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共 7294 条记录，以下是1411-1420 订阅

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A Framework for Recommending Products to Solve Cold Starts Problem Using FP Growth Algorithm 15th

A Framework for Recommending Products to Solve Cold Starts P...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Dani, Virendra Bhati, Nisha Nagar, Sneha Mandloi, Ayesha Matkar, Mayank Department of Computer Science and Engineering Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Computer Science and Engineering Medicaps University Indore India Department of Electronics and Communication Engineering Indore Institute of Science & Technology Indore India Department of Computer Science and Engineering SKITM-Technical Campus Indore India

ISBN: (纸本)9783031889912

Different product characteristics and consumer expectations must be analyzed when making a product or service recommendation based on use. However, if all types of knowledge are inaccessible, this is known as the clod start problem in a recommendation system. To explain and address the issue of cold start, this paper suggests a structure for user experience-based product recommendation. As a result, this paper introduces a four-phase recommendation scheme. In the first step, site usage data is preprocessed and used to make product recommendations. The frequent pattern-based recommendation is performed in the next step. The decision is made in the third process based on the user's current search (user click streams) and related user habits seen in network access logs. In addition, the final recommendation is based on filtering the results obtained in the third step, as well as the product or service's cost, brand, and social review. The results show that the prediction is accurate and precise. Furthermore, compared result of proposed system to traditional system using Apriori Algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Apriori cold start problem Data Mining frequent pattern k-NN Prediction Recommendation System Web Log

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Comparison of Algorithms for Classification of Financial intelligence Reports 3rd

Comparison of Algorithms for Classification of Financial Int...

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3rd EAI international conference on Data and Information in Online Environments (DIONE)

作者： Zaina, Roberto Jeronimo de Macedo, Douglas Dyllon Dutra, Moises Lima Culmant Ramos, Vinicius Faria de Araujo, Gustavo Medeiros Univ Fed Santa Catarina Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

This work shows the application of machine learning algorithms to decide whether Financial intelligence Reports analyzed by the Brazilian Federal Police should be investigated or archived. We explain how the suspicious financial operations found in the Financial intelligence Reports are a crucial piece of information to combat money laundering crimes. We depict the processing of such reports, which are often used to initiate a police investigation. When that is not the case, the reports are archived. In this work, we propose using machine learning to analyze these reports. We trained and used three classification algorithms: Decision Tree, Random Forest, and KNN. The results show that most reports should be archived. While Decision Tree and Random Forest indicated that about 2/3 of the reports should be archived, KNN indicated that about 4/5 of them should be archived. In the end, this work shows the feasibility of automating the analysis of the Financial intelligence Reports by the Brazilian Federal Police, despite the need for more adjustments and tests to improve the accuracy and precision of the models developed.

关键词： Financial intelligence report Artificial intelligence machine learning

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Robust Deep Learning Model for Real-Time Vehicle Detection in IoV Applications 3

Robust Deep Learning Model for Real-Time Vehicle Detection i...

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3rd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Thakur, Kunal Taneja, Ashu Ankushavali, Md Obbayeda, Saif Chitkara University Institute of Engineering and Technology Punjab India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal 506371 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798331521691

Crucially important for traffic management, autonomous navigation, and road safety, vehicle recognition is the pillar of intelligent transportation systems (ITS). This work offers a convolutional neural network (CNN)-based architecture meant to solve the difficulties in vehicle recognition under various environmental settings. Using cutting-edge designs including ResNet and YOLOv4, the model combines regularizing methods, data preparation, and transfer learning to improve *** data augmentation and hyperparameter tweaking, the approach consists in strong training procedures that significantly increase both training and testing accuracy. With a test accuracy of 95% and a clear drop in test loss, experimental data show the great dependability of the model. Effective generalizing shown by validation loss patterns helps to minimize overfitting and guarantees applicability to unprocessed *** vehicle identification to classification, these results emphasize the promise of CNN-based models for practical ITS uses. While suggesting future improvements, such including multi-modal data and optimizing for edge devices, the paper ends with addressing constraints including deployment scalability and environmental robustness. This study helps ITS models to be more accurate and efficient, therefore opening the path for more effective vehicle recognition systems. © 2025 IEEE.

