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data Fusion and Processing of smart Sensors in Mechatronics Devices

Data Fusion and Processing of Smart Sensors in Mechatronics ...

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data analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Qiang Yu Changjian Song Zhengyu Xia Chongxin Wang Chenglin Wang Shandong Jianzhu University Jinan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the rapid development of mechatronics equipment, smart sensors play an increasingly important role in various applications. The diversity and complexity of smart sensors make data processing and fusion a key link in improving system performance. This paper studies the application and performance of embedded smart sensor networks in smart electronic systems, analyzes the fundamentals, working principles and communication protocols of embedded smart sensor networks, reveals its key technologies in smart electronic systems, focuses on its application scenarios and advantages in smart electronic systems, and demonstrates its role in improving system intelligence, optimizing performance, and reducing energy consumption through case studies. Including small molecule semiconductors, polymer semiconductors and conductive polymers and other organic sensing materials, as well as chemical sensors, temperature sensors, optical sensors and mechanical sensors and other organic intelligent sensor devices of cutting-edge applications, the results show that the effective application of data fusion and processing methods can significantly improve the level of intelligent mechatronics equipment and operational efficiency, for the further development of the relevant fields to provide a theoretical basis and practical guidance. Theoretical foundation and practical guidance for the further development of related fields.

关键词： Temperature sensors Technological innovation Energy consumption Mechatronics System performance data integration Solids Robustness Polymers intelligent sensors

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate 27

Short-term Portfolio Optimization using Doubly Regularized E...

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27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Guan, Quanlong He, Jinneng Lai, Zhao-Rong Hou, Yuyu Jiang, Quming Chen, Ziliang Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Chutian Dragon Co Ltd Dongguan Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Zhuhai Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. This paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. The approach uniquely combines l(1) and l(2)-regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. This algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Short-term portfolio optimization machine learning in finance application FinTech exponential growth rate approach

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Traffic Incident Detection Algorithm based on Deep Learning: A Statistical Optimization Approach 3

Traffic Incident Detection Algorithm based on Deep Learning:...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jing, Zhao Heilongjiang Institute of Technology College of Automobile and Traffic Engineering Harbin150050 China

ISBN: (纸本)9798331509675

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m-c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data. © 2025 IEEE.

关键词： intelligent systems

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Exploring Human Resource Management Intelligence Practices Using Machine Learning Models

Exploring Human Resource Management Intelligence Practices U...

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2023 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2023

作者： Selvam, S. Stalin Panneer Paramasivan, C. Dinesh, N. Mukherjee, Sayantan Mekala, S. Shajahan, Umaya Salma Hindustan Institution of Technology and Science Department of Commerce Chennai India PG & Research Department of Commerce Tiruchirappalli620023 India Jain Deemed to Be University OB & HRM Area CMS B School. Faculty of Management Studies India Manipal University Jaipur India KPR College of Arts Science and Research School of Management Arasur India Sona College of Technology Department of Management Studies India

ISBN: (纸本)9798350319200

Human Resource Management Intelligence (HRMI) and sophisticated data analytics are converging to bring about a revolutionary change in HR procedures in today's information age. This study takes an inquisitive trip to the interface between human resources and machine learning, investigating the empirical discoveries and practical applications that reframe HRMI. We explore the complex relationship between HR choices and outcomes like performance and turnover using machine learning models. A convincing narrative is articulated by the study's authors: machine learning methods, ranging from linear regression to random forests, lead a data-driven HR revolution. The results highlight the value of strategic investment in employee development, provide light on the complex dynamics of employee happiness and retention, and explore the ethical elements of HRMI in an age of heightened data scrutiny. Real-world examples illustrate the concepts and provide actionable guidance for HR experts who want to make more informed decisions with the help of data. This study also supports a more comprehensive method of HRMI, one that integrates empirical data analysis with theoretical frameworks. The convergence of AI, ethical AI, and the changing nature of remote labor opens up exciting possibilities for HRMI in the future. This waypoints towards an HRMI field that is not just reactive but also prescient, an area where information serves as the compass leading businesses to improved financial health, more diversity of thought, and a more promising future in the workplace. The research paper reveals the future of HRMI, which is a data-driven frontier where HR experts transform into architects of better work environments, where strategic insights, organizational success, and the well-being of the workforce are all harmoniously balanced. © 2023 IEEE.

关键词： Attrition Factors data-Driven Decision-Making data-Driven HR Employee Performance Employee Satisfaction HR Management Intelligence (HRMI) Machine Learning Models Workforce Optimization

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Proceedings - 6th international conference on computing Methodologies and Communication, ICCMC 2022

Proceedings - 6th International Conference on Computing Meth...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

ISBN: (纸本)9781665410281

The proceedings contain 298 papers. The topics discussed include: a novel framework for multi source based cloud forensic;a comparative analysis of cloudlet provisioning in mobile cloud computing environment;smart home monitoring system and prediction of power consumption;simulation of pick and place robotic arm using coppeliasim;effective metrics modeling of big data technology in electric power information security;microcontroller and mobile app based garments environment monitoring system for workers;study on wind energy analytics and its algorithms;tracking and monitoring of medical equipments using uwb for smart healthcare;use of nanotechnology sensors for sustainable agriculture;cloud storage and authenticated access for intelligent medical system;a survey on electronic health records (EHRS): challenges and solutions;and intelligent application of virtual reality technology in the design of elderly service station.

