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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Su, Yingying Deng, Yuanyuan Zhou, Nengyang Si, Hongyun Peng, Jie Chongqing University of Science and Technology College of Electrical Engineering Chongqing China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing China

ISBN: (纸本)9798350316520

To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to improve parameter efficiency and detection accuracy. The nearest neighbor up-sampling is substituted with CARAFE (a lightweight operator) to enhance the receptive field and utilize semantic information. GhostNetV2 replaces the CBS convolutional module for efficient feature extraction through stacking operations. Additionally, the Hard Swish activation function is incorporated to boost accuracy. Experimental results on the NEU-DET dataset demonstrate that the average accuracy of the improved YOLOv5s algorithm is 81.2%, a 1.7% increase compared to the original algorithm, the amount of computation is 17.2GFLOPs and the number of model parameters is 8.65M. The model effectively enhances detection accuracy. © 2024 IEEE.

关键词： Surface defects

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Research on Network Security Access Control Based on Improved Algorithm 24

Research on Network Security Access Control Based on Improve...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Li, Shenghao Wu, Yuanyuan Chen, Yingsheng School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering China School of Science and Technology Chongqing College of Humanities China

ISBN: (纸本)9798400718137

This article proposes a computer network access isolation control method based on trusted computing. Firstly, the wavelet analysis method is used to analyze computer network access, and then the frequent IP address distance algorithm is used to identify and analyze dangerous access. Based on the characteristics of dangerous access, a network access isolation control model is established using trusted computing technology to complete the overall structural design of the computer network access isolation control system, and analyze the content of each module in the structure. By analyzing the security attributes of network access isolation control, the process of computer network access isolation control is obtained, thereby achieving the establishment and application of a computer network access isolation control system. This method can effectively prevent malicious attacks on computer networks and ensure the safe and stable operation of computer networks. Experimental results have shown that the proposed method can effectively isolate and control computer network access, improve the accuracy of isolation control, reduce the difficulty of computer network access isolation control. © 2024 Copyright held by the owner/author(s).

关键词： Computer network

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Using Generative Models to Improve Fire Detection Efficiency 10

Using Generative Models to Improve Fire Detection Efficiency

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10th International Conference on Information technology and Nanotechnology, ITNT 2024

作者： Andriyanov, Nikita Kim, Alexandr Fao, Xenin Data Analysis And Machine Learning Department Financial University Under The Government Of The Russian Federation Moscow Russia Data Science Department Huazhong University Of Science And Technology Huazhong China

ISBN: (纸本)9798350372939

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1-2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models. © 2024 IEEE.

关键词： Generative adversarial networks

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SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow 38

SemFlow: Binding Semantic Segmentation and Image Synthesis v...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Chaoyang Li, Xiangtai Qi, Lu Ding, Henghui Tong, Yunhai Yang, Ming-Hsuan School of Intelligence Science and Technology Peking University China UC Merced United States Institute of Big Data Fudan University China

Semantic segmentation and semantic image synthesis are two representative tasks in visual perception and generation. While existing methods consider them as two distinct tasks, we propose a unified framework (SemFlow) and model them as a pair of reverse problems. Specifically, motivated by rectified flow theory, we train an ordinary differential equation (ODE) model to transport between the distributions of real images and semantic masks. As the training object is symmetric, samples belonging to the two distributions, images and semantic masks, can be effortlessly transferred reversibly. For semantic segmentation, our approach solves the contradiction between the randomness of diffusion outputs and the uniqueness of segmentation results. For image synthesis, we propose a finite perturbation approach to enhance the diversity of generated results without changing the semantic categories. Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks. We hope this simple framework will motivate people to rethink the unification of low-level and high-level vision. © 2024 Neural information processing systems foundation. All rights reserved.

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Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

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Automated Video Quality Analysis Using Deep Learning

Automated Video Quality Analysis Using Deep Learning

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Dewan, Yashas Vadivu, G. Malik, Mihir Singh Srm Institute of Science and Technology Department of Data Science and Business Studies Chennai Kattankulathur India

ISBN: (纸本)9798350382693

Video streaming is a common service utilized in today's times, ranging from popular platforms such as Netflix, Disney, and Amazon Prime to live video calls facilitated by WhatsApp and FaceTime. Given the growing computational capabilities and the imperative of delivering optimal end-user experiences, there arises a necessity to develop methods for testing and analyzing the video quality of streaming content. In our research, we propose a method for analyzing the same as well as a substantial publicly available dataset comprising thousands of distorted frames. We employ a Convolutional Neural Network (CNN) to train a model for discerning whether a frame in a video input is glitched or not, addressing the imperative of ensuring seamless video playback and enhanced user satisfaction. © 2024 IEEE.