关键词： Accident Reduction with Smart Systems Innovation in Traffic Systems Road Safety Safe and Efficient Transportation Systems Smart Transportation Sustainable Urban Development Urban Mobility Solutions

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Research on the Application of Voice Acoustics for Speech intelligence in Folk Songs 3rd

Research on the Application of Voice Acoustics for Speech In...

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3rd international conference on Advanced Intelligent Technologies, ICAIT 2022

作者： Lyu, Shiliang Chen, Shuang Qi, Hanyun Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Gansu Provincial Key Laboratory of Intelligent Processing of National Languages Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9789819952021

Folk songs take language as the carrier, and the singing tune and cultural connotation of singing are the important research contents of many music scholars, ethnologists, and linguists. In the process of folk song singing, the vocalization of the singing voice has an important influence on the singing voice and artistic performance. The state of the singing pronunciation can be carefully observed through the acoustic analysis of the voice. This paper will introduce the specific application of voice acoustic analysis method in the research of folk song singing. Combined with the singing characteristics of folk songs, the vocal type of singing voice can be obtained through the parameters of voice acoustic analysis. The significance and the feasibility of the application of voice acoustic analysis in folk song research will be further explored. The research parameters and conclusions are helpful to speech recognition, speech synthesis, and artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech synthesis

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional Signal Processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, Optimization and Its Applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring machine intelligence Using Black-Box-Based Universal intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Analysing the Need for 5G Networks based on Smartphone Market Penetration 3

Analysing the Need for 5G Networks based on Smartphone Marke...

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3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Panse, Chetan Chaskar, Aayush Sibm Symbiosis International Deemed University Pune India

ISBN: (纸本)9781665453578

The term '5G' refers to the latest advancements in mobile technology. The next key stage of mobile communications standards after the soon-to-be-implemented 4G standards is 5G. Due to the development of 5G technology, the bulk of high bandwidth users will change how they access their phones. With 5G pushed via a VOIP-equipped smartphone, people would experience a volume of calls and data transfer unlike anything else. [1] The word '5G' relates to the generation of wireless telecommunications, which will change multiple areas of our daily life. Due to emerging mobile technologies like virtual reality applications, HD video consumption, and cloud-based entertainment, mobile users and their usage is expanding very quickly. The rate of increase in demand and the predicted trends of new technologies, such as driverless vehicles and virtual reality, will far exceed the capacity of 4G networks in a few years Consequently, multiple attempts have been made by industry experts and research associations to make 5G networks a reality soon. Academicians and thought leaders have agreed that these new network systems would employ highly exciting, developed technologies like SDN and NFV to realize their objectives. The transmission speed of 5G is a lot greater than that of the present network Data transmission speeds up to 10Gbps, which are 10 to 100 times faster than 4G and 4G- LTE will be available with 5G. To facilitate the development of new services, 5G is anticipated to merge ultra-broadband networks and incorporate new-age technologies like the Internet of Things (IoT), blockchain, big data, artificial intelligence, machine learning and cloud computing. © 2023 IEEE.

关键词： 5G mobile communication systems

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Research on Target recognition and Location Algorithm Based on Camera and Lidar

Research on Target Recognition and Location Algorithm Based ...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), international conference on

作者： Zhaodong Li Tingting Yang Yiquan Wang Yu Yang Xuyang An Xijun Zhao China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing China China North Artificial Intelligence & Innovation Research Institute Beijing China

The method of Target recognition and location the key technology for team-accompanied robots to realize personnel-following navigation. In this paper, the target recognition algorithm based on YOLOv3 is combined with the target positioning algorithm based on European point cloud clustering to realize the recognition and location of following targets. The YOLOv3 algorithm using the camera as a sensor can accurately and real-time recognize the target person; the algorithm based on lidar point cloud clustering can effectively obtain the location information of the target person. By fusing the two algorithms, target location can be performed simultaneously with target recognition, and the reliability and accuracy of the algorithm are improved at the same time.

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Algorithm for Fault Diagnosis System of Pumping Units Based on Fuzzy Logic and Neural Networks

Algorithm for Fault Diagnosis System of Pumping Units Based ...