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Leveraging Optimization Techniques for Effective Arabic Query Expansion

Leveraging Optimization Techniques for Effective Arabic Que...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Allahim, Azzah Cherif, Asma College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia IT Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia The Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819614820

The primary goal of query expansion is to gather terms that are closely associated with the original query terms. To accomplish this goal, similarity measurements are used to evaluate the similarity between the query terms and the collected terms. Unfortunately, existing Arabic query expansion frameworks focus on expanding the query words individually. This approach can affect the expansion process for a complex language such as Arabic. In this paper, we investigate the effectiveness of applying an AI optimization algorithm to Arabic query expansion. The study aims to produce the optimal combination of terms by measuring the similarity of the collected terms against all query terms. We tested two intelligent swarm algorithms: the Cuckoo Search algorithm and Harmony search algorithm. Both algorithms were successful in enhancing the model’s precision and recall score with Cuckoo Search algorithm achieving the best performance of (97.5%) precision and (93%) recall. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Design and Development of Bayesian Optimization Algorithms for Big data Classification Based on MapReduce Framework 1

引用

international conference on intelligent and smart computing in data analytics, iscda 2020

作者： Banchhor, Chitrakant Srinivasu, N. School of Computer Engineering and Technology Dr. VishwanathKarad MIT World Peace University Pune Maharashtra India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram AP India

ISBN: (数字)9789813361768

ISBN: (纸本)9789813361751

The handling of big data refers efficient management of processing and storage requirements of very large volume of structured and an unstructured data of association. The basic approach for big data classification using naïve Bayes classifier is extended with correlation among the attributes so that it becomes a dependent hypothesis, and it is named as correlative naïve Bayes classifier (CNB). The optimization algorithms such as cuckoo search and grey wolf optimization are integrated with the correlative naïve Bayes classifier, and significant performance improvement is achieved. This model is called as cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). The further performance improvements are achieved by incorporating fuzzy theory termed as fuzzy correlative naïve Bayes classifier (FCNB) and holoentropy theory termed as Holoentropy correlative naïve Bayes classifier (HCNB), respectively. FCNB and HCNB classifiers are comparatively analyzed with CNB and CGCNB and achieved noticeable performance by analyzing with accuracy, sensitivity and specificity analysis. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Advanced Neural Network Models for Predictive analytics and Healthcare Management in Neurodegenerative Diseases

Advanced Neural Network Models for Predictive Analytics and ...

引用

Advancements in smart, Secure and intelligent computing (ASSIC), international conference on

作者： Seeniappan Kaliappan M.D.Raj Kamal V. Balaji Gotte Ranjith Kumar Department of Mechatronics Engineering KCG College of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Velammal Institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamil Nadu India School of Computer Science & Artificial Intelligence SR University Warangal Telangana

The proposed method, HybridNet-NDM, integrates three vital algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs)-in a synergistic manner for applications in predictive analytics and healthcare management of neurodegenerative diseases. CNNs play a role in extracting biomarkers from medical imaging data, identifying complex patterns crucial for pinpointing neurodegenerative diseases. Meanwhile, LSTMs take charge of modeling the temporal dimensions of patient data, utilizing longitudinal records to predict disease progression. GCNs contribute by analyzing brain connectivity patterns, shedding light on disease development through an examination of brain structure and function. This hybrid neural network marries the strengths of these algorithms, fusing their unique features to attain unparalleled predictive accuracy. An attention mechanism is incorporated to further hone the feature fusion process, ensuring precision in predictions. Demonstrating improved performance across a range of metrics, the optimized model stands out in its effectiveness for predictive analytics and healthcare management in the realm of neurodegenerative diseases.

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AI-based traffic counting: A Case Study in Vietnam

AI-based traffic counting: A Case Study in Vietnam

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international conference on Advanced computing and analytics (ACOMPA)

作者： Nhu Phuc Nguyen Hoang Minh Nguyen Hoang-Loc La Truong Thi Tran Thi Ngoc Hieu Duong Thanh Sach Le Duy Lai Nguyen Le Nam Thoai Ho Chi Minh City Univ Technol HCMUT Ho Chi Minh City Vietnam Hoa Sen Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665461719

Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%.

关键词： deep learning traffic counting intelligent transportation system CCTV

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"I-Care" - Big-data analytics for intelligent Systems 8

"I-Care" - Big-data Analytics for Intelligent Systems

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8th international conference on smart computing and Communications (ICSCC)

作者： Singh, Paras Nath CMR Inst Technol Dept CSE Bengaluru 560037 India

ISBN: (纸本)9781728196879

A very novel predicament for quantitative data science has been generated by the abundance of large, well-cured data sets in biological and social science, coupled with an extraordinary increase in computational ability. This is the possibility of sophisticated studies combined with remedial understanding. analytics for intelligent systems should cover architecture of hardware platforms and application of software methods, technique and tools. It is anticipated that adapting dynamic memory information, processing parametric values of large data sheets with optimization, would be faster. The field of Big-data analytics under recent trends of data Science studies various means of pre-processing, analyzing and filtering from huge and semi-structured data sets from different sources which are complex to be handled by traditional data processing systems. In addition to extracting and aggregating data from various main performance measures, this proposal also forecasts potential values for these KPIs (Key Performance Indicators) and alerts them when unfavorable values are about to occur. As AI and ML are implemented through different platforms and sectors including chat-bots, robotics, social media, healthcare, self-driven automobile and space exploration, large companies are investing in these fields, and the demand for ML and AI experts is growing accordingly. Python is becoming the most popular language for AI (Artificial Intelligence and Machine Learning) due to its rich supported tools. This proposed applications "I-Care" (intelligent Care) provide recommendations to improve Quality of Service of Big-data analytics. So, the proposed paper examines the methodology and requirements, architecture, modeling and analytics with implementation and describes the architectural design and the results obtained by the pilot application using Python and its powerful tools like Pandas and Scikit-Learn.

关键词： Artificial Intelligence Big data analytics Healthcare Machine Learning intelligent Systems Python

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