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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Optimizing Software Development through data Access Speed using Object-Relational Mapping (ORM) on Credit Risk Application 3

Optimizing Software Development through Data Access Speed us...

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3rd International Conference on Smart Cities, Automation and intelligent computing systems, ICON-SONICS 2023

作者： Sierra, Evelyn Yuhana, Umi Lali Informatics Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (纸本)9781509062805

This study presents an examination of the implications of integrating Object-Relational Mapping (ORM) in credit risk software development. ORM is a software layer that allows developers to work with databases using object-oriented code and concepts, rather than writing raw SQL queries. It provides a set of classes and methods that enable the creation, retrieval, update, and deletion of data in a database using familiar programming constructs like classes, objects, and methods. For a case study, we use credit risk software development with Indonesia macroeconomic data. Two distinct experiments were conducted to assess the advantages and trade-offs associated with ORM implementation, both during mid-project and at the project's inception. The findings reveal intriguing insights into the impact of ORM on code execution time, security, and code maintainability. The study concludes that the decision to implement ORM hinges on project-specific needs and the state of the database structure. While ORM may pose minor performance trade-offs, its long-term advantages in code quality, security, and scalability underscore its value. Thus, the timing of ORM integration should align with project objectives to strike a balance between performance optimization and code quality. © 2023 ieee.

关键词： Software design

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Development of intelligent Sensor Based System for Monitoring and Control of Nutrient Supply in Root Crops 3

Development of Intelligent Sensor Based System for Monitorin...

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3rd International Conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Mahesh, N. Prabhu, K. Baluprithviraj, K.N. Sathiya Seelan, L. Sharanya, T. Shedhu, M. Kongu Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350306989

In today's era, Agriculture plays an important role in the economic progress of any country. The soil fertility and moisture level will have a major impact on the crop yield. Normally, fertilizers are suggested based on the nutrients present in the soil. Manually measuring the soil nutrients in the soil is a time consuming. Usually most of the farmers avoid soil testing in the laboratory and encourages to promote the same crop in the field continuously, so as a result the fertility of the soil will be lost. For the benefits of farmers, a precise agriculture method system has been introduced with the help of wireless sensor networks, the benefit of this system is remotely monitoring the soil nutrients, which are available in the soil, that is the three main nutrients, nitrogen, phosphorus and potassium which contributes to major nutrients of the soil and other parameters namely moisture level of the soil, pH of soil, humidity of soil, sunlight, temperature and TDS. pH level accounts for the stability of the growth of the plant. TDS value determines the purity of water supplied to the root crops. Sunlight is an essential parameter in the growth of the plant which is monitored in this system. This data is copied and transmitted to the cloud server so the noted values of the nutrients are displayed in the mobile application server. The proposed Internet of things (IoT) software system has the intelligence to suggest the amount of water and fertilizer that will enhance the soil's quality and guarantee the best possible crop development. © 2023 ieee.

关键词： Nutrients

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A Review of State-of-Art Methods for Passive Driver Fatigue Detection: Progress and Prospect 3

A Review of State-of-Art Methods for Passive Driver Fatigue ...

引用

3rd International Conference on Artificial Intelligence and Autonomous Robot systems, AIARS 2024

作者： Wang, Dong Wu, Pianhui Guangdong Baiyun University Big Data and Computing Institute Guangzhou China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangzhou China

ISBN: (纸本)9798350376173

In recent years, there has been a noticeable rise in traffic accidents attributed to driver fatigue, sparking increased interest among researchers. Current research on fatigue driving is broadly categorized into two approaches: active detection and passive detection. Passive detection involves the use of technological tools to unobtrusively monitor physical signs of fatigue. This approach is favored over active detection for several reasons, including its higher precision, enhanced dependability, and independence from the driver's personal judgment. Passive detection has become the predominant focus in the study of driver fatigue monitoring. This paper presents a comprehensive review of the latest advancements in passive driver fatigue detection technologies over the last few years. It highlights key passive detection techniques, thoughtfully assesses the merits and drawbacks of various fatigue monitoring strategies, and concludes by suggesting potential areas for future research in fatigue detection. © 2024 ieee.

关键词： Fatigue of materials

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th International Conference on Soft computing and intelligent systems and 25th International Symposium on advanced intelligent systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 ieee.

