Egyptian Informatics Journal最新文献

Advanced effort estimation in software projects using ensemble-based fusion techniques 基于集成的融合技术在软件项目中的高级工作量估计

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.eij.2026.100914

Ritu , Pankaj Bhambri , Mudassir Khan , Barga Mohammed Mujahid

{"title":"Advanced effort estimation in software projects using ensemble-based fusion techniques","authors":"Ritu , Pankaj Bhambri , Mudassir Khan , Barga Mohammed Mujahid","doi":"10.1016/j.eij.2026.100914","DOIUrl":"10.1016/j.eij.2026.100914","url":null,"abstract":"<div><div>The software development process heavily relies on effort estimation, which is crucial for determining the development approach and methodology. This research aims to propose a methodology to enhance effort estimation, ensuring that initial work estimation remains precise and reliable. In such cases, a fusion approach using an ensemble of Machine Learning (ML) techniques is proposed. The proposed methodology uses linear regression and random forest ML algorithms. The results are cross-checked against well-known datasets to demonstrate their estimation accuracy. A ML method is used to forecast the project’s effort, as some parameters required for work estimation may not be determinable at the beginning of the process. The model is trained using parametric training with fixed-size parameters, with the use case repository data serving as the training set and the China dataset as the test set. The proposed work uses an ensemble model to compute the fusion approach’s result, which is then inputted into a linear regression model. All regression models are then run individually to calculate the effort, and the efficiency of the proposed model is 94% which is a great improvement as compared to the individual model’s performance which is 74% only.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100914"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396547","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}

引用次数: 0

Catalyzing big data excellence: the enhanced Data Placement Policy (EDPP) revolution 催化大数据卓越：增强型数据放置政策（EDPP）革命

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.eij.2025.100878

Jeffin Gracewell , P. Ramya , S. Venkatesh Babu

{"title":"Catalyzing big data excellence: the enhanced Data Placement Policy (EDPP) revolution","authors":"Jeffin Gracewell , P. Ramya , S. Venkatesh Babu","doi":"10.1016/j.eij.2025.100878","DOIUrl":"10.1016/j.eij.2025.100878","url":null,"abstract":"<div><div>The Enhanced Data Placement Policy (EDPP) offers a novel approach to optimising data storage in Hadoop-based Big Data systems that utilise the Hadoop Distributed File System (HDFS). Efficient data placement is essential for maximising data retrieval speed and overall system performance. EDPP leverages the combined power of MapReduce (MR) and Particle Swarm Optimisation (PSO) to address this challenge effectively. In these systems, files are divided into chunks, which can be either duplicates (pointing to existing data copies) or unique (requiring strategic placement). EDPP uses MR to identify the most suitable Data Nodes (DNs) for storing unique chunks, enhancing data retrieval efficiency. Furthermore, EDPP addresses the issue of maintaining balanced data distribution in heterogeneous clusters. By utilising PSO, it intelligently selects DNs based on their storage capacity and response times, ensuring optimal resource utilisation and load balancing. This research presents a practical and efficient solution to the complex problem of data placement in Big Data systems, offering significant benefits to organisations with diverse hardware and software configurations in their Hadoop clusters. Finally, experimental results demonstrate that the Enhanced Data Placement Policy (EDPP) significantly improves performance in Hadoop-based Big Data systems. The EDPP reduces data retrieval time by an average of 33 % compared to traditional methods and achieves a throughput of 35.03 MB/s with a 98 GB dataset, more than doubling the performance of the existing methods. Additionally, it ensures even data distribution, preventing node overload and optimising resource utilization.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100878"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841261","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}

引用次数: 0

Automatic action recognition technology combining pyramid convolution and attention mechanism in gymnastics training 结合金字塔卷积和注意机制的体操训练动作自动识别技术

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.eij.2025.100881

Li Shang

{"title":"Automatic action recognition technology combining pyramid convolution and attention mechanism in gymnastics training","authors":"Li Shang","doi":"10.1016/j.eij.2025.100881","DOIUrl":"10.1016/j.eij.2025.100881","url":null,"abstract":"<div><div>This study proposes an action recognition method that combines pyramid convolution and attention mechanism to address the problem of low efficiency and inability to effectively capture the detailed features of movements in traditional gymnastics training. The aim is to achieve the level of intelligence in gymnastics training and improve the precision of action recognition. This method improves the efficiency and accuracy of finite element by constructing a multi-scale feature detection model based on a pyramid convolutional kernel dual stream neural network. To further enhance the performance of action analysis, an improved selective kernel attention mechanism is introduced, and a method for automatic motion analysis that incorporates attention mechanism alongside multi-level features is proposed. The outcomes indicate that in comparison with conventional dual stream neural networks, the proposed method improves the accuracy of spatial flow and event flow by 5.00 % and 3.12 %, respectively. In comparison with the original attention mechanism, the recall rate of the proposed method increases by 9.73 %, accuracy increases by 5.79 %, and the average accuracy of motion analysis for spatial and temporal streams increases by 1.99 % and 0.83 %. The outcomes reveal that the proposed action recognition method can efficiently extract key features and has excellent accuracy in recognizing gymnastics training actions. This study introduces an innovative technological approach in the realm of sports science, which has the potential to enhance the intelligence quotient of athlete training and maximize training efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100881"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977567","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}

引用次数: 0

LLM-based data augmentation for text classification on imbalanced datasets: A case study on fake news detection 基于llm的非平衡数据集文本分类的数据增强：假新闻检测的案例研究

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.eij.2026.100886

Ahmet Okan Arık , Gizem Parlayandemir , Serra Çelik

引用次数: 0

Channel-attentive YOLOv5 and capsule auto-encoder for pomegranate disease detection 通道关注型YOLOv5和胶囊型石榴病害检测编码器

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.eij.2025.100877

P. Sajitha , A. Diana Andrushia , N. Anand , Eva Lubloy

{"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}

引用次数: 0

Beyond 5G: PHWAN – A secure, low-latency, and cost-effective framework for Industry 4.0 smart manufacturing 超越5G: PHWAN——工业4.0智能制造的安全、低延迟和经济高效的框架

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 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

{"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}

引用次数: 0

Optimizing regenerative braking in light electric vehicles using deep deterministic policy gradient reinforcement learning 基于深度确定性策略梯度强化学习的轻型电动汽车再生制动优化

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.eij.2026.100893

Muhammad Rizalul Wahid , Endra Joelianto , Bentang Arief Budiman , Muhamad Praja Dewanata , Muhammad Aziz

{"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}

引用次数: 0

Fully automated Pell & Gregory classification on panoramic radiographs 全自动佩尔和格雷戈里分类全景x线照片

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1016/j.eij.2026.100917

Betül Uzbaş , Fatma Büşra Doğan , Mogham Njikam Mohamed Nourdine , Şule Yücelbaş , Cüneyt Yücelbaş , Zeynep Betül Arslan , Füsun Yaşar

{"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}

引用次数: 0

On-demand dynamic charging pricing strategy for Electric Vehicles 电动汽车按需动态充电定价策略

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI: 10.1016/j.eij.2026.100921

Adil Hussain , Qing-Chang Lu , Kashif Naseer Qureshi , Khalid Javeed

{"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}

引用次数: 0

SF-YOLOv9: PGI based hybrid backbone with dual-path attention for small object detection in aerial imagery SF-YOLOv9：基于PGI的双路径关注混合主干航拍图像小目标检测

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.eij.2026.100888

Shahzad Hussain , Iqra Mumtaz , Chong Wang , Pei Lv

{"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}

引用次数: 0