLTC) Class 1 driving cycle, the DDPG-RL controller achieved the best performance, yielding the lowest energy consumption of 1.99 kWh, highest regenerative energy contribution of 18.15 %, and highest energy efficiency of 12.59 km/kWh, corresponding to a 15.4 % increase in driving range over the baseline (original) controller. A kernel density estimation analysis also revealed that DDPG-RL exhibited the most consistent and intense regenerative power distribution, particularly in the 20–40 km/h range, which is typical for urban driving. The baseline model was experimentally validated to ensure the power flow representation accuracy. The results revealed a mean absolute error of 0.17 % in the battery state of charge and a final deviation of 0.33 %, thus verifying the reliability of the comparative evaluation. These results validate the DDPG-RL strategy as a highly effective approach for maximizing energy recovery, reducing consumption, and extending the driving range, thus being a potential solution for the sustainable optimization of LEVs.
{"title":"Optimizing regenerative braking in light electric vehicles using deep deterministic policy gradient reinforcement learning","authors":"Muhammad Rizalul Wahid , Endra Joelianto , Bentang Arief Budiman , Muhamad Praja Dewanata , Muhammad Aziz","doi":"10.1016/j.eij.2026.100893","DOIUrl":"10.1016/j.eij.2026.100893","url":null,"abstract":"<div><div>The low mass, limited motor capacity, and small battery size of light electric vehicles (LEVs) constrain the regenerative energy recovery process, limiting the driving range of these vehicles and in turn their widespread use. An effective regenerative braking control strategy is required to maximize energy recovery while maintaining brake stability. This paper presents the modeling and experimental validation of three regenerative braking control strategies for LEVs: a baseline (original) controller, an interval type-2 fuzzy logic controller, and a deep deterministic policy gradient reinforcement learning (DDPG-RL) controller. Under the worldwide harmonized light vehicles test cycle (WLTC) Class 1 driving cycle, the DDPG-RL controller achieved the best performance, yielding the lowest energy consumption of 1.99 kWh, highest regenerative energy contribution of 18.15 %, and highest energy efficiency of 12.59 km/kWh, corresponding to a 15.4 % increase in driving range over the baseline (original) controller. A kernel density estimation analysis also revealed that DDPG-RL exhibited the most consistent and intense regenerative power distribution, particularly in the 20–40 km/h range, which is typical for urban driving. The baseline model was experimentally validated to ensure the power flow representation accuracy. The results revealed a mean absolute error of 0.17 % in the battery state of charge and a final deviation of 0.33 %, thus verifying the reliability of the comparative evaluation. These results validate the DDPG-RL strategy as a highly effective approach for maximizing energy recovery, reducing consumption, and extending the driving range, thus being a potential solution for the sustainable optimization of LEVs.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100893"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187893","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 : 2026-03-01Epub Date: 2025-12-19DOI: 10.1016/j.eij.2025.100877
P. Sajitha , A. Diana Andrushia , N. Anand , Eva Lubloy
Fruits are the most vital items of global diets because of their rich nutritional value, thereby providing very high demand and agricultural revenues to the economy. Among the fruit crops, pomegranate is a valuable one due to its highest antioxidant potential. However, most crops of pomegranate suffer from diseases, which greatly reduce agricultural yield and productivity. Thus, along with the increasing demand of the fruit, early detection as well as classification of diseases will prove very crucial in boosting the yield and taking appropriate measures for prevention. We propose a segmentation-based model using deep learning in this paper to conduct disease identification in pomegranates The process begins with pre-processing images that is primarily an activity of cropping and resizing of the images, followed by enhanced Wiener filtering, which eliminates noise and enhances the clarity of the images The preprocessed images are then further segmented using a CA_YV5GC algorithm, (Channel Attentive YOLOv5-based Grab Cut), which isolates diseased regions from the images. Then the optimized ResNet-152 network is applied to acquire the fundamental features embedding the texture along with the shape characteristics which could identify ailments related symptoms. Coati Optimization is applied to choose the most dominant features in the lower dimensional representation of the extracted information for the classification of the disease. Ultimately, classification is performed using a Deep Capsule Canonical Auto-encoder (DC_CAENet) to classify the disease type with higher accuracy. Adaptive Osprey Optimization is used to optimize the parameters of the model. The existing methods are compared with that results proved this technique to be more accurate and efficient as compared to traditional techniques.