关键词： Cost reduction

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Non-contact sensor-based human fall detection method 3

Non-contact sensor-based human fall detection method

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3rd International Conference on Biomedical and intelligent systems, IC-BIS 2024

作者： Ruan, Tingting Yan, Tianxing Song, Yongkun Zhang, Ke Liu, Xian Changsha Health Vlocational College ChangSha41000 China Changsha University of Science & Technology Changsha410000 China

ISBN: (纸本)9781510681279

With the exacerbation of global population aging, the issue of falls among the elderly is increasingly drawing attention from various sectors of society. Consequently, the development of an effective human fall detection system is crucial for timely identification of fall incidents and provision of emergency assistance. This paper proposes a fall detection method based on non-contact sensors, aiming to achieve real-time monitoring and accurate identification of fall events through advanced sensor technology and data processing algorithms. Initially, this study employs low-frequency ultra-wideband radar (UWB) as the primary tool for data acquisition, followed by the utilization of deep learning techniques to extract key dynamic features from radar data, thus providing a reliable foundation for fall event identification. Furthermore, this paper designs and implements a fall recognition model based on Convolutional Neural Networks (CNN), which, through training on a large amount of radar image data, learns to distinguish patterns between normal activities and falling behavior. The results demonstrate that the proposed method can effectively detect falls under various common daily activities, with a recognition rate as high as 99.338%. © 2024 SPIE.

关键词： data acquisition

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intelligent Monitoring and Scheduling of Real-time Dynamic data Based on Digital Twin 3

Intelligent Monitoring and Scheduling of Real-time Dynamic D...

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3rd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Zhao, Bo Dang, Qian Zhang, Qin Du, Chunhui Li, Chunliang Information Telecommunication Company State Grid Gansu Electric Power Company Lanzhou China

ISBN: (纸本)9798350302356

The Digital Twin (DT) is a key concept in the industrial Internet of Things (IoT) that creates virtual representations of physical objects through software definition. It maps the status, features, and evolution of physical entities in the digital space, providing a practical solution for capturing the dynamic and complex nature of industrial environments. This paper presents a proposed DT network architecture for managing devices in an information room. By integrating DT and edge computing, we establish an effective mapping between IoT equipment and network systems. Furthermore, we construct an edge twin network framework based on federated learning and introduce anomaly recognition models to make the twin network have predictive capability for abnormal data. Meanwhile, optimal strategy of communication resource allocation based on deep neural network model is determined to reduce the transmission delay, complete the prediction of the whole life cycle of the device, and realize real-time dynamic intelligent monitoring and scheduling. © 2023 ieee.

关键词： Internet of things

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Monte Carlo-based Measurement Uncertainty Propagation on a Virtual Sensor for Real- Time Human Balance Exergame Analysis 3

Monte Carlo-based Measurement Uncertainty Propagation on a V...

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3rd ieee International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Giulietti, Nicola Fabiocchi, Davide Giberti, Hermes Carnevale, Marco Università degli studi di Pavia Dip. Ingegneria Industriale e dell'Informazione Pavia Italy

ISBN: (纸本)9798350378009

Nowadays, increasingly advanced Artificial Intelligence techniques are progressively being integrated with design methodologies. The use of advanced inferential models trained on real-time data is making it possible to analyze the dynamic behaviors of very complex systems simply by observing them through vision-based measurement techniques, offering an innovative approach to indirect measurement of forces at play without the need for additional bulky and difficult-to-install sensors. However, it is not always possible to estimate the reliability of these systems because they depend on variables that are poorly controlled and difficult to assess. In addition, the assessment of measurement uncertainty is critical for the adoption of data-driven artificial intelligence models in commercial products and services. This paper explores the possibility of using cameras for the development of a virtual sensor for the dynamic analysis of human balance in a balance platform system. The developed virtual sensor is characterized by estimating the propagation of the measurement uncertainty through the use of the Monte Carlo method. The results obtained make it possible to evaluate the metrological performance of the proposed method. © 2024 ieee.

关键词： intelligent systems

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Advancements in Multiple Sclerosis Disease Classification Through Machine Learning 3

Advancements in Multiple Sclerosis Disease Classification Th...

