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2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society, CAISAIS 2023

作者： Amran, Gehad Abdullah Wang, Shuang Al-Qaness, Mohammed A. A. Al-Bakhrani, Ali A. Mutmbak, Khaled Hussain Ghawy, Mohammed Zaid Dalian University of Technology Department of Management Science and Engineering Dalian116024 China College of Software Engineering Northeastern University Shenyang110169 China Emirates International University Sana'a16881 Yemen College of Software Dalian University of Technology Dalian116024 China College of Computing and Information Technology University of Tabuk Tabuk Saudi Arabia College of Cpmputer Science Thamar University Dhamar Yemen

ISBN: (纸本)9798350314786

In recent years, the academic and industrial science community has shown great interest in the Internet of Things and the decentralization of Blockchain networks, particularly in smart cities and emerging communities. Unmanned Aerial Vehicles (UAVs) have gained significant attention in military and commercial systems, serving as emergency transport during natural disasters and epidemics such as the Coronavirus pandemic. In such scenarios, the use of Ad-Hoc wireless networks to cover large areas can be more secure if implemented as a shared database, such as Blockchain. This paper suggests the Unmanned Aerial Vehicles as a solution for the location and sterilization of coronavirus contaminated areas. Thus, one of these aerial vehicles will locate the infected area and update its spatial data block. Also, the surrounded aerial vehicles comply with this modification and change their location information in the Blockchain to pursue the target location, and together start to spray sterilizer. To improve localization accuracy, a swarm intelligence algorithm is employed, while Blockchain technology serves as a decentralized database for clusterization of other UAVs. Through realistic studies and analyses of localization and clusterization, the proposed method proves effective in locating and sterilizing polluted air regions. Additionally, this database technique provides a higher level of security, making it challenging to attack. Ultimately, this research contributes to the advancement of emergency transport networks, Blockchain technology, and IoT services. © 2023 IEEE.

关键词： Blockchain

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Real-Time Plant Disease Detection Using YOLOv5 and Autonomous Robotic Platforms for Scalable Crop Monitoring

Real-Time Plant Disease Detection Using YOLOv5 and Autonomou...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： M. Hassan Tanveer Zainab Fatima Muzammil Ali Khan Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta USA Department of Software Engineering NED University of Engineering and Technology Karachi Pakistan Department of AI and Data Science FAST University of computing and Emerging Science Karachi Pakistan

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

Plant diseases pose a significant threat to global food security, with annual losses amounting to billions of dollars. Early detection and intervention are critical for mitigating these losses. Traditional methods of disease detection, such as visual inspection, are time-consuming, labor-intensive, and often inefficient. This paper presents an advanced approach using the YOLOv5 (You Only Look Once) model, a state-of-the-art object detection algorithm, to identify and classify plant diseases in real-time. The YOLOv5 model offers high accuracy and efficiency, particularly for tasks requiring simultaneous detection of multiple objects, making it ideal for agricultural applications. This paper explores using novel data collection methods, such as mounting cameras on Husky UGV (Unmanned Ground Vehicle), to capture diverse images of crops from various angles in the field. These autonomous systems enhance dataset variety, contributing to more accurate detection results. Our methodology focuses on training the YOLOv5 model using annotated datasets, which enables the model to identify diseases such as apple rust leaf, bell pepper leaf spot, and others. The experimental results demonstrate a detection accuracy of 92 %, showcasing the potential of YOLOv5 for realtime crop disease monitoring. Integrating robotics for field analysis is further discussed as a future direction, providing a scalable solution for continuous crop monitoring and disease management.

关键词： YOLO Plant diseases Visualization Accuracy Autonomous systems Crops Food security Real-time systems Robots Monitoring

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SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Nagib, Ahmad M. Abou-Zeid, Hatem Hassanein, Hossam S. School of Computing Queen’s University Canada Department of Electrical and Software Engineering University of Calgary Canada Faculty of Computers and Artificial Intelligence Cairo University Egypt

Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access networks (O-RANs) due to simulation-to-reality gaps. These policies often lack safety guarantees to ensure compliance with service level agreements (SLAs), such as the strict latency requirements of immersive applications. As a result, a deployed DRL slicing agent may make resource allocation (RA) decisions that degrade system performance, particularly in previously unseen scenarios. Real-world immersive applications require maintaining SLA constraints throughout deployment to prevent risky DRL exploration. In this paper, we propose SafeSlice to address both the cumulative (trajectory-wise) and instantaneous (state-wise) latency constraints of O-RAN slices. We incorporate the cumulative constraints by designing a sigmoid-based risk-sensitive reward function that reflects the slices’ latency requirements. Moreover, we build a supervised learning cost model as part of a safety layer that projects the slicing agent’s RA actions to the nearest safe actions, fulfilling instantaneous constraints. We conduct an exhaustive experiment that supports multiple services, including real virtual reality (VR) gaming traffic, to investigate the performance of SafeSlice under extreme and changing deployment conditions. SafeSlice achieves reductions of up to 83.23% in average cumulative latency, 93.24% in instantaneous latency violations, and 22.13% in resource consumption compared to the baselines. The results also indicate SafeSlice’s robustness to changing the threshold configurations of latency constraints, a vital deployment scenario that will be realized by the O-RAN paradigm to empower mobile network operators (MNOs). Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model