{"title":"Channel-attentive YOLOv5 and capsule auto-encoder for pomegranate disease detection","authors":"P. Sajitha , A. Diana Andrushia , N. Anand , Eva Lubloy","doi":"10.1016/j.eij.2025.100877","DOIUrl":"10.1016/j.eij.2025.100877","url":null,"abstract":"<div><div>Fruits are the most vital items of global diets because of their rich nutritional value, thereby providing very high demand and agricultural revenues to the economy. Among the fruit crops, pomegranate is a valuable one due to its highest antioxidant potential. However, most crops of pomegranate suffer from diseases, which greatly reduce agricultural yield and productivity. Thus, along with the increasing demand of the fruit, early detection as well as classification of diseases will prove very crucial in boosting the yield and taking appropriate measures for prevention. We propose a segmentation-based model using deep learning in this paper to conduct disease identification in pomegranates The process begins with pre-processing images that is primarily an activity of cropping and resizing of the images, followed by enhanced Wiener filtering, which eliminates noise and enhances the clarity of the images The preprocessed images are then further segmented using a CA_YV5GC algorithm, (Channel Attentive YOLOv5-based Grab Cut), which isolates diseased regions from the images. Then the optimized ResNet-152 network is applied to acquire the fundamental features embedding the texture along with the shape characteristics which could identify ailments related symptoms. Coati Optimization is applied to choose the most dominant features in the lower dimensional representation of the extracted information for the classification of the disease. Ultimately, classification is performed using a Deep Capsule Canonical Auto-encoder (DC_CAENet) to classify the disease type with higher accuracy. Adaptive Osprey Optimization is used to optimize the parameters of the model. The existing methods are compared with that results proved this technique to be more accurate and efficient as compared to traditional techniques.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100877"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791746","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 : 2026-03-01Epub Date: 2025-12-15DOI: 10.1016/j.eij.2025.100859
Nurzati Iwani Othman , Hassan Jamil Syed , Athirah Mohd Ramly , Nur Hanis Sabrina binti Suhaimi , Aitizaz Ali , Mohamed Abdulnabi , Ahmad Fadzil Ismail
The digital transformation of Industry 4.0 requires networking solutions that deliver ultra-low latency, energy efficiency, and robust security. Conventional 5G architectures face limitations such as high infrastructure costs, performance bottlenecks, and vulnerabilities in mission-critical environments. This study proposes the Private Hybrid Wireless Access Network (PHWAN) framework, a novel architecture that combines localized spectrum management, edge–cloud orchestration, and blockchain-based Zero Trust security. A comprehensive cost–benefit model and MATLAB-based simulation of an industrial IoT environment were used to evaluate PHWAN against traditional 5G deployments. Results show that PHWAN reduces latency by 50 % (0.5 ms to 0.25 ms), lowers energy consumption by 61 % (5.4 mJ to 2.1 mJ), and improves bandwidth utilization by 108 %. Security analysis further demonstrates improved access control and data integrity without incurring significant overhead. These findings establish PHWAN as a scalable and cost-effective alternative to 5G for delay-sensitive and resource-constrained industrial IoT applications. Future research will extend validation to standardized platforms such as NS-3 and 5G-LENA and explore integration with 6G spectrum slicing, quantum-secured communications, and industrial metaverse applications to enhance resilience and interoperability in next-generation smart factories.
