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Research on Factor Support Vector Multi-classification Algorithm Based on Factor Space theory 6th

Research on Factor Support Vector Multi-classification Algor...

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6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

作者： Zhang, Kaijie Zeng, Fanhui Li, Jiaxin College of Intelligent Engineering and Mathematics Liaoning Technical University Fuxin123000 China College of Science Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9783031712524

In order to solve the accuracy and complexity of multiple classification algorithms, on the basis of factor space theory, the divisibility measurement condition and the construction condition of binary tree are defined. On the basis of balanced binary tree, combining the relationship between class spacing and sample circle radius, a factor support vector multiple classification algorithm(M-FSV) is proposed by using recursion idea and the principle of "easy classification first". Experiments are done with one-to-one support vector machine and balanced binary tree support vector machine algorithm, and experimental comparison is made. the results of 8 data sets in UCI database show that the training time of M-FSV is less than that of SVM, and the algorithm accuracy is higher than that of SVM. the research results expand the theory and application of factor space, and provide a new idea and simple method for classification problems in machine learning. © IFIP international Federation for Information Processing 2025.

关键词： Vectors

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IoT-Enabled intelligent System for Helmet Detection and automated Penalty Enforcement using AI & ML

IoT-Enabled Intelligent System for Helmet Detection and Auto...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Nandasree Shathanakota Saimanaswini Rachamadugu Uppara Layavardhini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the increasing number of road accidents due to non-compliance with helmet regulations highlights the need for intelligent systems to enhance rider safety. this paper presents an IoT-enabled system that utilizes Artificial Intelligence (AI) and Machine learning (ML) to detect riders without helmets and enforce penalties automatically. the system integrates a Raspberry Pi with a camera module to capture real-time images of motorcyclists at traffic signals. AI-powered object detection models, such as YOLOv5, determine helmet usage, while Optical Character Recognition (OCR) techniques extract license plate details for violators. the system transmits violation data to a centralized server via IoT communication protocols, where an automated penalty system processes the information and notifies the rider through SMS or email. By automating helmet detection and penalty enforcement, this system enhances road safety, reduces manual intervention, and ensures adherence to traffic regulations.

关键词： Head Road accidents Manuals Machine learning Road safety Regulation Real-time systems Safety intelligent systems Monitoring

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Development of a learning-Based Control System for Robot-Assisted Grinding of Rubber Roller

Development of a Learning-Based Control System for Robot-Ass...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Fabian Reiling Maximilian Bause Stefan Gröger Christian Henke Steven Koppert Ansgar Trächtler Scientific Automation Fraunhofer IEM Paderborn Germany Intelligent Automotive Industry Capgemini Invent Hamburg Germany Control Engineering and Mechatronics Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Grinding rubber rollers to a specific geometry presents significant challenges due to the material's non-linear behavior. Currently, this process has not been successfully automated, primarily because the control concepts implemented thus far do not achieve the desired accuracy for economical operation. Consequently, these rollers are manually ground to the required geometry on a grinding wheel. Previous work has led to the development of a system that automates the grinding of rollers using a robot. the objective of this paper is to demonstrate, through modeling, how a learning-based control system can be developed for this process to reduce production time and facilitate an economical operation. To this end, we propose two control concepts: one that directly predicts the required control variable using a learning-based controller, and another that employs an AI-corrected control mechanism, which utilizes the control variable provided by a proportional controller to correct the error between the control variable and the control difference. Finally, we conduct a comparative simulation of the performance of these concepts against a conventional controller.

关键词： Geometry Hands Manufacturing processes Process control Wheels Production Control systems Parametric statistics Rubber Mobile robots

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IoT-Driven intelligent Waste Management System with Disinfectant Spray

IoT-Driven Intelligent Waste Management System with Disinfec...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： N. S. Swapna Basutkar Kalyan Rao Kummari Netra Paradesi Abhinav Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid increase in urban waste generation demands innovative solutions to improve public health and operational efficiency. this paper presents an IoT-driven intelligent waste management system that integrates real-time monitoring, dynamic route optimization, and automated disinfectant mechanisms. Smart bins equipped with ultrasonic, gas, rain, and infrared sensors transmit data via ESP8266 Wi-Fi/GSM modules to a cloud platform (thingSpeak), enabling anomaly detection and route optimization using a modified Travelling Salesman Problem (TSP) algorithm. A solenoid-based disinfectant system reduces bacterial load by 98% (verified through lab testing [5]), while the TSP algorithm reduces collection time by 40% and fuel consumption by 30% compared to traditional methods [4]. A three-level dashboard (admin, collector, user) provides actionable insights through a mobile app. Deployed in a 1 km 2 testbed with 10 bins, the system demonstrates scalability and cost-effectiveness. Future work includes integrating machine learning for predictive analytics and automated waste segregation. this solution aligns with UN Sustainable Development Goals (SDGs) 11 and 13 [1], promoting cleaner cities and climate action.