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IEEE Transactions on Services computing 2025年第3期18卷 1445-1458页

作者： Yinchen Pan Jun Zeng Ziwei Wang Haoran Tang Junhao Wen Min Gao School of Big Data and software engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.

关键词： Trajectory Diffusion models Spatiotemporal phenomena Feature extraction Optimization Transformers Data mining Noise measurement Data models Correlation

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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Potholes Detection Using Yolo Algorithms and Their Integration Into Gazebo Simulation

Potholes Detection Using Yolo Algorithms and Their Integrati...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： M. Hassan Tanveer Zainab Fatima Tanazzah Rehman Moaaz Siddique Arbaz Shah Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta USA Department of Software Engineering NED University of Engineering and Technology Karachi Pakistan Department of AI and Data Science FAST University of computing and Emerging Science Karachi Pakistan

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. The researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. This will help us see the most useful ones for finding potholes when managing and maintaining roads.

关键词： YOLO Location awareness Roads Transportation Maintenance engineering Prediction algorithms Classification algorithms Object recognition Jamming Videos

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Exploring Aspects of Agile software Development Risk – Results from a MLR 1

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29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Nolan, Aaron Strickland, Ben Quinn, Adam Gallagher, Kyle Yilmaz, Murat Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey Lero the Science Foundation Ireland Research Center for Software Lero Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Agile software development methods are widely used by software organisations, focusing on short developmental life cycles and customer satisfaction through the iterative and incremental development of software products. Despite their popularity, these methods present risks that may be underappreciated. This paper examines certain risks attributed to agile software development, with a focus on the lack of documentation, scope creep, technical debt and job satisfaction. Through the application of a multivocal literature review, we find that agile software development can greatly benefit projects. However, when agile methods are implemented inappropriately or sub-optimally, projects risk over-spending, delayed or defective software, employee turnover, and overall decreased productivity. Understanding the risks associated with agile software development can help practitioners to achieve higher efficiency and success in their software development projects. © 2022, Springer Nature Switzerland AG.

关键词： Job satisfaction

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An optimal container update method for edge-cloud collaboration

An optimal container update method for edge-cloud collaborat...

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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Preserving Privacy in Association Rule Mining Using Multi-Threshold Particle Swarm Optimization

SSRN

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SSRN 2024年

作者： Aljehani, Shahad Alotaibi, Youseef Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia

The utilization of healthcare data to uncover hidden correlations holds substantial promise for advancing medical research. Association Rule Mining (ARM) has proven invaluable in identifying relationships between diseases, medications, and symptoms. However, the amalgamation of data from multiple healthcare systems, while enhancing accuracy, raises concerns about patient privacy. In response, Preserving Privacy Association Rule Mining (PPARM) has garnered significant attention, with existing approaches relying on metaheuristic-based algorithms and a single predefined Minimum Support Threshold (MST). The impracticality of using a uniform MST for diverse pattern lengths necessitated a novel solution. This study introduces the Multi-threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model, integrating the Apriori algorithm and utilizing a dynamic MST setting through the multi-threshold Particle Swarm Optimization (PSO) algorithm. This innovative approach facilitates the adaptive selection of an MST based on rule length, effectively concealing sensitive information with varying patterns. Evaluation on diverse datasets, including Chess, Mushroom, Retail, and Heart Disease, demonstrated the superiority of the MPSO4ARM method over traditional Apriori and conventional PSO algorithms. The results indicate improved fitness values and robust protection of sensitive rules, showcasing its efficacy in minimizing adverse effects across different datasets and MST values. © 2024, The Authors. All rights reserved.

关键词： Association rules

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Adapting CRISP-DM to model enteric fermentation emission: farm level application

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International Journal of Applied Decision Sciences 2025年第3期18卷 330-359页

作者： Guerrón, Philippe Belmont Hallo, María Luján-Mora, Sergio Faculty of Engineering Systems Escuela Politecnica Nacional Ladron de Guevara E11-253 Edif 20 Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante03690 Spain

Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH4 head−1 year−1 compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Farms

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