{"title":"Beyond 5G: PHWAN – A secure, low-latency, and cost-effective framework for Industry 4.0 smart manufacturing","authors":"Nurzati Iwani Othman , Hassan Jamil Syed , Athirah Mohd Ramly , Nur Hanis Sabrina binti Suhaimi , Aitizaz Ali , Mohamed Abdulnabi , Ahmad Fadzil Ismail","doi":"10.1016/j.eij.2025.100859","DOIUrl":"10.1016/j.eij.2025.100859","url":null,"abstract":"<div><div>The digital transformation of Industry 4.0 requires networking solutions that deliver ultra-low latency, energy efficiency, and robust security. Conventional 5G architectures face limitations such as high infrastructure costs, performance bottlenecks, and vulnerabilities in mission-critical environments. This study proposes the Private Hybrid Wireless Access Network (PHWAN) framework, a novel architecture that combines localized spectrum management, edge–cloud orchestration, and blockchain-based Zero Trust security. A comprehensive cost–benefit model and MATLAB-based simulation of an industrial IoT environment were used to evaluate PHWAN against traditional 5G deployments. Results show that PHWAN reduces latency by 50 % (0.5 ms to 0.25 ms), lowers energy consumption by 61 % (5.4 mJ to 2.1 mJ), and improves bandwidth utilization by 108 %. Security analysis further demonstrates improved access control and data integrity without incurring significant overhead. These findings establish PHWAN as a scalable and cost-effective alternative to 5G for delay-sensitive and resource-constrained industrial IoT applications. Future research will extend validation to standardized platforms such as NS-3 and 5G-LENA and explore integration with 6G spectrum slicing, quantum-secured communications, and industrial metaverse applications to enhance resilience and interoperability in next-generation smart factories.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100859"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791748","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}
This study proposes a fully automated deep learning system based on the U-Net architecture for classifying mandibular third molars using the Pell & Gregory method. Novel anatomical landmarks were introduced and automatically detected on panoramic radiographs by the model. These landmarks were then used to determine the classification through their spatial relationships. The system was trained and evaluated using panoramic radiographs collected from different patients. Two independent datasets were constructed according to the side of mandibular third molar impaction: 373 images for the left jaw (teeth 37–38) and 328 for the right jaw (teeth 47–48). For the Pell & Gregory classification, the proposed approach achieved a classification accuracy of 93.24% for the left jaw and 91.30% for the right jaw, demonstrating consistent and reliable performance across both datasets. The model effectively localized anatomical points and classified third molars without manual input. This automated approach enhances diagnostic consistency and reduces observer variability, offering practical utility in clinical environments. Overall, the study demonstrates the potential of artificial intelligence to improve diagnostic workflows by providing a reliable tool for the automated classification of impacted third molars according to the Pell & Gregory system.
{"title":"Fully automated Pell & Gregory classification on panoramic radiographs","authors":"Betül Uzbaş , Fatma Büşra Doğan , Mogham Njikam Mohamed Nourdine , Şule Yücelbaş , Cüneyt Yücelbaş , Zeynep Betül Arslan , Füsun Yaşar","doi":"10.1016/j.eij.2026.100917","DOIUrl":"10.1016/j.eij.2026.100917","url":null,"abstract":"<div><div>This study proposes a fully automated deep learning system based on the U-Net architecture for classifying mandibular third molars using the Pell & Gregory method. Novel anatomical landmarks were introduced and automatically detected on panoramic radiographs by the model. These landmarks were then used to determine the classification through their spatial relationships. The system was trained and evaluated using panoramic radiographs collected from different patients. Two independent datasets were constructed according to the side of mandibular third molar impaction: 373 images for the left jaw (teeth 37–38) and 328 for the right jaw (teeth 47–48). For the Pell & Gregory classification, the proposed approach achieved a classification accuracy of 93.24% for the left jaw and 91.30% for the right jaw, demonstrating consistent and reliable performance across both datasets. The model effectively localized anatomical points and classified third molars without manual input. This automated approach enhances diagnostic consistency and reduces observer variability, offering practical utility in clinical environments. Overall, the study demonstrates the potential of artificial intelligence to improve diagnostic workflows by providing a reliable tool for the automated classification of impacted third molars according to the Pell & Gregory system.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100917"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396550","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 : 2026-03-01Epub Date: 2026-03-03DOI: 10.1016/j.eij.2026.100921
Adil Hussain , Qing-Chang Lu , Kashif Naseer Qureshi , Khalid Javeed
