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Design and Implementation of Distributed Image recognition App with Federal learning Techniques 5th

Design and Implementation of Distributed Image Recognition A...

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5th EAI international conference on Smart Grid and Internet of things, SGIoT 2021

作者： Chan, Yu-Wei Wu, Bo-You Huang, Yi-Ming Yang, Chao-Tung Department of Information Management Providence University Taichung Taiwan Department of Computer Science Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan

ISBN: (纸本)9783031203978

In recent years, machine learning technology has been widely used in many fields, such as smart transportation, smart healthcare, smart finance and smart cities. Although machine learning technology has brought people a lot of convenience, the privacy problem of user data has also emerged [1]. Considering that users are not necessarily willing to upload personal privacy data to the cloud for deep learning training, therefore, instead of consuming a lot of bandwidth to upload data to the cloud, it is better to train on the local device and then use the model parameters obtained after training. (For example: weights and bias, etc.) upload to the server for aggregation. this emerging machine learning technology is called federated learning. In this way, the privacy and security of data can be guaranteed, and the purpose of decentralized learning can be achieved through aggregation. this study uses the architecture of federated learning technology and convolutional neural network algorithms to implement distributed image recognition mobile applications. this application allows users to use their mobile devices and the central servers for repeated training. After multiple rounds of repeated training, the convergence will be stabilized, and the accuracy will be significantly improved. At the same time, it can take into account privacy and achieve the machine the purpose of learning. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image recognition

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On the Possibility of Developing a System for Predicting Students Academic Performance Using machine learning Methods

On the Possibility of Developing a System for Predicting Stu...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Y.A. Tsygankov A.Iu. Solovev M.S. Sergeychik Automated and Information Control Systems Department Stary Oskol Technological Institute (Branch) NUST "MISIS" Stary Oskol Russia

this article considers the possibility of developing data mining techniques and applying machine learning models to identify hidden dependencies that uniquely characterize different categories of students in order to predict their academic performance. the application of machine learning in this subject area is conditioned by their ability to analyze large amounts of data, obtaining, as a result, generalizing models capable of classifying the objects of study and predicting the required indicators. the development of such a system will allow, based on the analysis of a priori data and current indicators, to predict the expected performance of students in disciplines of different orientation, providing information on the categories of performance in a visual form.

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Time Series Decision Analysis Based on Least Squares Method and LSTM

Time Series Decision Analysis Based on Least Squares Method ...

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5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Li, Aiqi School of International Business Xiamen University Tan Kah Kee College Zhangzhou China

this study explores a novel approach combining the least squares technique with LSTM neural networks for time series analysis in decision-making processes. the research framework leverages the precise parameter estimation capabilities of least squares for establishing variable relationships, while utilizing LSTM's advanced pattern recognition to identify temporal dependencies in sequential data. through practical implementation, the methodology establishes quantitative relationships between sales performance and pricing strategies using least squares, while employing LSTM architecture for forecasting inventory replenishment needs. the proposed analytical framework demonstrates significant capability in identifying complex temporal patterns, offering empirical evidence for data-informed corporate decision-making. Experimental outcomes substantiate the methodology's efficacy, presenting innovative tools and insights for temporal data analysis applications. © 2025 the Authors. Published by Elsevier B.V.

关键词： Risk perception

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Enhancing Security with machine learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

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Survey on One-Shot learning for Image recognition using machine learning and Deep learning Techniques

Survey on One-Shot Learning for Image Recognition using Mach...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Ganesh Vitthal Kadam Ghanshyam Prasad Dubey Computer Science Amity School of Engineering & Technology Gwalior Maharajpura Gwalior Madhya Pradesh ASET-CSE Amity School of Engineering & Technology Maharajpura Gwalior Madhya Pradesh

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-shot learning restricting the number of data in each category is developed to attain enhanced results. Specifically, the traditional neural networks lead to over-fitting problems in training with a limited number of samples. In contrast, machine learning algorithms are capable of one-shot learning from limited samples like humans have the ability of oneshot learning. Hence, it is necessary to analyze one-shot object recognition techniques prevalent in image processing systems. this study will explore different one-shot learning techniques and methods from the state-of-the-art models and examine the merits and challenges of the approach with 25 literature papers. the effectiveness of the existing approaches will be evaluated by various techniques, datasets, and performance metrics are discussed in detail. At last, the paper discusses the potential future studies and challenges in achieving better detection accuracy.

关键词： Training Measurement Surveys Image recognition Accuracy Training data One shot learning Artificial neural networks Reliability Object recognition

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data mining and Network Pharmacology on Tibetan Medicine Prescriptions for the Prevention and Treatment of COVID-19 5

Data Mining and Network Pharmacology on Tibetan Medicine Pre...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Aray, Baht Zhang, Xubao Fu, Rao Sun, Mengliao Li, Zhiyong Huang, Xiulan School of Pharmacy Minzu University of China Beijing China Capital Normal University High School Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In this paper, the compatibility rules and drug combinations of Tibetan medicine prescriptions are explored, and the possible anti-epidemic mechanism is analyzed from the perspectives of biological network and signaling pathway, so as to provide reference for scientifically elaborating the potential value of ancient Tibetan medicine in preventing epidemic diseases. Association rules and clustering analysis are used for Tibetan drug mining. A total of 18 prescriptions involving 113 Tibetan medicinal materials were included, and 26 high-frequency Tibetan medicinal materials with the statistical frequency ≥3 are included, most of which are medicinal materials for clearing heat, detoxicating, eliminating plague. thirteen potential drug combinations are obtained through association rule analysis. the KMean clustering and hierarchical clustering were used for clustering analysis to obtain five drug clusters, and the "Bamusaeconcretiosilicea - Carthami Flos"combination was selected for network pharmacology research by comparing the two methods. After drug target and pathway analysis, "Bamusaeconcretiosilicea - Carthami Flos"through MAPK cascade, response to oxygen level, reactive oxygen species metabolism process, PI3K-Akt signaling pathway, NF- κ B signaling pathway, cytokine-cytokine Receptor interaction and calcium signaling pathways have certain feasibility for the treatment of immune disease fever in three aspects: immune response, inflammatory response, and oxidative stress. © 2022 IEEE.

