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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Dongmei Fan Wendou Yan Yi Liang Zhi Cheng Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou China

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid.

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AIxAS 2022 - Proceedings of the Italian Workshop on Artificial intelligence for an Ageing Society 2022, co-located with 21st international conference of the Italian Association for Artificial intelligence, AIxIA 2022

AIxAS 2022 - Proceedings of the Italian Workshop on Artifici...

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3rd Italian Workshop on Artificial intelligence for an Ageing Society, AIxAS 2022

The proceedings contain 5 papers. The topics discussed include: sarcopenia evaluation by using surface EMG-based platform with supervised classification;sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset;using virtual reality to assess age-related differences in driving behavior;ChAALenge: an ambient assisted living project to promote an active and health ageing;and artificial intelligence facing multidimensional poverty in elderly.

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Data Analytics as a Bridge Between Pre and Post Conditioned Osteoarthritis Handwriting: Forensic intelligence 3rd

Data Analytics as a Bridge Between Pre and Post Conditioned ...

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3rd international conference on Artificial-Business Analytics, Quantum and machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Mukhtar, Jahan Zaib Kaur, Ridamjeet Department of Forensic Science Chandigarh University Mohali Gharuan India

ISBN: (纸本)9789819725076

Data analysis prepares the forensic data of many cases that contributes the solution to the additional cases that involve forensic handwritings, signatures, ballistics, forensic chemistry and DNA analysis. Many cases have been reported where data analytics and machine learning used in crime prevention. Development of new techniques has created interventions that provided insight into examination procedure and future investigation. Data science techniques provide a unique opportunity to revolutionize forensic handwriting examination. Forensic handwriting examiners can extract, analyse, and compare handwriting features with better speed and accuracy by applying machine learning algorithms, statistical analysis, and other data-driven methodologies. Therefore, an attempt has been made to analyse the data to bridge the connection in pre and post OA conditions. In line to the previously reported cases, the present research has been conducted to study the impact of osteoarthritis (OA) handwriting features in comparison pre-and post-conditioned samples using statistical analysis. Osteoarthritis (OA) is a joint disorder that can cause discomfort, stiffness, and pain. As the illness can affect any joint in the body;hips, knees, and hands are the most commonly affected body parts. In present research study, pre- and post-conditioned OA handwriting features are statistically analysed as well as qualitatively analysed. Data science has aided in identification of deviation in handwriting characteristics from pre to post conditioned OA handwriting features. This can give forensic handwriting examiners important insights and help them draw more accurate and dependable conclusions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data Science

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3rd IEEE 2022 international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

3rd IEEE 2022 International Conference on Computing, Communi...

引用

3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

ISBN: (纸本)9781665462006

The proceedings contain 168 papers. The topics discussed include: communication induced checkpointing based fault tolerance mechanism – a review and CIAC-FTM framework in IoT environment;a semantic review on challenges, trends towards defensive ids in Internet of things;semantic segmentation in medical image based on hybrid Dlinknet and UNet;smart car - one stop for all automobile needs;hybrid cryptography algorithm for the internet of things based plant information security;5G network: architecture, protocols, challenges and opportunities;fiber Bragg grating temperature sensor and calibration scheme in high magnetic field environment : an application for aluminum electrolysis cell in potline;federated learning with blockchain: a study of the latest decentralized couple;an enhanced copy-deterrence scheme for secure image outsourcing in cloud;decentralized data privacy protection and cloud auditing security management;land registry and title management using blockchain and smart contracts;breast cancer modeling and prediction combining machine learning and artificial neural network approaches;and confluence of artificial intelligence and blockchain powered smart contract in finance system.

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Control Design of Smart Home Bookshelf

Control Design of Smart Home Bookshelf

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Information Technology,Big Data and Artificial intelligence (ICIBA), international conference on IEEE international conference on

作者： Xin Liu School of Intelligence & Electronic Engineering Dalian Neusoft University of Information Dalian China

With the development of the Internet of Things and artificial intelligence, smart home has also entered an era of rapid development. Although many publications are widely accepted in the form of e-books and paperless office is increasingly popular, there are still many people who like to read or collect paper books. If there are a large number of books at home, most of them are easily forgotten on the shelf, and it is very inconvenient to access and search, especially for many books or materials that have not been electronic in the past, and it becomes very difficult to find them over time. This design uses OpenCV text recognition technology to realize the recognition of book information, uses the electric slide to complete the access of books at high places, uses the method of combination of bookcase to facilitate the intelligent expansion and reduction of bookshelves, and the client program carries in-depth intelligent applications, which greatly facilitates the use of users.

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Radiological Approach Through Artificial intelligence in the Field of Health Care 6

Radiological Approach Through Artificial Intelligence in the...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Dhyani, Bijesh Kumar, Brijesh Agarwal, Prakhar Kumar, S. Barath Kumar, P. Karthigai Mahajan, Rupali A. Graphic Era Hill University Uttarakhand Dehradun India School of Commerece and Management IIMT University Uttar Pradesh Meerut India Teerthankar Mahaveer University Department of Orthopaedics Uttar Pradesh Moradabad India Karpagam Academy of Higher Education Department of Biotechnology Tamilnadu Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India COMP Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350304480

Microarray technology, which refers to the retrieval of numerous numeric characteristics from clinical pictures, is a term that is comparatively new to the field of radiography. machine learning (AI) is generally referred to as a collection of highly developed numerical programs that essentially learn the trends in the input data to make forecasts on previously unexplored data sets. Radiomics can be used in conjunction with AI due to its superior capacity to deal with vast amounts of data when contrasted with traditional analytical techniques. Next to each other, these areas'main goal is to unearth and meaningfully assess as much concealed quantifiable information as is practical for use in data integration. Most psychiatrists are concerned about being replaced by clever computers as a result of the focus that both microarray technology and AI have indeed recently received for them before most in a variety of radiographic duties. Because of the accessibility of large data sets and the ongoing development of processing capacity, it seems unavoidable that people and computers will eventually coexist in therapeutic practice. The doctors must therefore be acquainted with both of these ideas, independent of their emotions. We set out to accomplish three things with from this paper: first, acquaint clinicians with metabolomics and AI;3rd, motivate medical doctors to engage in these rapidly evolving disciplines;but rather third, offer a set of guidelines for best practices in the conception and evaluation of future designs. © 2023 IEEE.