Electric Vehicles (EVs) charging pricing plays an important role in reducing the charging demand during peak hours and increasing Charging Station Operator (CSO) profits. However, the existing studies have overlooked the charging pile availability and the current Charging Stations (CS) occupancy. This study proposes a novel on-demand dynamic pricing strategy considering limited charging spaces and CS occupancy using the low and high occupancy thresholds, with low and high cost adjustments in the charging costs. The idle occupancy at the CSs with a limited number of spaces can reduce the CSO profit; therefore, the idle time penalty is also introduced. The real-world EV charging data of 6 CSs from 3 districts of Jiaxing city, China, is used. The case study also includes analysis of occupancy thresholds, cost adjustments, idle time penalty limits, and penalty costs. The findings show that the proposed strategy, including both algorithms, improved CSO profits across most EV charging sites as compared to Time-of-Use (ToU) pricing. The profits are increased by 8.019% with algorithm 1 and 9.603% with algorithm 2 for the Bus Station location. The Government Agency site achieved a 4.284% and 6.109% increase, while the Shopping Mall also increased by 3.315% and 5.107%, respectively. The Tourist Attraction location also experienced profit rises of 0.657% and 2.710%. Expressway Service District C and Financial Industrial Park showed a slight decrease of 0.237% and 0.299% with Algorithm 1, and improved by 1.824% and 1.442% using Algorithm 2, respectively. The results highlight that algorithm 2 consistently improves profit across all six CS locations.
{"title":"On-demand dynamic charging pricing strategy for Electric Vehicles","authors":"Adil Hussain , Qing-Chang Lu , Kashif Naseer Qureshi , Khalid Javeed","doi":"10.1016/j.eij.2026.100921","DOIUrl":"10.1016/j.eij.2026.100921","url":null,"abstract":"<div><div>Electric Vehicles (EVs) charging pricing plays an important role in reducing the charging demand during peak hours and increasing Charging Station Operator (CSO) profits. However, the existing studies have overlooked the charging pile availability and the current Charging Stations (CS) occupancy. This study proposes a novel on-demand dynamic pricing strategy considering limited charging spaces and CS occupancy using the low and high occupancy thresholds, with low and high cost adjustments in the charging costs. The idle occupancy at the CSs with a limited number of spaces can reduce the CSO profit; therefore, the idle time penalty is also introduced. The real-world EV charging data of 6 CSs from 3 districts of Jiaxing city, China, is used. The case study also includes analysis of occupancy thresholds, cost adjustments, idle time penalty limits, and penalty costs. The findings show that the proposed strategy, including both algorithms, improved CSO profits across most EV charging sites as compared to Time-of-Use (ToU) pricing. The profits are increased by 8.019% with algorithm 1 and 9.603% with algorithm 2 for the Bus Station location. The Government Agency site achieved a 4.284% and 6.109% increase, while the Shopping Mall also increased by 3.315% and 5.107%, respectively. The Tourist Attraction location also experienced profit rises of 0.657% and 2.710%. Expressway Service District C and Financial Industrial Park showed a slight decrease of <span><math><mo>−</mo></math></span>0.237% and <span><math><mo>−</mo></math></span>0.299% with Algorithm 1, and improved by 1.824% and 1.442% using Algorithm 2, respectively. The results highlight that algorithm 2 consistently improves profit across all six CS locations.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100921"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396706","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}
Small object detection in aerial imagery is a challenging task due to the minimal pixel information in dense clutter, scale variation, and complex backgrounds. YOLOv9 has demonstrated the effectiveness of Programmable Gradient Information (PGI) in mitigating feature degradation. However, its fully convolutional architecture lacks the capability for global context modeling, which is critical for resolving ambiguities in small targets. To address these limitations, we propose SF-YOLOv9, a hybrid architecture that enhances YOLOv9c by improving the backbone through the integration of a novel PGI-Aware Swin Fusion Block (Transformer-GELAN) at its final stage. This module effectively preserves high-resolution local features while injecting long-range global context through Swin Transformer-based fusion. It results in richer and more discriminative semantic representations. We introduce a Dual-Path Spatial and Channel Attention Module (DSCAM) into the main detection head and the reversible auxiliary branches of PGI. By refining attention across all supervisory signals, DSCAM significantly improves gradient flow and feature fidelity during PGI training, reducing missed detections and false positives. We evaluate SF-YOLOv9 on VisDrone and NWPU-VHR-10 datasets to demonstrate the effectiveness of SF-YOLOv9. It outperformed the baseline models, achieving 49.1% [email protected] on VisDrone and 98.3% [email protected] on NWPU VHR-10 in small-object detection.