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3rd International Conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Pandya, Darshanaben D. Degadwala, Sheshang Vyas, Dhairya Solanki, Vishal Narendrakumar Sureshbhai, Sharma Vishalkumar Patel, Hiteshkumar Gunvantbhai Gujarat Visnagar India Sigma University Dept. of Comm. Engg. Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gujarat Himatnagar India Gujrat Visnagar India Quality Engineering - Data Tiger Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350306989

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning. © 2023 ieee.

关键词： Support vector machines

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Design of Multi-Source data Collection Cloud Platform for Transmission Lines Integrating Technology 3

Design of Multi-Source Data Collection Cloud Platform for Tr...

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3rd ieee International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhu, Yifeng Tian, Lin Liu, Chengyu Cui, Jian Fang, Ming China Southern Power Grid Co. Ltd. EHV Transmission Company Guangdong Guangzhou China Guiyang Bureau of China Southern Power Grid Co. Ltd. EHV Transmission Company Guizhou Guiyang China

ISBN: (纸本)9798350317022

As power systems continue to evolve, aggregation technology has become a mainstream solution. This article aims to provide a detailed analysis and study of data acquisition issues in centralized transmission lines. In the aggregation of transmission lines, this article adopts a distributed computing service model, which is to set up a centralized municipal area network through the network to realize the monitoring and management of load demand and power supply reliability at the bureau and county level. This article combines smart terminals with traditional networks to meet users' different application functional requirements, and uses the characteristics of the Internet to provide a data collection platform with strong realtime performance, high reliability, and large maintenance and management capabilities. Finally, this article tests the performance of the platform. The test results show that the system receives data quickly and processes data quickly. The overall disk utilization index is above 0.84, and the highest index of 0.98 is infinitely close to 1, which shows that the performance of the system is efficient. © 2023 ieee.

关键词： Cloud Platform data Collection Multi-source data Transmission Lines

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Leveraging AI to Personalize HR Marketing Campaigns: A data-Driven Approach 3

Leveraging AI to Personalize HR Marketing Campaigns: A Data-...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Alshar, Moath Mahmoud Sao, Ameet Sharma, Manish Kadyan, Sunil Rao, Vuda Sreenivasa Anitha Vijayalakshmi, B. Jadara University Faculty of Business Business Administration Department Jordan National Institute of Construction Management and Research Haryana Bahadurgarh India Maharaja Agrasen Institute of Management Studies Department of Business Administration Delhi India Manav Rachna University School of Management and Commerce Faridabad India Koneru Lakshmaiah Education Foundation Department of CSE A.P. Vaddeswaram India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ECE Chennai India

ISBN: (纸本)9798331523893

The research proposes an AI-driven personalization framework for enhancing the efficiency and engagement of HR marketing campaigns. The system integrates real-time data processing, collaborative filtering, and feedback loops to dynamically recommend job opportunities tailored to individual candidate preferences. Using an HR-related dataset from Kaggle, the framework preprocesses data by handling missing values, scaling numerical features via Min-Max Scaling, and reducing dimensionality using Principal Component Analysis (PCA). Collaborative Filtering is employed to predict candidate preferences through matrix factorization, dynamically updating recommendations based on real-time user interactions. The experimental evaluation highlights the framework's superior performance compared to traditional methods. Click-Through Rate (CTR) improved significantly, with AI-driven personalization achieving 30% compared to 18% from rule-based methods and 12% from the baseline. Accuracy analysis revealed that Collaborative Filtering outperformed random and content-based methods, achieving the highest precision (85%), recall (88%), and F1-score (86%). These results underscore the effectiveness of AI in aligning candidate preferences with recruitment goals while ensuring dynamic adaptation through feedback. The proposed system also seamlessly integrates personalized insights into HR marketing channels such as job portals, email campaigns, and social media advertisements, optimizing recruitment processes and enhancing user engagement. Future research aims to extend the dataset to include diverse candidate profiles, incorporate multi-modal data sources, and leverage deep learning for improved recommendation accuracy. Additionally, explainability techniques will be explored to ensure transparency and build trust among users. This framework demonstrates the transformative potential of AI-driven personalization in reshaping HR marketing strategies. © 2025 ieee.

关键词： Principal component analysis

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