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Artificial intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Guannan Zhang No.4 Operation Area of No.6 Oil Production Plant of Daqing Oilfield Co. Ltd Daqing Heilongjiang China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In order to reduce the exploitation cost, improve the economic benefit and ensure the sustainable exploitation of crude oil, it is particularly urgent to innovate the oil extraction technology. This paper introduces two advanced intelligent technologies, fuzzy logic and neural network. Fuzzy logic can deal with uncertain and fuzzy information, which provides an effective means to deal with the complex faults of pumping units. Neural network, through its powerful self-learning and pattern recognition ability, provides a high-accuracy solution for fault classification and recognition of pumping units. Through experimental verification, this paper finds that the fault diagnosis method of pumping unit based on fuzzy logic and neural network is significantly better than the traditional method in accuracy and efficiency. This method can not only identify fault types and causes more accurately, but also realize timely early warning and prediction of faults, thus greatly improving the safety and production efficiency of oil fields. The fault diagnosis technology based on fuzzy logic and neural network provides strong support for the intelligent development of oil fields, and also lays a solid foundation for the intelligent, efficient and safe exploitation of oil fields in the future.

关键词： Fault diagnosis Fuzzy logic Accuracy Oils Neural networks Production Solids pattern recognition Safety Optimization

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A Tunnel Structural Health Monitoring and Damage Identification Algorithm Based on Deep Reinforcement Learning

A Tunnel Structural Health Monitoring and Damage Identificat...

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Artificial intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yike Wei Bin Yang China Merchants Chongqing Testing Center for Highway Engineering CO. LTD Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the continuous promotion of infrastructure construction such as water conservancy, transportation, and municipal engineering in China, the number of tunnels put into operation is increasing, making the healthy service and longterm operation of tunnel structures an urgent and important task. During tunnel operation, structural diseases such as cracks, voids, voids, and water seepage occur frequently. These diseases not only weaken the strength of the tunnel structure and shorten its service life, but also pose a serious threat to the safety of people's lives and property. This paper proposes a tunnel structure health monitoring and damage identification algorithm based on deep reinforcement learning (DRL) to address this issue. This algorithm fully utilizes the advantages of deep learning (DL) in feature extraction and pattern recognition, as well as the ability of reinforcement learning (RL) in decision optimization and adaptive learning. By constructing a model for the interaction between intelligent agents and the environment, it achieves real-time monitoring of the health status of tunnel structures and damage recognition. The experimental results show that the algorithm can accurately identify the damage situation of tunnel structures, effectively monitor the health status of tunnels, and provide strong technical support for the safe operation of tunnels.

关键词： Water conservation Feature extraction Deep reinforcement learning Real-time systems pattern recognition Intelligent agents Monitoring Optimization Testing Diseases

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NeuroDAVIS-FS: Feature Selection Through Visualization Using NeuroDAVIS 27th

NeuroDAVIS-FS: Feature Selection Through Visualization Using...

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27th international conference on pattern recognition, ICPR 2024

作者： Maitra, Chayan Sengupta, Anwesha De, Rajat K. Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of Applied Statistics Maulana Abul Kalam Azad University of Technology West Bengal Haringhata India

ISBN: (纸本)9783031783944

Dealing with high-dimensional datasets is challenging nowadays due to computational complexity, the curse of dimensionality, and model overfitting. It becomes necessary to reduce the dimension of the dataset for a better understanding of inherent information. Feature selection techniques are widely utilized to rank features based on their importance and accordingly reduce the dimension of the original datasets with respect to this ranking. Existing feature selection methods are mainly developed for specific downstream tasks and show several drawbacks e.g., not considering the inherent associations and their importance. In most of the cases, the methods are computationally expensive as well. In order to address such drawbacks, the present study aims to propose a feature selection method, called NeuroDAVIS-FS, which performs in an unsupervised learning setup without assuming any prior data distribution. Initially, it considers training using the model NeuroDAVIS, developed earlier for data visualization, and selects features according to the trained model. The efficacy of the proposed NeuroDAVIS-FS has been demonstrated on various datasets from different domains and found to be effective in comparison with state-of-the-art feature selection methods. In addition, two case studies on image and biological datasets with a very low sample-feature ratio, have been executed and found to be effective for relevant feature selection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature Selection

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