{"title":"SF-YOLOv9: PGI based hybrid backbone with dual-path attention for small object detection in aerial imagery","authors":"Shahzad Hussain , Iqra Mumtaz , Chong Wang , Pei Lv","doi":"10.1016/j.eij.2026.100888","DOIUrl":"10.1016/j.eij.2026.100888","url":null,"abstract":"<div><div>Small object detection in aerial imagery is a challenging task due to the minimal pixel information in dense clutter, scale variation, and complex backgrounds. YOLOv9 has demonstrated the effectiveness of Programmable Gradient Information (PGI) in mitigating feature degradation. However, its fully convolutional architecture lacks the capability for global context modeling, which is critical for resolving ambiguities in small targets. To address these limitations, we propose SF-YOLOv9, a hybrid architecture that enhances YOLOv9c by improving the backbone through the integration of a novel PGI-Aware Swin Fusion Block (Transformer-GELAN) at its final stage. This module effectively preserves high-resolution local features while injecting long-range global context through Swin Transformer-based fusion. It results in richer and more discriminative semantic representations. We introduce a Dual-Path Spatial and Channel Attention Module (DSCAM) into the main detection head and the reversible auxiliary branches of PGI. By refining attention across all supervisory signals, DSCAM significantly improves gradient flow and feature fidelity during PGI training, reducing missed detections and false positives. We evaluate SF-YOLOv9 on VisDrone and NWPU-VHR-10 datasets to demonstrate the effectiveness of SF-YOLOv9. It outperformed the baseline models, achieving 49.1% [email protected] on VisDrone and 98.3% [email protected] on NWPU VHR-10 in small-object detection.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100888"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037845","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 : 2026-03-01Epub Date: 2026-01-05DOI: 10.1016/j.eij.2025.100883
Utpal Ghosh , Uttam kr. Mondal , Abdelmoty M. Ahmed , Ahmed A. Elngar
The deployment of effective data transmission with minimal resources, minimum architecture, low power consumption, and improved security makes this proposed lightweight wireless acoustic sensor network (WASNs) an appealing solution. This paper addresses the challenges of secure and energy-efficient audio broadcasting in WASNs. To transfer the entire gathered signal from source to recipient, a common setup for this application would be to send it over multi-hop communication to a distant server. On the other hand, persistent data streaming may induce an abrupt reduction in sensor energy, which may shorten the network lifetime and raise concerns about the application’s feasibility. This suggested method is supplemented during the design phase with several methods or processes for reducing the overhead of architectural design, specifically regarding network resource consumption and development effort. This method aims to reduce the amount of energy used by the acoustic origin sensor and free up network bandwidth by carrying less unnecessary data. The proposed method guarantees secure transfer through an enhanced Elliptic Curve Cryptography (ECC). The method introduces a session key mechanism and a chaos-based private key generation approach to enhance resilience against cryptographic attacks. A novel feature extraction strategy utilizing a variety of extraction characteristics and classifications is suggested in this study. Based on experimental results, the suggested method saves 74.35% of energy and obtains 89% of feature extraction accuracy when compared to streaming the complete acoustic data to a distant server. The proposed method achieves superior security against known attacks while reducing computational overhead by over 97%.