关键词： Waste management Rain Machine learning algorithms Urban areas Real-time systems Mobile applications Predictive analytics Optimization Gas detectors intelligent sensors

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ROME: A Robust Ontology Matching Method Based on Ensemble learning

ROME: A Robust Ontology Matching Method Based on Ensemble Le...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart Computing (HPSC) and intelligent data and Security (IDS)

作者： Jiawei Ding Chao Wang Jiayin Liu Song Han Tianzhu Wang China Academy of Safety Science and Technology Beijing China Key Laboratory of Urban Fire Monitoring and Early Warning Ministry of Emergency Management Beijing China School of Mechanical and Electrical Engineering China University of Mining and Technology Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

In the era of big data, ontology matching, which determines correspondences between concepts, has become a crucial knowledge-mining task. Existing ontology matching methods typically focus on semantics, structure, and instances, often optimizing datasets within specific domains. However, these methods suffer from limited universality when applied across diverse domains. To address this issue, this paper proposes a Robust Ontology Matching method based on Ensemble learning (ROME). By integrating multiple machine learning algorithms, ROME improves scalability, robustness, and performance, overcoming the challenges of noisy data and data distribution shifts. the proposed approach employs ensemble learning techniques, such as Bagging and Boosting, to enhance model generalization and achieve superior results in ontology matching. Extensive experiments on human and rat anatomy datasets demonstrate that the ensemble fusion approach outperforms existing methods, yielding better mapping precision, recall, and F1 scores.

关键词： Semantic Web Scalability Semantics Ontologies Big data Rats Robustness Ensemble learning Security Noise measurement

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Citizen Science for Action: A Platform to Empower Public Participation in Radon Detection and Mitigation 11th

Citizen Science for Action: A Platform to Empower Public Par...

引用

11th international conference on Energy and Environment Research, ICEER 2024

作者： Silva, Joaquim P. Meneses, Paulo Lopes, Nuno Barros, Paulo Azevedo, Rolando Nunes, Leonel J. R. Curado, António Lopes, Sérgio I. 2Ai—School of Technology IPCA Barcelos Portugal LASI—Associate Laboratory of Intelligent Systems Guimarães Portugal Instituto Politécnico de Viana Do Castelo Viana Do Castelo Portugal Faculdade de Engenharia Universidade Do Porto Porto Portugal ADiT-Lab—Instituto Politécnico de Viana Do Castelo Viana Do Castelo Portugal IT—Instituto de Telecomunicações Aveiro Portugal CiTin—Centro de Interface Tecnologico Industrial Inovarcos Arcos de Valdevez Portugal

ISBN: (纸本)9783031824067

Despite all related studies and research efforts, radon gas emissions and their impact on human health remain relatively unknown to the general public. the World Health Organization (WHO) estimates that radon is at the origin of 3–14% of all lung cancers and, although WHO recommendations to tackle indoor radon exposure are progressively paving the way, most implemented measures have not been effective, so we need new approaches to increase public awareness and reduce harmful effects of radon. Awareness will be leveled by prioritizing a citizen science-based approach, which involves advocating for a participatory process that encourages the active involvement of citizens in scientific data collection. In the context of this research, we are developing a citizen science-based web platform that collects relevant data related to indoor radon concentration. On the web platform, all citizens are encouraged to participate in a pre-diagnosis procedure to assess the radon potential existing in the buildings they live or work in. An automated classification model assesses the potential for indoor radon gas and outputs a pre-diagnosis result in three categories: low, medium, or high risk. thus, while raising awareness of radon risk, we can provide newly qualified data for indoor radon exposure research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Citizen science Indoor radon potential Indoor radon risk communication Radon potential prediction

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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

作者： Sohum Kabra Prithvi Nadagouda Yash Nikam Manoj S. Kavedia Department of Electronics and Telecommunications Engineering (EXTC) Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices.

关键词： Temperature sensors Temperature measurement Home appliances Analytical models Voltage measurement Machine learning Logic gates Predictive models Real-time systems intelligent sensors

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Detection of Psychological States in Persian Social Media Posts Using Advanced Deep learning Architectures with Attention Mechanisms

Detection of Psychological States in Persian Social Media Po...