关键词： Video analysis

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Skill Mount: Personalized Career Skills Development Using Machine Learning Algorithms 11

Skill Mount: Personalized Career Skills Development Using Ma...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Krishnan, Akhil Joseph, James Nihal, N.N. Salva Fathima, S. Priya, C.V. Muthoot Institute of Technology and Science Department of Artificial Intelligence and Data Science Kerala Kochi India

ISBN: (纸本)9798350379136

In today's rapidly changing job market, achieving career goals requires a strategic approach to skill development. Skill Mount is an intuitive web application designed to guide individuals through their career journeys, aiding in both planning and attainment of objectives through personalized technology-driven methods. Given the constant evolution of job roles and the competitive landscape, continuous skill enhancement is imperative for career advancement, making Skill Mount an indispensable tool. Serving as a personalized coach, it tailors daily skill acquisition plans, monitors progress, and suggests suitable job roles based on individual preferences. Leveraging artificial intelligence (AI), including Natural Language Processing (NLP) and Machine Learning (ML), Skill Mount analyzes user data to provide customized skill suggestions and job descriptions. The proposed system uses Recommendation Systems to propose relevant learning resources and utilizes an ensemble method (random forest algorithm) for job recommendations, As a result system acquired higher accuracy rates. With a focus on user experience (UX), Skill Mount ensures seamless interaction through its intuitive interface and automated features driven by AI. By simplifying the career journey and empowering individuals to adapt to the competitive job market, Skill Mount plays a pivotal role in facilitating career growth and success. © 2024 IEEE.

关键词： Commerce

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Deep learning and IoT for detecting and classifying leaf diseases in Agriculture 4.0: a systematic review

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International Journal of Internet Manufacturing and Services 2024年第4期10卷 346-376页

作者： Jena, Swarna Prabha Chakravarty, Sujata Paikaray, Bijay Kumar Department of ECE Centurion University of Technology and Management Odisha India Department of CSE Centurion University of Technology and Management Odisha India Center for Data Science SOA University Odisha India

Advancement of digital technology in agriculture leads to Agriculture 4.0. The technological revolution has provided the detection and classification of leaf diseases at an early stage. Hence, advancements are essential to reduce costs and increase quality and productivity. Deep learning techniques empower the system’s development, allowing better decisions to be made early, saving time and money. Nowadays, incorporating IoT in agriculture makes the system intelligent enough to support farmers independently. To know the direction, numerous methods have been systematically reviewed and kept in place to make the researcher understand. Learning the current state-of-the-art techniques for hardware requirements, dataset used, and performance matrices is crucial. Therefore, the core objective of the research article is to do a systematic literature review that analyses the research gaps, current trends, challenges and answers the research questions, which are helpful for investigation. Finally, this systematic review can be the starting point for upcoming researchers. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart agriculture

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Semi-Supervised Multi-View Multi-Label Learning with View-Specific Transformer and Enhanced Pseudo-Label 39

Semi-Supervised Multi-View Multi-Label Learning with View-Sp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Quanjiang Luo, Tingjin Jiang, Mingdie Jiang, Zhangqi Hou, Chenping Li, Feijiang College of Science National University of Defense Technology Hunan Changsha410073 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

ISBN: (纸本)157735897X

Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main challenges: i) How to extract advanced semantics from available views? ii) How to build a refined classification framework with limited labeled space? iii) How to provide more high-quality supervisory information? To address these problems, we propose a Semi-Supervised Multi-View Multi-Label Learning Method with View-Specific Transformer and Enhanced Pseudo-Label named SMVTEP. Specifically, Generative Adversarial Networks are employed to extract informative shared and specific representations and their consistency and distinctiveness are ensured through the adversarial mechanism and information theory based contrastive learning. Then we build specific classifiers for each extracted feature and apply instance-level manifold constraints to reduce bias across classifiers. Moreover, we design a transformer-style fusion approach that simultaneously captures the imbalance of expressive power among views, mapping effects on specific labels, and label dependencies by incorporating confidence scores and category semantics into the self-attention mechanism. Furthermore, after using Mixup for data augmentation, category-enhanced pseudo-labels are leveraged to improve the reliability of additional annotations by aligning the label distribution of unlabeled samples with the true distribution. Finally, extensive experimental results validate the effectiveness of SMVTEP against state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semi-supervised learning

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