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共 3348 条记录，以下是941-950 订阅

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Ethical Challenges in AI 22

Ethical Challenges in AI

引用

15th ACM international conference on Web Search and data mining (WSDM)

作者： Baeza-Yates, Ricardo Northeastern Univ Silicon Valley Inst Experiential AI San Jose CA 95113 USA

ISBN: (纸本)9781450391320

In the first part we address four current specific challenges through examples: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models);(2) stupid models (e.g., lack of semantic and context understanding);(3) physiognomy (e.g., facial biometrics based predictions);and (4) indiscriminate use of computing resources (e.g., large language models). these examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles, (2) cultural differences;(3) regulation and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future.

关键词： Ethics in AI machine learning bias feedback-loops participation inequality

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data mining Driven Modelling on the Individual Heterogeneity of Economic Preference: A Metaphor Corpus of Neuroeconomics 4th

Data Mining Driven Modelling on the Individual Heterogeneity...

引用

4th international conference on Innovative Computing, IC 2021

作者： You, Yucong Guangzhou College of Business and Technology GD Guangzhou51000 China

ISBN: (纸本)9789811642579

this paper proposes an economic preference theory based on neuroeconomics and economic metaphor, and initially constructs a metaphorical mapping context-corresponding economic preference convolutional neural network metaphor corpus model;machine deep learning algorithms and Markov jump system chain thought are both applied, with the aid of convolutional neural network model structure visualization experimental tools as empirical research methods and experimental means, the economic preference theory of economic metaphor context and experience mapping has been carried out empirically. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Research on Web Components and Fingerprint Intelligent mining Methods

Research on Web Components and Fingerprint Intelligent Minin...

引用

international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Kaiming Yang Tianyang Zhou Guoren Zhong Junhu Zhu Ziqiao Zhou Institute of Cyberspace Security Information Engineering University Zhengzhou China Institute of Information and Communication National University of Defense Technology Wuhan China

Web component fingerprint library is the basis to solve the problem of Web component identification. A complete and accurate Web component fingerprint library can effectively improve the Web component identification capability. At present, the expansion mode of Web component fingerprint database is still mainly based on expert experience for manual mining, which is difficult to expand and update. therefore, there is an urgent need for a method to efficiently extend the Web component fingerprint library. To solve this problem, an intelligent method for mining Web components and fingerprints is proposed. this method uses the idea of manual mining new components for reference, and uses the search result characteristics of Web components in search engines to intelligently mine new Web components. At the same time, the fingerprint of Web components can be obtained automatically through data mining on the websites where new components are applied. the experimental results show that 22 new components and 102 component fingerprints have been found in a short time by using intelligent mining methods, which can efficiently mine Web components and fingerprints. Compared with the current mainstream manual mining methods, the efficiency of this method is greatly improved, which proves the feasibility of this method.

关键词： databases Manuals machine learning Fingerprint recognition Search engines Big data Libraries

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Construction of Computer Mega data Security Technology Platform Based on machine learning 4

Construction of Computer Mega Data Security Technology Platf...

引用

4th IEEE international conference on Information Systems and Computer Aided Education, ICISCAE 2021

作者： Liu, Zhangrong Forestry Vocational Technical College Information Engineering Department of Fujian Nanping353000 China

ISBN: (纸本)9781665441247

With the wide popularization of the Internet, advanced technologies such as AI (artificial intelligence) and mega data have also been produced and widely used, effectively promoting the development and progress of related industries. In the era of mega data, people can get valuable information through data mining and analysis. the core goal of building a computer mega data security technology platform under AI environment is to build a corresponding computer mega data security technology platform through AI, so as to realize the protection of user data information. On the basis of simulating human behavior, ML (machine learning) technology enables computers to acquire more new skills and knowledge so as to complete the continuous optimization of organizational structure. this paper introduces the key technologies of network security and expounds the application of ML technology in the field of network security, then analyzes the solutions of ML in network security under the background of mega data, and finally looks forward to the development trend of ML in network security research. © 2021 IEEE.

关键词： Network security

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Image recognition Using machine learning for Classifying Finger Print recognition using direction filtering

Image Recognition Using Machine Learning for Classifying Fin...

引用

international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Neeraj Bhargava Ritu Bhargava Swapnesh Taterh Pramod Singh Rathore Apoorva Bhowmick MDS University Ajmer India Sophia Girls' College (Autonomous) Ajmer India Amity University Rajasthan India Manipal University Jaipur Jaipur India United Nations Secretariate New Yourk NY USA Baghdad Iraq

Image processing is the new trend of implementing the image classification and many kinds of researchers are being working on this kind of implementation to process the data in the form of images. the current paper is focusing on identifying the images and processing the fingerprints which hold the secret information. We learn about steganography preparation, that involves the images which consists of secret information, and we need to decrypt that kind of information with some tools and techniques. Here in this article, we are focusing on the new concept of image processing which can consider the fingerprint of the human to hide secret information. this kind of information is very sensitive and can be shared with anyone and the concept behind photo-response non- uniformity is the way of linking pictures to the camera sensors which are having well established finger printing information. the information we transform from one user to the other user in the government organizations must be confidential and we are performing better PRNU with machine learning and the implementation consists of different neural network approaches and these approaches are more likely to work with existing approaches of PRNU. machine learning and image processing will lead the concept of highest secured data transmission with the data encrypted in the form of fingerprint.

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Enhancing Spectrum Busting: A Federated learning Approach for Radio Fingerprinting for Defence Forces

Enhancing Spectrum Busting: A Federated Learning Approach fo...