{"title":"Designing lightweight secure and energy-efficient wireless acoustic sensor networks for optimized data transmission and processing","authors":"Utpal Ghosh , Uttam kr. Mondal , Abdelmoty M. Ahmed , Ahmed A. Elngar","doi":"10.1016/j.eij.2025.100883","DOIUrl":"10.1016/j.eij.2025.100883","url":null,"abstract":"<div><div>The deployment of effective data transmission with minimal resources, minimum architecture, low power consumption, and improved security makes this proposed lightweight wireless acoustic sensor network (WASNs) an appealing solution. This paper addresses the challenges of secure and energy-efficient audio broadcasting in WASNs. To transfer the entire gathered signal from source to recipient, a common setup for this application would be to send it over multi-hop communication to a distant server. On the other hand, persistent data streaming may induce an abrupt reduction in sensor energy, which may shorten the network lifetime and raise concerns about the application’s feasibility. This suggested method is supplemented during the design phase with several methods or processes for reducing the overhead of architectural design, specifically regarding network resource consumption and development effort. This method aims to reduce the amount of energy used by the acoustic origin sensor and free up network bandwidth by carrying less unnecessary data. The proposed method guarantees secure transfer through an enhanced Elliptic Curve Cryptography (ECC). The method introduces a session key mechanism and a chaos-based private key generation approach to enhance resilience against cryptographic attacks. A novel feature extraction strategy utilizing a variety of extraction characteristics and classifications is suggested in this study. Based on experimental results, the suggested method saves 74.35% of energy and obtains 89% of feature extraction accuracy when compared to streaming the complete acoustic data to a distant server. The proposed method achieves superior security against known attacks while reducing computational overhead by over 97%.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100883"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927212","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 : 2026-03-01Epub Date: 2025-12-11DOI: 10.1016/j.eij.2025.100850
Wei Zheng , Lili Huang , Haiqiang Liu , Fa Zhu , Achyut Shankar , Imad Rida , Davide Moroni
The angle-based outlier detection (ABOD) is proposed to tackle the “curse of dimensionality” that exists in distance-related or density-related outlier detectors. However, ABOD may fail on multimodal datasets since it only considers global information. Furthermore, ABOD needs to calculate the angles between difference vectors from an instance to each pair of instances in the dataset except itself. Its time complexity reaches O (n3). In order to address these two issues, this paper proposes localized angle-based outlier detection (LABOD) which first finds the influence set, and then calculates the variance of angles between the difference vector from an instance to the mean of its neighbors in the influence set and the difference vectors from the instance to its neighbors in the influence set. The influence set consists of the nearest neighbor set and the reverse nearest neighbor set. Because the variance is defined by the angles in a local region, the proposed method can overcome the drawbacks of ABOD. The experiments performed on both synthetic and benchmark datasets demonstrate that LABOD is superior to ABOD.
