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Discovery of association rules between factors affecting user satisfaction in software project by FP-GROWTH Algorithm 18

Discovery of association rules between factors affecting use...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Kaewbanjong, Katawut Intakosum, Sarun King Mongkut's Institute of Technology Ladkrabang Software System Engineering Laboratory Department of Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9780738111278

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP-growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction © 2021 IEEE.

关键词： Association rules

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EPCP: An Efficient Point Cloud Classification Network with Position Fusion 19

EPCP: An Efficient Point Cloud Classification Network with P...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yunming Wang, Jianming Ma, Haoran Sun, Yukuan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin China Tiangong University School of Software Tianjin China Tiangong University Center for Engineering Intership and Training Tianjin China

ISBN: (纸本)9798350346558

Recently, deep learning on 3D point cloud data has driven rapid development in the field of autonomous driving. However, processing 3D point clouds is a challenging task. Transformers are widely used in various fields of deep learning. In the community of 3D point clouds, Point Cloud Transformer(PCT, a transformer-based framework) also achieves excellent performance on 3D point cloud tasks. However, the well-known attention mechanism benefits performance improvement while it also leads to expensive computational costs. To alleviate the problem, we propose a novel structure named efficient Point Cloud Classification Network with Position Fusion(EPCP), which uses the strategy of fusing absolute coordinates-based features and relative position information instead of the attention mechanism. By fusing relative position information, our method ensures that the output features maintain different sensitivity to specific areas, which plays a similar role as the attention mechanism. To verify the effectiveness of our method, we conduct experiments on ModelNet40 and real-world ScanObjectNN datasets. Compared with PCT, our method maintains the same accuracy on ModelNet40 and improves by 5.5% on ScanObjectNN. At the same time, our method reduces the number of parameters by 91% and FLOPs by 50% compared to PCT on the ModelNet40 dataset. © 2022 IEEE.

关键词： Classification (of information)

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Bi-Causal: Group Activity Recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity Recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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General Dynamic Regularization Federated Learning with Hybrid Sharpness-Aware Minimization

General Dynamic Regularization Federated Learning with Hybri...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fengchun Zhang Dongfen Li Jinshan Lai Yang Zhang Fengli Zhang Ruijin Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Network Security Chengdu University of Technology Chengdu China Science Research Centre The Second Research Institute of CAAC Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

One of the main challenges in federated learning is its non-independent and identically distributed (non-IID) nature, where independent client training leads to overfitting and model deviations, negatively impacting overall performance. To address this, most research focuses on aligning local and global models to reduce client drift. However, existing algorithms use Empirical Risk Minimization (ERM) as the local optimizer, leading the global model to sharp minima, increasing bias in some clients and lowering generalization. To address these challenges, we propose GFed-HSAM, a general federated learning method that improves both local and global model generalization. GFed-HSAM uses Hybrid Sharpness-Aware Minimization (HSAM) as the local optimizer to smooth gradients with zeroth-order and first-order sharpness measures. It also includes a dynamic regularizer (DR) to align global and local models at the parameter level. Experiments show that GFed-HSAM outperforms state-of-theart methods in accuracy and generalization across different data heterogeneity settings on CIFAR10/CIFAR100.

关键词： Training Accuracy Risk minimization Federated learning Heuristic algorithms Perturbation methods Signal processing algorithms Signal processing Minimization Speech processing

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Secure and Efficient AI-SDN-based Routing for Healthcare-Consumer Internet of Things

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IEEE Transactions on Consumer Electronics 2024年

作者： Zabeehullah Khan, Nauman Ali Din, Ikram Ud Almogren, Ahmad Altameem, Ayman Guizani, Mohsen Department of Computer Software Engineering Islamabad Pakistan The University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Science Riyadh11633 Saudi Arabia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The advancement of communication technologies and cloud systems has led to the emergence of the Healthcare-Consumer Internet of Things (H-CIoT) as a significant domain. This emergence has transformed the traditional healthcare system into the next generation of H-CIoT, characterized by higher connectivity and intelligence. software-Defined Networking (SDN) is currently being incorporated into H-CIoT, enabling it to meet the complex, dynamic, and heterogeneous requirements of H-CIoT networks. As the H-CIoT network expands, there is an increasing demand for secure, efficient, and optimal routing to ensure low latency and high throughput. In this paper, we propose an Artificial Intelligence (AI)-based approach that combines the strengths of Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) to accurately detect anomalies in H-CIoT imbalance data and achieve optimum routing. The DRL model dynamically formulates the optimal routing policies through efficient adaptation to underlying network traffic patterns. It also comprehends the characteristics of imbalance data to enhance its routing decisions. Simulation-based results validate the effectiveness and superiority of our proposed model over OSPF routing optimization technique in term of throughput (12%), latency (20%), and the Probability of avoiding malicious minor class attacks (30%), confirming it as an outstanding suitability for the next-generation H-CIoT network. © 2024 IEEE.

