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检索条件"任意字段=IEEE International Conference on Computer Science and Automation Engineering"

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 ieee.

关键词： Lung cancer

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Mechanical Electrical automation Control System Based on PLC Technology 5

Mechanical Electrical Automation Control System Based on PLC...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Su, Shengheng College of Electrical Engineering Northwest Minzu University Lanzhou Gansu China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533694

In response to the increasingly complex mechanical and electrical automation control needs in modern industry, this paper studies a mechanical and electrical automation control system design scheme based on PLC (Programmable Logic Controller) technology. This study first starts with the system architecture and builds an industrial automation control system, including power supply, input/output modules, control logic, human-computer interaction, communication and safety modules. The system core uses Siemens S7-1500 PLC;the input/output module uses photoelectric isolation technology to improve signal stability;the control logic module integrates the adaptive PID (Proportion Integral Differential) algorithm;the human-computer interface realizes real-time data and alarm display based on the WinCC platform. The communication module uses the PROFINET network enhanced by TSN (Time-Sensitive Networking);the safety module is designed with emergency stop and light curtain sensor mechanisms. During the test, the performance of the system is verified through hardware-in-the-loop (HIL) simulation technology, and the stability and parameter sensitivity of the control algorithm are analyzed using the gray box test method. Experimental results show that in the position control experiment, the maximum absolute error of the system is 0.1 mm, the velocity fluctuation rate is controlled within pm 0.3 %, and the force control error is maintained within pm 0.2 ∼N. Under a variety of load and environmental conditions, the system exhibits excellent dynamic response and robustness. The research results show that the PLC control system designed in this paper has strong adaptability and efficiency in industrial automation control applications, and can effectively meet the needs of modern industry for high-precision control and multi-device collaboration. © 2025 ieee.

关键词： Digital storage

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LLM-Based Video Analytics Test Scenario Generation in Smart Cities 29

LLM-Based Video Analytics Test Scenario Generation in Smart ...

引用

29th international conference on Information Technology, IT 2025

作者： Yilmazer, Merve Karakose, Mehmet Munzur University Computer Engineering Tunceli Turkey Firat University Computer Engineering Elazig Turkey

ISBN: (纸本)9798331517649

Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field. © 2025 ieee.

关键词： generative artificial intelligence large language model prompt engineering smart cities software testing

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Stress Detection Using SVM and Virtual Assistant

Stress Detection Using SVM and Virtual Assistant

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Amale, Pranav Vibhute, Anup Mathpati, Mahesh S. Jatti, Ashwini V Thigale, Somnath B. Dr. D. Y. Patil Institute of Technology Electronics and Telecommunications Department Pune India Dr. D. Y. Patil Institute of Technology Instrumentation Engineering Department Pune India SVERIs College of Engineering Electronics and Telecommunications Department Pandharpur India Fabtech College of Engineering Computer Science and Engineering Department Sangola India

ISBN: (纸本)9798331529833

This paper presents a chatbot model designed to detect stress in users through simple yes-or-no questions. Utilizing a Support Vector Machine (SVM) algorithm, the model achieves high accuracy in identifying stress levels and provides users with actionable advice or medical recommendations. The chatbot's user-friendly interface is built using Anaconda Navigator and Python, ensuring ease of use for non-technical users. The model achieves a 100% accuracy rate on the given dataset, making it a promising tool for stress detection and early intervention. © 2025 ieee.

关键词： Python

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 ieee.

关键词： Authentication Protocol

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PI-Type Sliding Mode Fractional-Order Synchronization of a Memristive Chua Circuit

PI-Type Sliding Mode Fractional-Order Synchronization of a M...

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2025 international conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Astekin, Dorukhan Adiguzel, Fatih Yildiz Technical University Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9798331529604

In this study, a sliding mode fractional-order synchronization is proposed for the memristive chaotic Chua system. After the integer-order model of the chaotic memristive Chua system is given, a PI-type sliding surface-based controller structure combined with the fractional derivative operator is devised. The stability analysis of the synchronization closed-loop system is proven in the sense of Lyapunov theory. To demonstrate the effectiveness and applicability of the proposed controller structure, the fractional-order derivative operator in the controller structure is considered with two different fractional orders, and computer-based numerical simulation studies are performed comparatively. © 2025 ieee.

