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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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A multi-source heterogeneous data fusion method based on time alignment and federated learning for the "dual carbon" digital intelligence monitoring center 4

A multi-source heterogeneous data fusion method based on tim...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Yan, Zhenhua Han, Hongwei Zhu, Dongge Liu, Jia Ma, Rui Wang, Jianfu Liu, Chaochao Ningxia Electric Power Research Institute Yinchuan750002 China State Grid Ningxia Electric Power Co. Ltd. Yinchuan750001 China State Grid Xintong Yili Technology Co. Ltd. Xiamen361001 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Due to the complex composition of the "Dual Carbon" digital intelligence monitoring data, the error in using it to analyze carbon emission status is large. therefore, a multi-source heterogeneous "Dual Carbon" digital intelligence monitoring center based on time registration and federated learning is proposed. First, in terms of "double carbon" historical data accumulation, the composition of static basic data and carbon emission historical data is analyzed;in terms of "double carbon" real-time monitoring data, the composition of IoT sensing data and dynamic business data is analyzed. After adding timestamps to each data type, align the data to a unified timeline, build the "Double Carbon" digital intelligence monitoring center multi-source heterogeneous data high-order tensor in the federated learning framework, and perform Tucker decomposition and iterate to outputs the converged quadratic linear correlation function. In the test results, the method has the smallest difference between the analysis results of carbon emissions in region A and the actual situation. the corresponding error is always within 0.5*107 t, and the maximum error is only 0.46*107 t. © 2025 SPIE.

关键词： Federated learning

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

4th International Conference on Advanced Computational and C...

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

ISBN: (纸本)9789819942831

the proceedings contain 44 papers. the special focus in this conference is on international conference on Advanced Computational and Communication Paradigms. the topics include: Time-Related Natural Language Query Handling to Extract Autism Spectrum Disorder Information from Temporal Database;emotion-Based Music Recommendation;a Comprehensive Study on Natural Language Processing, It’s Techniques and Advancements in Nepali Language;quality Evaluation of Enterprise Architecture Frameworks;natural Language Query Processing System to Extract Autism Spectrum Disorder Information from Database;spiking Neural Network in computer Vision: Techniques, Tools and Trends;a Quantitative Study on the FaceNet System;Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of machine learning Classifiers;comparative Analysis of Minimax Algorithm with Alpha–Beta Pruning Optimization for Chess Engine;designing of Advanced machine learning-Based Predictive Model for Maternal Mortality;a Modified Microwave Based System Design for Early-Stage Breast Cancer Detection;research Review and Literature Perception Towards Medicinal Plants Classification Using Deep learning Techniques;features Fusion-Based Gait Recognition with Covariate Conditions;computational Modelling of Glucocerebrosidase Signalling Pathways in Parkinson’s Disease;COVID-19 Lung Patch Segmentation Using COVSeg-NET;research Study on Leukemia Detection Using Deep learning Techniques;automated Classification of Diabetic Retinopathy Using Deep learning Architecture;Deep learning-Based Tumor Segmentation from CT Images;DIVS: A Real-Time Video Stabilization Framework for Consumer Camera;Saliency-Driven ‘Evidence CNN’ for Fine-Grained Recognition of Twins;multi-criteria-Based Page Ranking Using Metaheuristic Swarm Optimization;IoT-Based Landslide Monitoring System Using LiDAR Sensors: A Review;Change Detection in LULC for Durgapur: An Efficient GIS-Based Framework;morphological Operation-Based Backgrou

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Optimization of Pose Recognition Algorithms in Smart Wearable Devices 4

Optimization of Pose Recognition Algorithms in Smart Wearabl...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Luo, Mingze Huang, Jueqiao Zheng, Bowen Lin, Guofei Zhao, Tianze Cao, Kangjie School of Electronic Science and Engineering Southeast University Jiangsu Nanjing China Minzu University of China Department of Computer Science and Technology China Fok Ying Tung Graduate School Hong Kong University of Science and Technology Hong Kong School of Accounting and Finance Jiageng College Xiamen University Fujian Zhangzhou China Minzu University of China Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350343113

With the surge in popularity of smart wearable devices, the necessity for pose recognition has become paramount, especially in fields like motion analysis, health monitoring, and virtual reality. Yet, achieving efficient and precise pose recognition on devices with limited resources poses significant hurdles. Addressing this, we introduce an innovative optimization method for pose recognition algorithms, harnessing the power of the C++ compiler. Our approach uniquely integrates traditional machine learning paradigms with model simplification techniques. Furthermore, by exploiting C++ functionalities such as inline assembly, template metaprogramming, and compile-time optimization, we craft an optimization tailored explicitly for wearable hardware. Evaluations conducted on various devices testify to our method's superiority, marking a 10% surge in accuracy, 30% boost in efficiency, and a 15% dip in energy consumption relative to contemporary methods. Apart from achieving stellar accuracy and real-time performance, our method distinguishes itself in resource efficiency and scalability. this article offers an in-depth exploration of our design philosophies, optimization tactics, experimentation methodologies, and seminal discoveries, signposting the future trajectory of pose recognition in smart wearables and highlighting its commercial potential. © 2023 IEEE.