关键词： Analytical techniques Artificial intelligence best practices Data integration Genomics Guidelines Image acquisition machine learning medical doctors Metabolomics Nanomedicine Proteins Radiomics Segmentation Texture-based analysis

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Human Age Estimation Based on Dental Images 3

Human Age Estimation Based on Dental Images

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Raidzan, Nur Ain Mohtar, Itaza Afiani Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Computer Science Studies College of Computing Teknologi MARA Perak Branch Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality. © 2023 IEEE.

关键词： age estimation convolutional neural network forensic Inception-V3 machine learning pre-teen dental images)

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Customized Convolutional Neural Networks for Efficient MNIST Digit recognition

Customized Convolutional Neural Networks for Efficient MNIST...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Parul Nasra Sheifali Gupta R Archana Reddy Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

In image classification, identification of handwritten digits forms a simple choreacle especially with datasets such as MNIST that has grown to become a benchmark for testing machine learning models. Though several models for digital recognition have become available, achieving high accuracy and economy in processing remains a difficulty. It is meant to present a tailored design for a Convolutional Neural Network (CNN) with regard to the MNIST dataset, which should achieve an optimal balance between accuracy and speed. For improving the learning ability and robustness of the network, special architectural design changes are incorporated into our proposed architecture: dropout regularisation and optimum convolutional layers. Using these techniques the model precisely identifies the digits at 98%. This excellent performance of the model underlines its potential to be applied in practical scenarios where speed and accuracy are crucially important. The work advances this increasingly popular field of deep learning, insofar as it demonstrates how fine-tuned CNNs can be engineered to yield accurate and efficient results for more challenging image classification tasks, with far-reaching consequences for broader machine learning applications.

关键词： Deep learning Handwriting recognition Accuracy Biological system modeling Computational modeling Robustness Convolutional neural networks Optimization Image classification Postal services

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Infrared spectroscopy electronic instruments combined with lightweight DAResNet12 network for gas identification method research 3

Infrared spectroscopy electronic instruments combined with l...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Wang, Zhikang Zhao, Guodong Zhang, Junming School of Electronic Information Shanghal Dianji University Shanghai China School of Arts and Sciences Shanghal Dianji University Shanghai China College of Information Engineering Huanghuai University Henan China

ISBN: (纸本)9781510671423

In order to quickly and accurately identify the gas infrared spectral components, a relatively new research result, the extended residual network (DA-ResNet12), is introduced. The gas infrared spectral data measured using an infrared spectroelectronic meter were selected for the experiment. First, the raw data are preprocessed, and the spectral features are extracted and the feature vectors are generated by feature engineering;then, the lightweight DA-ResNet12 model is built and the model parameters are adjusted;finally, the proposed lightweight DA-ResNet12 model is compared with the commonly used algorithms according to the evaluation indexes such as the classification accuracy, the required training time, and the size of the model parameters. The experimental results demonstrate that the newly proposed DA-ResNet12 network model outperforms other comparative algorithms in terms of the number of model parameters, accuracy and time efficiency. © 2023 SPIE.

关键词： pattern recognition

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A Comprehensive Study on the Integration and Impact of Bits and Bytes in the Digital Era 3

A Comprehensive Study on the Integration and Impact of Bits ...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kaliappan, S. Maranan, Ramya Ali, H. Mohammed Yamsani, Nagendar Lovely Professional University Division of Research and Development Punjab Phagwara India Saveetha School of Engineering Simats Department of Research and Innovation TN Chennai India Srm Institute of Science and Technology Department of Mechanical Engineering Ramapuram Campus TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

Technology, data analysis, efficiency in using resources, and practical applications are just a few of the many areas that have benefited greatly from the digital era's merging of Bits and Bytes. In this in-depth research, we present a new approach to evaluating the results of this combination by using Agent-Based Modeling (ABM), machine Learning (ML), and Network Analysis. Novel approaches are used to compare six performance assessment metrics, including ABM, ML, and others. The suggested approach shines in terms of novelty in technology, analysis of data, efficiency in the use of resources, precision, adaptability, and practicality. While ML makes use of data-driven insights, Network Analysis reveals complicated network architecture, while ABM provides a fine-grained simulation of individual agent interactions. Cost-benefit analysis demonstrates financial feasibility, while simulation and modeling improve forecasting accuracy. In contrast, time-tested techniques like ABM and ML have been shown to be mostly ineffective, particularly in terms of efficiency and practicality. Although these techniques do provide useful information, the suggested method demonstrates the superiority of integrated approaches. The findings demonstrate the importance of the comprehensive method for comprehending the far-reaching effects of Bits and Bytes amalgamation in all its forms. This research deepens our understanding of the dynamic relationship between digital data and material objects in the modern digital age. It emphasizes the significance of the suggested strategy for conducting thorough evaluations and making well-informed decisions across a wide range of fields. © 2023 IEEE.

关键词： Efficiency

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