引用

international conference on Web Research (ICWR)

作者： Zeinab Sheikh Hosseini Fard Hesam Fooladvand Mohammadreza Parvizimosaed Amirhossein Damia Department of Computer Islamic Azad University of Khoramabad Lorestan Iran Department of Electrical and Computer Engineering Queen's University Kingston Canada Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

In recent years, social media platforms have emerged as significant spaces for users to express their psychological states. Early detection of mental health conditions such as depression, anxiety, and suicidal ideation in these environments can play a vital role in timely intervention and prevention. this study presents a system based on advanced deep learning architectures incorporating attention mechanisms for the automatic detection of psychological states in Persian-speaking users. data were collected from platforms such as Instagram, Twitter, Telegram, and specialized psychiatric websites, and were labeled by a team of expert psychiatrists. After comprehensive preprocessing of Persian texts, several models including LSTM, BiLSTM, and Bidirectional GRU with Attention were evaluated. Results indicate that the Bidirectional GRU with Attention achieved the highest performance with an accuracy of 0.87. By focusing on Persian language data and utilizing expert-labeled realworld content, this research contributes a significant step toward the development of intelligent tools for mental health monitoring in digital environments.

关键词： Attention mechanisms Social networking (online) Prevention and mitigation Focusing Mental health Depression Natural language processing Web sites Multimedia communication Monitoring

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Education 4.0: Considering the Development of Teachers’ Psychological Dispositions Towards the Use of ICT with Kindergarten Children 14th

Education 4.0: Considering the Development of Teachers’ P...

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14th international conference on Methodologies and intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Peretti, Sara Caruso, Federica Pino, Maria Chiara Di Mascio, Tania Centre of Excellence DEWS University of L’Aquila L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Biotechnological and Applied Clinical Sciences L’Aquila Italy

ISBN: (纸本)9783031855603

Since 2020, the COVID-19 pandemic has forced teachers to rapidly adopt Information and Communication Technology (ICT) tools for distance learning, transforming education into an all-digital environment. this shift posed significant challenges, especially for kindergarten teachers accustomed to traditional, face-to-face methods. these changes will have long-term impacts on education, influencing teachers’ attitudes and beliefs, which take time to stabilize and evaluate. the present paper addresses this by posing the question: In 2024, four years after the COVID-19 pandemic, what attitudes have kindergarten teachers developed towards ICT integration in the classroom? For this purpose, the standardized "Intrapersonal Technology Integration Scale" was chosen to assess the beliefs of Italian kindergarten teachers. the study includes 49 kindergarten teachers, all women. data analysis showed that they have a strong interest in using ICT, indicating a need for ongoing support and training initiatives. However, teachers did not report feeling self-efficacy in using ICT with children in the classroom. this finding is crucial as it can influence their expectations and motivation towards integrating these technologies into daily teaching activities. In conclusion, this study highlights the critical importance of teachers’ attitudes in shaping future learning environments within the Education 4.0 era and provides suggestions to maximize ICT integration starting from early education to better meet the needs of future citizens. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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intelligent Nanorobots for Precision Cancer Diagnosis and Treatment using Deep Reinforcement learning

Intelligent Nanorobots for Precision Cancer Diagnosis and Tr...

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Mechatronics and Robotics engineering (ICMRE), international conference on

作者： Kapil Sharma Ravi Ranjan Dept. Information Technology Delhi Technological university Delhi India

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Early detection of cancer is essential as it significantly improves treatment outcomes and survival rates by addressing the disease before it spreads to other organs. Many cancers, such as breast, cervical, and colorectal, are highly treatable in their initial stages. With the development of Artificial intelligence, nano technology, the engineers, doctors, and biologist are working on nanorobots for diagnosis and treatment of cancer. It can help detect cancer at an early stage. Early diagnosis often reduces the complexity and cost of treatment, alleviating the physical and financial burdens on patients. Nanorobots are at the forefront of advanced biomedical applications, offering potential breakthroughs in targeted drug delivery and precision diagnostics, particularly for cancer treatment. By integrating artificial intelligence and Deep Reinforcement learning, these devices can autonomously navigate complex biological environments to detect cancer cells via biomarker gradients. this work explores the deployment of Deep Q-learning to improve nanorobot decision-making, enabling efficient path optimization and obstacle navigation.

关键词： Training Navigation Biological system modeling Veins Medical services Biological systems Deep reinforcement learning Nanobioscience Medical diagnostic imaging Cancer

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