关键词： machine learning

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Research on Foreign Language Teaching Effect Prediction Model Based on Deep learning 4

Research on Foreign Language Teaching Effect Prediction Mode...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Ren, Aima Xi'an Eurasia University Xi'an China

ISBN: (纸本)9798331528676

this paper proposes a predictive model for foreign language teaching effectiveness based on deep learning. Firstly, the paper introduces the application of label transfer technology in cross-language environment, and realizes the efficient conversion of labels between different languages through deep learning algorithm, which lays a foundation for the subsequent entity extraction and translation work. through the combination of shared presentation layer and task-specific layer, the optimization of multiple tasks such as label migration, cross-language entity extraction and self-learning dictionary translation is realized. We use deep neural networks to automatically identify and extract key entity information from multiple languages. At the same time, the self-learning dictionary translation technology is introduced to improve the accuracy and efficiency of translation through continuous learning and optimization. A prediction model for foreign language teaching effect is constructed and its validity and accuracy are verified by many experimental data. the experimental results show that the model can effectively predict the effect of foreign language teaching and provide an important reference for teachers and students. ©2024 IEEE.

关键词： Prediction models

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Climate Time Series Prediction with Deep learning and LSTM 4

Climate Time Series Prediction with Deep Learning and LSTM

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sha, Ravi Guha, Tapas Tata Consultancy Services Department of Computer Systems MelbourneVIC3004 Australia University of Engineering and Management Department of Computer Science and Technology West Bengal Kolkata700160 India

ISBN: (纸本)9798350300888

Climate time series prediction is a critical task for understanding and forecasting complex climate patterns and trends. Traditional methods have shown limited accuracy and struggle to capture the intricacies of climate data. In this study, a deep learning-based approach is proposed to improve the accuracy of climate time series prediction and address the challenges posed by previous works. the main objective of this work is to train Deep learning models from collected data to generate climate predictors. For this public data on climate indicators is collected;Deep learning models are trained;climate forecasts from the models trained from the collected data is carried out. the innovation in proposed approach lies in the integration of a novel temporal attention mechanism that allows the model to focus on relevant time steps, improving interpretability and prediction accuracy. Furthermore, transfer learning is adopted with pretrained models to capitalize on their knowledge and accelerate convergence on climate-specific tasks. the comparison with most recent related work showcases the superiority of the proposed approach in capturing complex climate patterns and delivering precise long-term forecasts. the model's interpretability enables researchers to gain valuable insights into the underlying climate dynamics, facilitating informed decision-making and policy planning. © 2023 IEEE.

关键词： Long short-term memory

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An Analysis of Knowledge Management Changes through Artificial Intelligence with Probst's Model 4

An Analysis of Knowledge Management Changes Through Artifici...

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4th international conference on AI Research, ICAIR 2024

作者： Toptas, Burak Nuremberg University of Applied Sciences Nuremberg Germany

ISBN: (纸本)9798331309466

In recent years, the integration of Artificial Intelligence (AI) into Knowledge Management (KM) has led to transformational changes. these changes have significantly enhanced traditional KM processes. To identify how AI technologies improve and reshape knowledge processes, this study conducted a systematic literature review. the review identified AI technologies suitable for each of Probst's building blocks, which outline the eight central KM processes. the research reveals a wide range of AI technologies, including machine learning, natural language processing and chatbots such as ChatGPT. these technologies can be applied in different domains and introduce innovative approaches to improve KM processes. Based on the AI technologies analysed, this study proposes a four-stage model to support the documentation and application of best practices and lessons learned. the model is designed to enhance the knowledge development process and aims to document and secure key project developments in the long term. A further objective was to analyse which KM process is most affected by chatbots. the findings indicate that chatbots have the potential to transform the use of knowledge in organisations. they act as facilitators by breaking down existing barriers, foster an open culture of knowledge sharing, streamline workflows and increase the accessibility of knowledge. the study also examines the broader changes that AI will bring to KM and forecasts the sixth generation of KM. It draws on Bencsik's (2021) evolutionary and revolutionary perspectives that specifically forecast this next generation. the study shows that AI not only enhances existing KM processes but also has the potential to fundamentally disrupt traditional methods and approaches. these findings underline the need for future research to explore the effective integration and scalability of AI technologies in real-world KM environments. this will help ensure that their long term impact and potential benefits are fu

