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2022 1st international conference on Artificial intelligence Trends and pattern recognition, ICAITPR 2022

2022 1st International Conference on Artificial Intelligence...

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1st international conference on Artificial intelligence Trends and pattern recognition, ICAITPR 2022

ISBN: (纸本)9781665425216

The proceedings contain 49 papers. The topics discussed include: analysis of customer reviews using deep neural network;a comparative investigation on the use of machine learning techniques for currency authentication;a diagnostic survey on sybil attack on cloud and assert possibilities in risk mitigation;e-health security on could computing and its challenges;high speed multiplier using embedded approximate 4-2 compressor for image multiplication;a review on self-stabilizing platform in scope of merchant navy applications;study on information management system that connects students and instructor through chatting;machine learning approaches for microscopic image analysis and microbial object detection(MOD) as a decision support system;and literature survey on video surveillance crime activity recognition.

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1st international conference on pattern Analysis and machine intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st international conference on pattern Analysis and machine intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and machine intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical Image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing Image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent Image recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the Image Sensor and CNN-GRU Fusion Neural Network;stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature recognition Method Based on Comb Filter;emotional state recognition of English Learners Based on Deep Learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.

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1st international conference on Computational intelligence in pattern recognition, CIPR 2019

1st International Conference on Computational Intelligence i...

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1st international conference on Computational intelligence in pattern recognition, CIPR 2019

ISBN: (纸本)9789811390418

The proceedings contain 88 papers. The special focus in this conference is on Computational intelligence in pattern recognition. The topics include: Classification of Speech and Song Using Co-occurrence-Based Approach;Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and stress Surveys;automatic Multilingual System from Speech;an Improved Data Hiding Scheme Through Image Interpolation;generalization of Multi-bit Encoding Function Based Data Hiding Scheme;a machine Learning Approach to Comment Toxicity Classification;a Minutia Detection Approach from Direct Gray-Scale Fingerprint Image Using Hit-or-Miss Transformation;face Identification via strategic Combination of Local Features;a study on Reversible Image Watermarking Using Xilinx System Generator;a Novel Enhancement and Segmentation of Color Retinal Image Based on Fuzzy Measure and Fuzzy Integral;SVM and MLP Based Segmentation and recognition of Text from Scene Images Through an Effective Binarization Scheme;user Identification and Authentication Through Voice Samples;template Matching for Kinship Verification in the Wild;Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT;statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection;Singer Identification Using MFCC and CRP Features with Support Vector machines;object Proposals Based on Variance Measure;deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen;Robust Non-blind Video Watermarking Using DWT and QR Decomposition;detection and Classification of Earthquake Images from Online Social Media;On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm;numerical Integration Based Contrast Enhancement Using Simpson’s Method;self Driving Car.

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Multi-modal Knowledge Graph Completion: A Survey 24

Multi-modal Knowledge Graph Completion: A Survey

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Tongtong Beijing Forestry Univ Beijing Peoples R China

ISBN: (纸本)9798400707032

In recent years, with the rapid development of artificial intelligence, multi-modal knowledge graph completion (MMKGC) has become increasingly important. Many scholars have conducted in-depth research on multi-modal knowledge graphs (MMKGs), leading to the proposal of numerous MMKGC models. Summarizing the current state of research is crucial for guiding future studies. This survey aims to review the current advanced techniques for MMKGC. By analyzing and elaborating on the value and categories of MMKGs in detail, we summarize the challenges faced by existing MMKGC methods. Our work provides valuable insights and explorations for the research and application of completing MMKGs.

关键词： Multi-modal Knowledge Graph Completion Explainable Artificial intelligence Few-shot Learning

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Optimization of Intelligent Substation Location Scheme Based on Data Fusion Technology and Artificial intelligence 24

Optimization of Intelligent Substation Location Scheme Based...

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Wang, Zhipeng Xie, Dan Feng, Cai He, Chunhui Lan, Feng State Grid Shandong Elect Power Co Econ &Technol Res Inst Beijing Peoples R China

ISBN: (纸本)9798400707032

At present, the world is experiencing the second information wave with data as the core and the Internet as the means, and the society is shifting from IT (information) era to DT (data) era. The planning of substation location is mainly based on the existing satellite vector data through the software simulation platform, and the optimal scheme and path planning are often based on the experience of designers in actual design and erection. However, with the continuous expansion of the project scale and increasing complexity, it has brought great challenges to the design work. Therefore, substation location needs to be combined with current data fusion technology and artificial intelligence technology to effectively improve the intelligent level of substation location.

关键词： Substation location Data fusion technology Artificial intelligence technology Weighted Voronoi diagram

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Research on distribution inspection planning strategy based on artificial intelligence image recognition 24

Research on distribution inspection planning strategy based ...

