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A Novel Enhanced Random Forest for Medical Data Classification using Correlation Pearson and Best Number of Trees

Journal of Computing science and Engineering 2024年第1期18卷 57-68页

作者： Tarchoune, Ilhem Djebbar, Akila Merouani, Hayet Farida Rania, Harfi Department of Computer Science LRI Laboratory SRF equip Badji Mokhtar University Annaba Algeria

Random forests (RF) is a successful ensemble prediction technique that uses majority voting or a combination-based average. However, each tree in an RF may have a different contribution to the treatment of a certain instance. The objectives of this study were to produce accurate decision trees and to determine the best trees between them with an optimal combination search. In this paper, we proposed three solutions for the prediction of medical data: the first solution optimizes a random forest model using a similarity measure, the second optimizes the RF using feature selection, and finally a simultaneous selection approach to similarity measures based on RFs. We demonstrated that the prediction performance and classification rate of the RF implementation on eleven databases can be further improved by the learning methods applied. Our experiments also showed that the improvement gives better results than the classical method;the results showed that the optimized RF model avoids some limitations of the original RF model. The results obtained in our proposed models are satisfactory and encouraging with an average accuracy of 95% for standard RF, 100% for RF_- Similarity, 93% for RF_FS, and 100% for RF_FS_Similarity. © 2024. The Korean Institute of Information Scientists and Engineers

关键词： Decision trees

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Advancing automated social engineering detection with oversampling-based machine learning

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International Journal of Security and Networks 2024年第3期19卷 150-158页

作者： Remmide, Mohamed Abdelkarim Boumahdi, Fatima Boustia, Narhimene LRDSI Laboratory Department of Computer Science Faculty of Sciences University of Blida 1 Blida Algeria

Social engineering attacks have surged with the increased reliance on online interactions. However, detecting these subtle deceptions remains challenging. This study proposes a novel machine learning approach to enhance social engineering attack detection. We analyse well-known models (support vector machines and XGBoost) in imbalanced datasets and employ oversampling techniques (SMOTE-ENN) to address class imbalance issues. Experimental results demonstrate that the oversampled SVM model outperforms other techniques, achieving over 99% accuracy in attack detection. Statistical analysis via ANOVA confirms the significant improvement in detection performance compared to previous methods. This research contributes to the development of automated and reliable systems for identifying social engineering attacks, enhancing the security of online communications. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Light field salient object detection:A review and benchmark

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Computational Visual Media 2022年第4期8卷 509-534页

作者： Keren Fu Yao Jiang Ge-Peng Ji Tao Zhou Qijun Zhao Deng-Ping Fan College of Computer Science Sichuan Universityand National Key Laboratory of Fundamental Science on Synthetic VisionSichuan UniversityChengdu 610065China School of Computer Science Wuhan UniversityWuhan 430072China PCA Lab School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Computer Vision Lab ETH ZürichZürichSwitzerland

Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging *** paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency ***,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief *** datasets for light field SOD are also ***,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD *** to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and *** supplemental data make a universal benchmark ***,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection *** provide nine observations on challenges and future directions,and outline several open *** the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://***/kerenfu/LFSOD-Survey.

关键词： light field salient object detection(SOD) deep learning benchmarking

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Full-duplex strategy for video object segmentation

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Computational Visual Media 2023年第1期9卷 155-175页

作者： Ge-Peng Ji Deng-Ping Fan Keren Fu Zhe Wu Jianbing Shen Ling Shao School of Computer Science Wuhan UniversityWuhanChina Computer Vision Lab ETH ZurichETF C113.2Sternwartstrasse 78092 ZurichSwitzerland College of Computer Science Sichuan UniversityChengduChina Peng Cheng Laboratory ShenzhenChina School of Computer Science Beijing Institute of TechnologyBeijingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two *** this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding ***,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding *** improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the *** experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object *** project is publicly available at https://***/GewelsJI/FSNet.

关键词： video object segmentation(VOS) video salient object detection(V-SOD) visual attention

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Synthetic data generation by diffusion models

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National science Review 2024年第8期11卷 19-21页

作者： Jun Zhu Department of Computer Science and Technology BNRist Center Tsinghua-Bosch Joint ML CenterTsinghua University Pazhou Laboratory (Huangpu)

Sufficient and high-quality data are a prerequisite for building complex machine learning systems, especially those with a large number of parameters (e.g., ChatGPT). However, it is typically challenging or even impossible to acquire a sufficient amount of real data to train such systems. For example, auto-driving systems may need to learn from various accidental events in order to be reliable in driving, while collecting such real data is difficult or ethically infeasible.

关键词： generation diffusion models data synthetic

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Targeted materials discovery using Bayesian algorithm execution

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npj Computational Materials 2024年第1期10卷 1619-1630页

作者： Sathya R.Chitturi Akash Ramdas Yue Wu Brian Rohr Stefano Ermon Jennifer Dionne Felipe Hda Jornada Mike Dunne Christopher Tassone Willie Neiswanger Daniel Ratner SLAC National Accelerator Laboratory Menlo ParkCA94025USA Department of Materials Science and Engineering Stanford UniversityStanfordCA94305USA Department of Computer Science Stanford UniversityStanfordCA94305USA Department of Computer Science University of Southern CaliforniaLos AngelesCA90089USA

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximize material properties;however,materials design often requires finding specific subsets of the design space which meet more complex or specialized *** present a framework that captures experimental goals through straightforward user-defined filtering *** algorithms are automatically translated into one of three intelligent,parameter-free,sequential data collection strategies(SwitchBAX,InfoBAX,and MeanBAX),bypassing the time-consuming and difficult process of task-specific acquisition function *** framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision *** demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization,and show that our methods are significantly more efficient than state-of-the-art ***,our framework provides a practical solution for navigating the complexities of materials design,and helps lay groundwork for the accelerated development of advanced materials.

关键词： synthesis straight consuming

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Security Challenges of Intent-Based Networking

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Communications of the ACM 2024年第7期67卷 56-65页

作者： Kim, Jiwon Okhravi, Hamed Tian, Dave Jing Ujcich, Benjamin E. Department of Computer Science Purdue University West LafayetteIN United States MIT Lincoln Laboratory LexingtonMA United States Department of Computer Science Georgetown University WashingtonDC United States

computer SYSTEMS HAVE evolved over decades to enable more flexible programmability. Unsurprisingly, such programmability has converged more closely to how humans think and speak. This is perhaps best exemplified in the evolution of programming languages: an obtuse set of ones and zeros (machine language), small snippets of instructions (assembly language), early low-level abstractions with human-readable syntax (for example, ALGOL or C), and today’s high-level abstractions with complex operations (for example, Python or Go). With each subsequent generation, programming languages have described richer semantics that enables desired functionality to be expressed more naturally. Not only has this evolution made programming easier, but such evolution allows humans to focus on what they want to do instead of how the machine should be programmed to do it. In other words, the evolution of high-level semantics has allowed humans to focus on the intent instead of the mechanism. ©2024 Copyright is held by the owner/author(s)

关键词： Semantics

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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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