引用

international conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Jyoti Ranjan Satapathy John Ayeelyan Rajesh M Bodade Faculty of Communication Engineering Military College of Telecommunication Engineering Indore India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Each transmitter has a unique transmission pattern due to semiconductor variations. Using Software Defined Radio (SDR) and Artificial Intelligence (AI), transmitters can be identified through their Radio Frequency patterns. With the growing number of transmitters, ensuring security, availability, and data privacy is crucial for military and civilian applications. this paper introduces a novel hybrid method for radio fingerprinting via RF classification and proposes a Federated learning (FL) model, achieving 86% accuracy in the client model and 89% in the universal model. the solution addresses spectrum sensing, signal classification, spoofing countermeasures and ensures security, low latency, and resource optimisation.

关键词： Radio frequency Semiconductor device measurement Heuristic algorithms Radio transmitters pattern classification Fingerprint recognition Sensors Security Artificial intelligence Software radio

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Research on Personalized Recommendation Algorithm for Housing Rental Based on data mining 23

Research on Personalized Recommendation Algorithm for Housin...

引用

Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Yingzhuo Xu Yu Bai School of Computer Science Xi'an Shiyou University China

ISBN: (纸本)9798400709449

As China's economy develops and people's living standards improve, the demand for housing is increasing day by day. Many people choose to rent a house as a temporary solution to their housing needs. In view of the large amount of data in the housing rental industry and the difficulty for users to obtain information, this paper proposes a personalized recommendation algorithm for house rental based on data mining. A user interest model is extracted based on the user's historical behaviour, and a grey correlation analysis is used to calculate the correlation between all housing data and the user interest model, and the housing information with a high correlation is recommended to the user. In this paper, the personalized recommendation algorithm will be studied to provide a method for personalized recommendation service for users' rental properties.

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Specific Emitter Identification Based on Multi-Feature Fusion

Specific Emitter Identification Based on Multi-Feature Fusio...

引用

Digital Society and Intelligent Systems (DSInS), international conference on

作者： Yong Liu Shiyu Yin Southwest China Institute of Electronic Technology Simulation Chengdu China School of Mathematics Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

With the continuous development of the intelligent information industry, non-cooperative Specific Emitter Identification (SEI) plays a significant role in fields such as information security, electronic warfare, and spectrum management. Currently, the feature engineering of SEI mainly includes two categories, traditional statistical methods and deep learning methods. Traditional methods, implemented by extracting statistical features, usually cannot fully extract the complex information of signals and rely on experts' domain knowledge. therefore, deep learning methods can automatically extract the subtle features of the signals but are sensitive to noise, resulting in constrained recognition performance and stability. To address these issues, we propose a feature fusion method that combines the performance of deep learning methods with the domain prior knowledge of traditional methods. Experimental results on simulation data show that the proposed method achieves higher accuracy and better robustness than these two basic methods, demonstrating its effectiveness.

关键词： Deep learning Accuracy Statistical analysis Statistical learning Feature extraction Robustness Stability analysis data mining Noise measurement Radio spectrum management

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Enhanced Medicinal Plant Identification: Leveraging machine learning and Image Processing for Accurate Raw Material recognition

Enhanced Medicinal Plant Identification: Leveraging Machine ...

引用

Intelligent Technologies (CONIT), international conference on

作者： G. Fayaz Hussain Sayeeda Sameera Mahnaaz Sheema Parveen Shaik Shazia Tazeen Arigela Sravika Dept. of CSE Ravindra College of Engineering for Women Kurnool India Dept. of CSE(AI) Ravindra College of Engineering for Women Kurnool India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

data on medicinal plants and raw materials encompasses details about diverse botanical resources employed for their healing attributes in both traditional and contemporary medical practices. the difficulties in improving the identification of medicinal plants include fluctuations in image quality caused by elements such as lighting, background clutter, and camera angles, which affect the accuracy of algorithms, as well as the shortage of varied training data, potentially resulting in biases or inaccuracies during recognition. To address the challenges mentioned earlier, utilize customized Extended Convolutional Neural Network (CNN) techniques capable of managing variations in image quality and utilizing diverse training data to enhance the precision of medicinal plant identification. the Extended CNN approach provides better flexibility in handling varied image attributes and heightened precision in medicinal plant recognition, thanks to its specialized architecture and utilization of extensive training datasets.

关键词： Medicinal plants Training Image quality Accuracy Image recognition Computational modeling Training data

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A Novel Image Deep learning for recognition of Silicon Photonics Redistribution Layer

A Novel Image Deep Learning for Recognition of Silicon Photo...

引用

Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Jen-Kuang Fang Chao-Kai Hung Peng Yang Wen-Long Lu Chien-Wei Chang Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Due to the design requirements for miniaturization and high-density placement of silicon photonics (SiPh) components in integrated circuit (IC) packaging, the system in package (SiP) process and equipment have become important but challenging. Automatic optical inspection (AOI) detection equipment in IC packaging lines is also important in identifying redistribution layer (RDL) anomalies that affect signal transmission. If the AOI misrecognition rate is too high, the production yield and overall equipment performance are lowered. To solve the above problems, we proposed a novel Pix2Pix generative adversarial network (GAN) algorithm based on machine learning methods. the new feature extraction method has demonstrated highly sensitive image detection capabilities after processing tens of thousands of huge SiPh RDL image databases.

关键词： Deep learning System-in-package Image recognition Production Generative adversarial networks Silicon photonics Optical imaging

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