关键词： Coronavirus

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Feature recognition of Natural Gamma-Ray Spectral data Based on K-L Transformation

Feature Recognition of Natural Gamma-Ray Spectral Data Based...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Daobing Han Qingxin Meng Hebei Geological University Shijiazhuang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the natural gamma-ray spectroscopy observation technique is an important nuclear geophysical detection method and is one of the main tools for solving problems related to Earth and environmental sciences. the traditional natural gamma-ray spectrum observation technique mainly focuses on the analysis and interpretation of a small number of parameters (U, th, K) and basically does not require feature compression processing, but there are certain limitations. Generally natural gamma ray spectra are measured in a very broad range, for displaying physical properties, geological bodies, geotechnical and other ray spectra, the amount of data is very large, especially for low-energy spectral data, very complex, and the need for ray spectra to be identified by the characteristics of the ray spectra. In this paper, firstly, we obtain the raw data output from the radionuclide instrument, and secondly, we use matlab software to sample the raw data in the range of 0–3 MeV, the ray spectrum interval is more flexible, and the obtained data can be used in the form of ‘uniform sampling- mean output’, and then the output results are characterised by the K-L transform to identify the features of the output results, and extract those values with the largest eigenvalues to characterise its statistical features. the obtained data can be in the form of ‘uniform sampling-mean output’, and the ray spectrum is characterised by a number of output results obtained above, and finally, the K-L transform is used to identify the features of the output results, and the values with the largest eigenvalues are extracted to characterise its statistical features. thus, it can analyse the difference between the rock and soil more accurately, and provide more accurate and faster methods and techniques for geological exploration, resource exploration, environmental monitoring and other fields.

关键词： Accuracy Gamma-rays Transforms Soil Feature extraction Rocks Eigenvalues and eigenfunctions Object recognition data mining Geologic measurements

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Feature-Based machine learning for Predicting Resistances in Printed Electronics 5

Feature-Based Machine Learning for Predicting Resistances in...

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5th IEEE international conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Ivy, Landon Xie, Yutong Lobo, theo Gund, Ved Davaji, Benyamin Garud, Meera Doerschuk, Peter Lal, Amit School of Electrical and Computer Engineering Cornell University SonicMEMS Lab Ithaca United States School of Electrical and Computer Engineering Cornell University Ithaca United States Electrical and Computer Engineering Northeastern University Aims Lab Boston United States

ISBN: (纸本)9781665457330

Making repeatable devices is a significant obstacle to the widespread adoption of printed electronics. Due to the complexity and sensitivity of inkjet or extrusion printing physics, it is difficult to use simple models to make predictions that match measured devices. To overcome this challenge, we developed an AI-based approach to predict experimental values. We employed two different printers to create a test pattern with various controllable parameters, and an automated prober was developed to measure wire resistances across a large printed area. Image processing was used on scanned PCB images to extract texture data from the printed structures. A machine learning model was developed to predict the test structures' resistance from only their geometric parameters and texture data. In our best single-board experiments, the root mean square error between the predicted and measured values was within 0.01 Ω. this model can be used to design structures that achieve desired resistances by actively controlling the print texture. © 2023 IEEE.

关键词： Mean square error

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Cricket Shot Classification from Video Using Body Pose Landmarks 5

Cricket Shot Classification from Video Using Body Pose Landm...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Rao, Chava Govardhana Varma, N. Sai Harshith Kashish, K.C. Mazumder, Priyangshu Ramasamy, Prema Pes University Dept of Computer Science Bengaluru India

ISBN: (纸本)9798350356236

the primary focus of this study is to use deep learning techniques to classify cricket shots. data acquisition begins by gathering video clips of different cricket shots and manually annotating them. To address imbalance in class, generated synthetic examples are used to ensure each shot type has equal representation. Advanced techniques for handling missing data points using combined object detection and 3D landmark extraction to track the batsman's move-ments are employed for efficiency. To enhance accuracy of the analysis, complex methods are used to deal with missing data. By integrating object detection methods with 3D land-mark extraction, effective tracking of batsman movements is captured. Tests with multiple machine learning models such as simple neural networks and more complicated models which could capture movement sequence over time are tested on the data. the most successful results were achieved with systems that could process video frames in both forward and reverse directions, leading to a more precise identification of cricket shots. this research illustrates the potential of machine learning techniques and how advanced models can be used to analyze and classify sports activities in video data. We contribute to the growing community and knowledge base of automated sports analytics, paving the way for future innovations in real-time action recognition and commentary generation. © 2025 IEEE.

关键词： Deep learning

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AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue Operations

AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue O...

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Innovative Trends in Information Technology (ICITIIT), international conference on

作者： K Rashida Farsath K Jitha V.K Mohammed Marwan A Muhammed Ali Jouhar K.P Muhammed Farseen K.A Musrifa Department of Computer Science & Engineering MEA Engineering College

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

this paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with state-of-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. the system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of things (IoT) sensors, and sophisticated video analytics. machine learning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. the system’s AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. these tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, em-emphasizing responsible data handling practices, and robust security measures to ensure the system’s integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.

关键词： Image recognition machine learning algorithms Heuristic algorithms Autonomous aerial vehicles Emergency services Real-time systems Time factors

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