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Gao, Fengxi Wu, Zhuohang Han, Zhentao Wang, Qixiang He, Xin Sun, Yan Zhao, Dewei Zhao, Wengang Sun, Mingze State Grid Liaoning Elect Power Compant Bcon Res Inst Shenyang Liaoning Peoples R China

ISBN: (纸本)9798400707032

In order to actively promote the construction of new power systems and the digital transformation and upgrading of power grid companies, and explore the application of artificial intelligence technology, the power grid company has built artificial intelligence "two libraries and one platform", that is, artificial intelligence platform, sample library, model library, and has carried out a lot of work in the application of artificial intelligence technology in equipment management, safe operation, marketing customer service and other business fields, and has precipitated rich samples, algorithms, and model achievements. With the increasing demand for intelligent application services and the increasing demand for samples and models in various business departments, it is urgent to introduce capabilities such as intelligent identification of electrical components and defect detection in scarce scenes on top of the existing artificial intelligence basic support capabilities to realize model training, iteration, optimization and improvement of accuracy, and deeply integrate the basic capabilities of artificial intelligence with the core business applications of the power grid. Further improve the ability to empower the profession and serve the grassroots.

关键词： New power systems Drone inspection Artificial intelligence Defect detection

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Prediction of Driver Intention Using EEG and GSR Signals for Advanced Driver Assistance Systems 24

Prediction of Driver Intention Using EEG and GSR Signals for...

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Li, Jiaxiang Liu, Yadong Hu, Dewen Sun, Jianxiang Zhou, Tianjian Xu, Long Li, Yishan Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Hunan Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China

ISBN: (纸本)9798400707032

As vehicle intelligence increases, driver assistance systems continue to advance to support the driver. Driver condition monitoring is a key component of such systems, which utilize a variety of sensors to collect real-time data about drivers' physiological and psychological metrics. These data are analyzed by computer vision, signal processing, pattern recognition and multi-source information fusion techniques to assess the driver's condition and driving behavior. This study aims to predict the driver's intention to turn left, turn right, accelerate, decelerate, and go straight by fusing EEG and GSR signals prior to the driver's actual maneuvers. The study used Todynet network to obtain a certain prediction accuracy and the validity of the model was verified by simulation environment.

关键词： EEG GSR Driver Intent recognition Todynet

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Identifying AI-Written Text in Academia: A machine Learning-Based Framework 1

Identifying AI-Written Text in Academia: A Machine Learning-...

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1st international conference on Electrical, Computer, Telecommunication and Energy Technologies

作者： Maaloul, Kamel Univ El Oued Fac Exact Sci LIAP Lab Dept Comp Sci El Oued Algeria

ISBN: (纸本)9798350388497;9798350388480

The rise of AI-generated text poses new challenges to academic integrity, requiring robust mechanisms to identify and differentiate human-written content from machine-generated material in scholarly publications. This paper presents a machine learning-based framework for detecting AI-written text in academia, combining multiple techniques including stylometry, semantic analysis, and citation pattern recognition. The framework utilizes supervised classification models trained on features such as lexical diversity, syntactic complexity, and redundancy patterns to distinguish AI-generated content from genuine research writing. Additionally, unsupervised anomaly detection techniques are employed to flag unusual stylistic deviations. The framework also integrates traditional plagiarism detection tools and enlists human expert review for validating suspicious sections. By addressing both technical and ethical considerations, this approach aims to preserve the authenticity of academic work while adapting to the evolving landscape of AI-driven content generation.

关键词： Generative artificial intelligence research papers machine learning AI-generated text Framework

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The application of artificial intelligence to classroom attendance 24

The application of artificial intelligence to classroom atte...

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Xiang Liu, Chun Software Engn Inst Guangzhou Dept Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9798400707032

Traditional class attendance requires the teacher to call the roll or other auxiliary devices, which requires manual intervention and occupies a lot of time for teachers and students. This paper introduces the improved YOLO8 algorithm to practice face detection accurately to complete class attendance. First, the global attention mechanism GcNet model is introduced into the algorithm to help the model focus on key information better. The performance of the model is optimized. Secondly, the loss function adopts WIoU method to enhance the detection ability of small faces. Finally, the improved target detection head realizes the detection of small faces. Experimental result show that the improved YOLO8 algorithm can improve mAP50 by about 6% compared with the unchanged YOLO8 algorithm. This mode can also be used for face recognition, which proves the practicability of this model.

关键词： Class attendance YOLOv8 GcNet WioU Small faces Face detection Face recognition

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stacked Multi-Head Cross Attention for Image recognition 24

Stacked Multi-Head Cross Attention for Image Recognition

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1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Shuaichen Du, Xiao Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun Jilin Peoples R China Jilin Univ Sch Math Changchun Jilin Peoples R China

ISBN: (纸本)9798400707032

Long-range dependency plays a critical role in extracting intricate image features particularly in tasks involving image recognition. In previous study, the significance of long-range positional dependencies has been proved in both image classification and image segmentation. Based on this, we introduce a Multi-Head Cross Attention module, namely MHCA, along with four different operators, which are designed to capture and integrate contextual information at every pixel position within feature maps, spanning both horizontal and vertical directions, with parallel fashion, thus can transfer information and share weights across multiple heads of features. Moreover, by stacking our module twice, forming MHCA(2) layer, the whole context of each pixel in feature can be captured, with more lighter computation burden than general full connection or Non-local networks, and it is designed to be seamlessly plugged into existing network architectures. By replacing specific convolution layer in convolutional networks with a MHCA(2) layer, we construct MHCA network. Through extensive experiments upon various datasets, we demonstrate the validity of our approach. Furthermore, comparative analysis with similar methodologies highlight the superior performance of our method.

关键词： Cross Attention Image Classification Image Segmentation Long-range Dependency Lightweight Architecture

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