检索结果-内蒙古大学图书馆

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

2022 1st International Conference on Artificial Intelligence...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

1st International Conference on Computational Intelligence i...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empirical techniques for effort estimation in designing effective ML models

引用

international Journal of Computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence as a tool for missing person identification 1st

Artificial intelligence as a tool for missing person identif...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Bhardwaj, Samvedna Ahmad, Faizan Mishra, Vaishali Department of AIML Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of CSE Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

500 missing person cases are unresolved daily in India. Today’s world makes finding a missing person more difficult. Despite the widespread use of social media for information sharing, the process still involves extensive paperwork, consumes a significant amount of time, and offers little assurance of success. The project aims to address the challenges by implementing a technique for enhancing facial recognition searches. CNN is a highly effective deep learning technology for image-based applications. When a missing person’s image is processed, essential information is stored in the back end. Users can save photos of people they find suspicious in a database. The GUI program uses machine learning to compare user-submitted images with those uploaded by the person in charge. The system displays the match and the missing person’s last known location. © 2025 the Author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bird Detection and recognition Systems Through machine Learning and Artificial Intelligent: Advancements and Future Opportunities 21st

Bird Detection and Recognition Systems Through Machine Learn...

引用

21st international Symposium on Distributed Computing and Artificial intelligence

作者： Rossi Labianca, Lautaro Alvarez Uruena, Jaime Curto Hernandez, Javier Garcia Serrada, Raul Air Inst IoT Digital Innovat Hub Spain Deep Tech Lab Ave SantiagoMadrigal 39 Salamanca 37003 Spain Univ Salamanca BISITE Res Grp Edificio Multiusos IDICalle Espejo 2 Salamanca 37007 Spain

ISBN: (纸本)9783031820724;9783031820731

The paper provides an overview of advancements in bird detection and recognition systems using machine Learning and Artificial intelligence (AI). It highlights the increasing adoption of wind farms amid rising electricity demand, underscoring their environmental impact on avian species. To address these ecological challenges, the development of bird recognition solutions is crucial. The paper analyzes various techniques, including radar systems, sound recognition, Convolutional Neural Networks (CNNs), electromagnetic detection, YOLOv5, and color segmentation, discussing their features, computational costs, and constraints. It concludes that while deep learning models offer superior results, they need a balance between accuracy and speed, alongside training with large and representative datasets. Ultimately, the paper aims to contribute to efforts aimed at mitigating the adverse effects of wind farms on bird populations through advanced technologies. Additionally, through this paper we intend to shed light over the state of the art on bird detection systems and provide insights that intends to solve some of these drawbacks.

关键词： Bird recognition Bird detection machine Learning Artificial intelligence Computer Vision

来源：评论

学校读者我要写书评

暂无评论

Analysing Effectiveness of Artificial intelligence on Indian Judicial System 1

Analysing Effectiveness of Artificial Intelligence on Indian...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Biradar, Ashwini Shinde, Rucha Honap, Rohini Patil, Seema Naik, Ashutosh TMV's Lokmanya Tilak Law College Pune India

ISBN: (纸本)9798331518578

Artificial intelligence technology has been widely used in the legal system in recent years. Compared to human judges, artificial intelligence is more unbiased, competent, and efficient when used for judicial purposes. Mostly relying on huge data, algorithms, and processing power instead of natural intelligence, it has drawbacks. Artificial intelligence in the judiciary cannot fully replace human judges due to disparities in theoretical framework, application circumstances, and potential and ability. Declaring unequivocally that human judges will always be replaced by judicial artificial intelligence is essential. Investigating legal concerns and the several ways AI affects the judicial system is the goal of the study. Predicting a legal system's conclusion became feasible with the advent of artificial intelligence and related technologies. This study's objective is to provide the most precise legal case prediction possible. Furthermore, because of advances in machine learning (ML), natural language processing (NLP), and processing capacity, these data offer significant opportunities to employ machine-learning models to evaluate and improve judicial systems. DT achieved the lowest accuracy percentage, while KNN achieved the best at 72%. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Learning Algorithm in Artificial intelligence Archives Data 1

Adaptive Learning Algorithm in Artificial Intelligence Archi...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Wu, Yali Xuzhou College of Industrial Technology Xuzhou China

ISBN: (纸本)9798331529246

Aiming at the problems of single content, delayed update and low efficiency in the current university archives management, this paper applies an adaptive learning algorithm to improve the management efficiency and educational function of university school history archives, so that further promote the goal of "all-staff, full-process and all-round"education. First, this paper analyzes the characteristics and management needs of university archives data. Then, the adaptive learning algorithm is designed and applied in combination with artificial intelligence technology, and automatic classification, context recognition and dynamic feedback mechanisms are applied in archives data management. Then, this paper constructs the knowledge graph of school history archives by analyzing the key events, characters and time nodes in the school history archives. The average accuracy of the traditional retrieval method is 0.73, and the average recall rate is only 0.652. The application of adaptive learning algorithms further promoted the important role of archive resources in "all-staff, all-process, and all-round"education. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Application of machine Learning Algorithms in Artificial intelligence Image recognition Technology 1

Application of Machine Learning Algorithms in Artificial Int...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ouyang, Qi Dai, Siyang Xie, Kexin Sun, Zeyu Wang, Yao Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne VIC Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (纸本)9798331529246

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

1st EAI international conference on Computational intelligence and Generative AI, ICCIGAI 2024

1st EAI International Conference on Computational Intelligen...

引用

1st EAI international conference on Computational intelligence and Generative AI, ICCIGAI 2024

ISBN: (纸本)9783031766091

The proceedings contain 10 papers. The special focus in this conference is on Computational intelligence and Generative AI. The topics include: Prediction of Automobile Insurance Claims Using Deep Neural Networks;an IoT-Backed Blockchain System for E-waste Monitoring and Management in Smart Urban Environments;Cross-Silo Federated Learning for IDS in IoT Networks;effective Optimized Detection of Cardiovascular Disease by Supervised machine Learning Techniques;enhancing Healthcare Data Security with Homomorphic Encryption in Virtual Health Support;investigating Hyperparameter Effects on U-Net for Oral Epithelial Layer Segmentation;serverless Computing and DevOps: A Synergistic Approach to Modern Software Development;revoking Dynamic Access with Enforced Encryption.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：