关键词： Cloud platforms

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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Drowsiness Detection Using CNN Architecture

Drowsiness Detection Using CNN Architecture

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International Conference on Futuristic Technologies (INCOFT)

作者： N.T. Renukadevi K. Kishore K. Saraswathi T. Roshan Nithish Department of Computer Technology-UG Kongu Engineering college Erode India B.Sc. (Software Systems) Kongu Engineering College Erode India B.Sc.(Computer Systems and Design) Kongu Engineering College Erode India

Nowadays, road accidents occur owing to the distractions of people who drives the vehicles by Internal or External factors. Using of Mobile phones and driving continuously for several hours are some of the reasons of driver distraction. Among these, the main distractions faced by drivers are drowsiness. Specifically, a driver turns to sleep while driving causes an accident. Latest studies reveal that approximately 20% of vehicle hits have caused by drowsy drivers. And now, these types of accidents can be detected by using modern software technologies. In that, Deep learning is a category of Artificial Intelligence (AI) concepts, which imitate the way humans, gain certain types of knowledge. Deep Learning plays a vital role in health care industry also. The main characteristic of deep learning is, it can built a model automatically by self-learning. In deep learning, Convolutional Neural Network (CNN) is a type of deep neural networks, which is applied to investigate visual imagery. In this research work, a CNN model will be built to identify the drowsiness of the driver by observing the driver's face reactions. The driver drowsiness dataset is downloaded from data-flair website and trained with CNN architecture. In this work, an image of driver's face will be captured through web camera and compared with trained images .If matches occur, an alarm will be given to make awareness to the driver and to avoid accidents.

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AdvUSL: Targeted Adversarial Attack against U-shaped Split Learning

AdvUSL: Targeted Adversarial Attack against U-shaped Split L...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yunxiao He Chunqiang Hu Yuwen Pu Jiahao Chen Xingwang Li School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Zhejiang China College of Physics and Electronic Information Engineering Henan Polytechnic University Henan China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Split Learning is an emerging Distributed Machine Learning which allows clients and the server to collaborative train a model by splitting it successively. This approach not only addresses data scarcity, but also enhances data privacy while reducing communication and computation overhead. However, existing research on the security of Split Learning primarily focuses on privacy protection, neglecting model security. To fill this gap, we propose AdvUSL, a targeted adversarial attack method against U-shaped Split Learning. AdvUSL trains a surrogate model inexpensively with a small amount of labeled data; matches intermediate embedding and labels to infer clients' labels, and uses the matched embeddings as anchors to conduct targeted adversarial attacks. Comprehensive experiments demonstrate that AdvUSL outperforms traditional white-box adversarial attacks, validating the effectiveness of our method.

关键词： Training Privacy Computational modeling Machine learning computer architecture Smart systems Security Servers Protection Glass box

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TMAE: Entropy-Aware Masked Autoencoder for Low-Cost Traffic Flow Map Inference

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IEEE Internet of Things Journal 2025年

作者： Luo, Xucheng Wu, Qiong Wang, Ye Zhang, Kuan Dai, Hong-Ning Chen, Dajiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Pengcheng laboratory Shenzhen China University of Nebraska-Lincoln United States Hong Kong Baptist University Department of Computer Science Hong Kong

Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few deployed sensors to infer traffic flow across entire urban areas, incorporating prior knowledge from road distribution maps. Specifically, TMAE employs a shared encoder to process traffic flow context, using self-attention scores to identify the importance of each region and guide a masking policy that retains regions rich in traffic flow information. The road distribution map, reflecting inherent traffic flow patterns, is incorporated as prior knowledge by substituting masked tokens during training. A cross-attention mechanism in the decoder further refines inference, where embeddings from the road distribution map serve as queries, and retained visible patches act as keys and values. Additionally, regional traffic entropy is introduced to quantify the information richness of each region, enabling the selection of minimal measurement regions to optimize inference for other areas. Extensive experiments conducted on datasets from various cities demonstrate the effectiveness and efficiency of TMAE, highlighting its potential as a scalable solution for low-cost traffic flow inference in urban environments. The source code of this work is released at https://***/TextGraph/TMAE. © 2025 IEEE.

关键词： Costs

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Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

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computers, Materials & Continua 2022年第12期73卷 5349-5365页

作者： N.Penchalaiah Abeer S.Al-Humaimeedy Mashael Maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani Department of CSE AITSRajampet516126Andhra PradeshIndia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityP.O.Box 103786Riyadh11616Saudi Arabia Software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and SciencesRajampet516126Andhra PradeshIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad10072Iraq Applied College in Abqaiq King Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and *** paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud *** Paper suggest designing and implementing a low cost,low power cluster of Single Board computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and *** Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and *** obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is *** Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in *** evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

关键词： Big data edge cloud cluster architecture performance engineering Raspberry pi dockers warm container technology data streaming

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