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7th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Alagappan, Annamalai Baptist Andrews, Leo John Sampath Kumar, V. Raj, Raymon Antony Sarathkumar, D. Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Botho University Faculty of Computing Department of Software Engineering Botswana Grant Thornton Plot 50370 Acument Park Fair Grounds Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu India

ISBN: (纸本)9781665489102

A dynamic platform, the Internet of Things uses data, which in turn invites cybersecurity risks. In a dynamic and complicated world, many organisations are putting more of their attention into understanding cyber threats and attempting to quantify their exposure. This paper highlights and goes into great detail on standard cyber risk assessment methods while focusing on the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach employing machine learning algorithms. Based on the statistical research, it can be determined that the CAQ model utilising J48 classifies at 94 percent, while the Bayesian method performs in evaluating possible risks at 82 percent. The Bayes algorithm also aids in estimating complicated risks coming from various sources, aids in comprehending how risk variables originate and connect in an IoT environment, and aids in determining what controls are necessary for quantifying and reducing them. © 2022 IEEE.

关键词： Cybersecurity

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Socially-Critical software Systems: Is Extended Regulation Required? 1

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

作者： Dagg, Niall Kostick, Conor Fallon, James O’Neill, Alex Yilmaz, Murat Messnarz, Richard Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey ISCN the International Software Consulting Network Graz Austria Lero the Science Foundation Ireland Research Center for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Data has become a prevailing aspect of our daily lives, becoming ever more present since the beginning of the 21st century. It is a commodity in today’s world and the amount of data being produced has increased enormously. One of the major ways data is produced and collected is from the use of websites and web-based applications. This data is later used for many different purposes. This paper presents findings from a multivocal literature review, exploring the methods of how this data is collected, what the data is used for once it has been collected, the ethics of data and its collection, and the future of data collection. Among the possible futures, we introduce the concept of socially-critical applications, where data harvesting in web-based applications might require pre-market disclosure and evaluation by notified bodies (instructed by regulation) as a means to break the existing cycle of technology companies outpacing under-resourced and ill-equipped regulators. Rather than regulators continually falling short of enacting laws to satisfy the common good, a new class of socially-critical application could be created in law to permit pre-market evaluation of applications (or versions of applications) that could undermine or interrupt the common good. © 2022, Springer Nature Switzerland AG.

关键词： Websites

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A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin 5th

A Feasibility Study of Fitting the Normal Distribution and G...

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5th International Conference on Water Resources, ICWR 2021

作者： Ghani, Nadiatul Adilah Ahmad Abdul Senawi, Azlyna Subramaniam, Roshan Department of Civil Engineering College Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Kuantan26300 Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

ISBN: (纸本)9789811959462

Rainfall data can be very useful for decision makers in developing precautionary strategies to mitigate the impact of climate change in a variety of ways, such as forecasting floods. Prior to the data being utilised, it is critical to determine the best fit probability distribution of the data. Only after this has been done then it is possible to obtain reliable analysis and results. In this study, the feasibility of fitting the data with normal distribution was investigated. The investigation specifically used peak monthly values of rainfall data from ten hydrological stations of the Kuantan River Basin during the year 1975 to 2017. Four different test statistics were employed to assess the possibility of fitting the data to the normal and Gamma distributions: Skewness, excess kurtosis, Kolmogorov–Smirnov and Shapiro–Wilk. Results obtained show that normal distribution fits well with the annual daily maximum rainfall data for only two hydrological stations, but normal distribution is not a good fit for the seasonal daily maximum rainfall case for all stations involved. Gamma 1P distribution was found to be not an ideal fit for both annual and seasonal data for all stations, but Gamma 2P, on the contrary, was discovered to fit well for all stations except one station. The findings of the study are anticipated to improve rainfall forecasting in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Deepfake Detection Using EfficientNet and XceptionNet 11

Deepfake Detection Using EfficientNet and XceptionNet

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11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Yasser, Basma Hani, Jumana El-Gayar, Salma Amgad, Omar Ahmed, Nourhan Ebied, Hala M. Amr, Habiba Salah, Mohamed Ain Shams University Software Engineering Faculty of Computer and Information Sciences Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves preprocessing the Celeb- DF dataset by extracting frames and isolating faces, training the models, and evaluating their performance using log loss and Area Under the Curve (AUC) metrics. The study shows that both models are effective in accurately classifying real and fake videos and highlights the importance of continuously updating deepfake detection algorithms in response to evolving deepfake generation techniques. © 2023 IEEE.

关键词： Fake detection

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Exploration and Sterilization Coronavirus in Air based on Unmanned Aerial Vehicles

Exploration and Sterilization Coronavirus in Air based on Un...

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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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