{"title":"Localized angle-based unsupervised outlier detection","authors":"Wei Zheng , Lili Huang , Haiqiang Liu , Fa Zhu , Achyut Shankar , Imad Rida , Davide Moroni","doi":"10.1016/j.eij.2025.100850","DOIUrl":"10.1016/j.eij.2025.100850","url":null,"abstract":"<div><div>The angle-based outlier detection (ABOD) is proposed to tackle the “curse of dimensionality” that exists in distance-related or density-related outlier detectors. However, ABOD may fail on multimodal datasets since it only considers global information. Furthermore, ABOD needs to calculate the angles between difference vectors from an instance to each pair of instances in the dataset except itself. Its time complexity reaches <em>O</em> (<em>n<sup>3</sup></em>). In order to address these two issues, this paper proposes localized angle-based outlier detection (LABOD) which first finds the influence set, and then calculates the variance of angles between the difference vector from an instance to the mean of its neighbors in the influence set and the difference vectors from the instance to its neighbors in the influence set. The influence set consists of the nearest neighbor set and the reverse nearest neighbor set. Because the variance is defined by the angles in a local region, the proposed method can overcome the drawbacks of ABOD. The experiments performed on both synthetic and benchmark datasets demonstrate that LABOD is superior to ABOD.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100850"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719068","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 : 2026-03-01Epub Date: 2026-01-01DOI: 10.1016/j.eij.2025.100880
Freedom M. Khubisa, Oludayo O. Olugbara
Deep learning has gained significant importance in manifold disciplines such as natural language processing, supply chain optimization, computer vision, financial analysis, mechatronics and robotics, cybersecurity, and healthcare. It offers alternative methods to proactively manage plant diseases to ensure healthy crop yields, minimize economic losses, contribute to global food security, and promote sustainable agricultural practices. Nevertheless, despite a huge volume of publications on plant disease management using deep learning, a gap exists in the methodical evaluation of the contributions, impacts, trends, and exploration of intellectual structures of the publication elements using bibliometric analysis. Therefore, a bibliometric analysis was performed on 4,317 publications indexed in the Scopus database from 2016 to 2025 regarding plant disease management utilizing deep learning methods. Bibliometric performance analysis was based on publication, citation, and citation-and-publication metrics. Science mapping was conducted based on citation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis using Biblioshiny and VOSviewer tools. The bibliometric analysis confirmed that Computers and Electronics in Agriculture and IEEE Access are the most impactful publication sources according to the metrics of h-index and citations. A publication written by Mohanty SP in 2016 was found to be the most globally cited. Five distinctive clusters were identified using bibliographic coupling of publications and co-word analysis of author keywords to provide useful insights into the knowledge structure of plant disease management using deep learning. The analysis findings can provide valuable insights into the broader impact of the extant literature on deep learning applications, offering a footing for progressing artificial intelligence applications in plant disease management and guiding future research directions.
{"title":"Bibliometric analysis of deep learning in plant disease management","authors":"Freedom M. Khubisa, Oludayo O. Olugbara","doi":"10.1016/j.eij.2025.100880","DOIUrl":"10.1016/j.eij.2025.100880","url":null,"abstract":"<div><div>Deep learning has gained significant importance in manifold disciplines such as natural language processing, supply chain optimization, computer vision, financial analysis, mechatronics and robotics, cybersecurity, and healthcare. It offers alternative methods to proactively manage plant diseases to ensure healthy crop yields, minimize economic losses, contribute to global food security, and promote sustainable agricultural practices. Nevertheless, despite a huge volume of publications on plant disease management using deep learning, a gap exists in the methodical evaluation of the contributions, impacts, trends, and exploration of intellectual structures of the publication elements using bibliometric analysis. Therefore, a bibliometric analysis was performed on 4,317 publications indexed in the Scopus database from 2016 to 2025 regarding plant disease management utilizing deep learning methods. Bibliometric performance analysis was based on publication, citation, and citation-and-publication metrics. Science mapping was conducted based on citation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis using Biblioshiny and VOSviewer tools. The bibliometric analysis confirmed that Computers and Electronics in Agriculture and IEEE Access are the most impactful publication sources according to the metrics of h-index and citations. A publication written by Mohanty SP in 2016 was found to be the most globally cited. Five distinctive clusters were identified using bibliographic coupling of publications and co-word analysis of author keywords to provide useful insights into the knowledge structure of plant disease management using deep learning. The analysis findings can provide valuable insights into the broader impact of the extant literature on deep learning applications, offering a footing for progressing artificial intelligence applications in plant disease management and guiding future research directions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100880"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884766","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}
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