关键词： Adversarial machine learning

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Android Malware Detection and Classification using Stacked machine learning 4

Android Malware Detection and Classification using Stacked M...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Nawshin, Faria Gad, Radwa Unal, Devrim Suganthan, Ponnuthurai Nagaratnam KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Department of Computer Science & Engineering College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9781839539954

the widespread use of Android smartphones in daily life can be attributed to the extensive prevalence stemming from the Android OS and the availability of open-source applications. People have become accustomed to performing various tasks, such as online banking transactions, scheduling appointments, attending virtual meetings and classes, and online shopping, on their mobile devices instead of computers. However, the significant use of mobile phones also presents opportunities for attackers to access sensitive and confidential information without the user’s knowledge. Intruders often use malicious applications found on the Google Play Store and other third-party app markets. the increased volume of Android malware in recent years has severely threatened human privacy and security. In this paper, we present a novel solution that utilizes a stacking ensemble machine learning approach to detect whether an application is malware or benign, which achieved 98% accuracy and F1-Score, and to identify the malware category with 96% accuracy and F1-Score. We conducted the experiment on the CCCS-CIC-AndMal-2020 dataset, which comprises 27,125 samples. Static features such as permissions, meta-data, and intents were analyzed, and the performance of standard machine learning algorithms was compared to our proposed model. Our results demonstrate that the stacked ensemble model shows promising results for malware detection (binary classification) and malware type classification (multi-class classification). © 2024 the Author(s).

关键词： Android malware

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Non-negative Tensor Representation and Unsupervised Classification of Object Pose in Continuous Image Sequences 4th

Non-negative Tensor Representation and Unsupervised Classifi...

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4th international conference on Artificial Intelligence Logic and applications

作者： Ying, Zhihao Zhao, Yang Pei, Jihong Yang, Xuan Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819603534;9789819603541

the analysis of video data in machine learning classification tasks has become a significant topic in both research and application areas. the accurate classification of video data by frame sets, particularly when the content contains objects that are in a state of dynamic change, represents a significant and complex undertaking. With regard to the temporal phase of video, the thesis proposes an unsupervised classification method based on non-negative tensor factorization (NTF). In order to transform the video data into low-dimensional data that can be classified by unsupervised clustering, the non-negative tensor factorization method is used to reduce the dimension of the input tensor, resulting in the generation of a non-negative rank-1 tensor combination. Following dimensionality reduction, the extracted features are divided into clusters through Mean Shift, thereby facilitating classification and recognition of video sequences. Experiments on the Coil-100 dataset of item image sequences at different angles and other dynamic pose-changing videos demonstrate the effectiveness of the method.

关键词： Video timing division Non-negative tensor factorization Unsupervised learning

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Real-Time Face Mask Detection in Video Streams Using Deep learning Technique 4

Real-Time Face Mask Detection in Video Streams Using Deep Le...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350300888

Face masks are necessary during the worldwide pandemic to prevent the transmission of infectious diseases. this research proposes a deep learning-based system for detecting face masks in live video feeds in real-time. the system's goal is to automatically determine if people on a video broadcast are hiding their faces behind masks. To do this, a deep convolutional neural network architecture is sued and is trained on a huge dataset of annotated photos that include both masked and unmasked faces. the existing systems struggle to handle large-scale deployment and real-time processing of video streams for face mask detection. the network was built to learn features that reliably identify masks or no masks on faces. the transfer learning method is employed for fine-tuning in order to enhance the network's ability to generalize. Further, this study employs a powerful detection pipeline that uses hardware acceleration and parallel processing approaches to deal with the real-time nature of video streams. Since the channel processes video frames in real-time, it can be used in places where fast detection is critical, such as hospitals, airports, and other public buildings. To measure the proposed system's efficacy, large trials are run over various video datasets, each with its unique combination of circumstances and camera angles. the findings show that the proposed Convolutional neural network method is effective, comparing with Dataset-1 and Dataset-2 with real-time processing speed and good accuracy. Solution is accurate 95% to 99% and efficient than the current techniques. © 2023 IEEE.

关键词： COVID-19

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the Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan 4

The Role of IOT in Creating SC'S through Ultra Fast Updation...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Hariharasudhan, S. Subasree, S. Bhuvaneswari, M. Victor, Mekvin Kumar, Rintu Kumar, K. Sambath Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Biomedical Engineering TN Coimbatore India Christ University School of Business and Management Bangalore India Ies University Department of Mech Engg Ies Institute of Technology and Management M.P. Bhopal462044 India Chennai Tamil Nadu Avadi India

ISBN: (纸本)9798350360165

the idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of the circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. this paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research. © 2024 IEEE.

关键词： AI-powered systems blockchain data management healthcare IoT machine learning smart cities smart technology urban efficiency wireless sensor networks

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