关键词： Sliding mode control

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Formula 1 Race Winner Prediction Using Random Forest and SHAP Analysis

Formula 1 Race Winner Prediction Using Random Forest and SHA...

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2025 international conference on Control, automation, and Instrumentation, IC2AI 2025

作者： El Haber, Elias Sawaya, Elie Attieh, Maroun Tannous, Aldo Ghazaly, Weam Owayjan, Michel American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Predicting the outcomes of Formula 1 (F1) races presents a significant challenge due to the complex interplay of numerous factors, including driver skill, vehicle performance, team strategy, and unpredictable race-day conditions. This paper investigates the application of the Random Forest algorithm enhanced with SHAP (Shapley Additive Explanations) analysis to forecast race winners while providing interpretability to the model's predictions. By leveraging extensive historical data encompassing driver metrics and race conditions, we aim to build a model that achieves high predictive accuracy and offers valuable insights into the most influential factors affecting race outcomes. Our approach demonstrates how integrating machine learning with explainable AI techniques can assist F1 teams in making data-driven decisions, ultimately optimizing their strategies in the highly competitive environment of professional racing. © 2025 ieee.

关键词： Random forests

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Enhancing NLP for Indic Languages with Limited Resources: A Study of Transformer Models for Translation and Summarization

Enhancing NLP for Indic Languages with Limited Resources: A ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhati, Abhivyakt Gupta, Ved Kumar Shah, Hare Ram Nagar, Rajesh Nimawat, Sunil Computer Science & Engineering-AIML IPS Academy Institute of Engineering & Science Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science SAGE University Indore India Computer Science & Engineering Indore India IQAC Cell Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science Indore India

ISBN: (纸本)9798331512965

Significant advancements have been made in natural language processing (NLP), especially in numerous languages. The vast linguistic diversity of India presents unique challenges for automated language processing tasks as Indian languages other than Hindi are marginalized in both cases;datasets which incur a vast toll on summarization models that require huge datasets to be trained on. This paper presents a comparative and comprehensive analysis of multiple transformer-based models which have been trained and/or fine-tuned for lesser-used Indic languages. Purely focusing on Natural Language Translation (NLT) and evaluating its efficiency in generating contextually accurate summaries for languages such as Bengali and Marathi. Models such as mT5, IndicBART, mBART and other multilingual NLP models will be benchmarked and assessed using the ROUGE score. This study highlights the model's strengths and weaknesses in handling the limitations summarization of low-resource Indic languages. The findings will contribute heavily to the development of NLP tools aimed at improving summarization inclusivity across India's diverse languages. © 2025 ieee.

关键词： Translation (languages)

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Dynamic Street Lighting based on Adaptive Learning

Dynamic Street Lighting based on Adaptive Learning

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Biswas, Suvrojyoti Sinha, Mourani Techno India University Department of Computer Science and Engineering West Bengal Kolkata India Techno India University Department of Mathematics West Bengal Kolkata India

ISBN: (纸本)9798331513894

Smart street light solution utilizing Long Range Wide Area Network (LoRaWAN) is one of the highly interesting topics in the field of Internet of Things (IoT) automation engineering. It is seen that many prototypes and modelling were served with IoT based sensors in this existing research idea globally and in fact Narrow Band IoT is used in some cases for data sharing along with secure communication channels. But some factors other than sensory technology associated with it will become more crucial to consider as those are emerging now like street light controlling as per traffic loads, adjustment of illumination power dynamically, and some changes of road condition if possible to distribute the data under a fault-tolerant architecture. In this paper, there is proposed an adaptive algorithm for street light control with decision-making logic based on Reinforcement Learning in some of the road cases as discernment. And with it traffic pattern observation to feed the dataset further using a deep learning technique. Also, simulate a Deep Q-learning algorithm that is necessary to be shown here and the most enthusiastic thing of implementation is to observe the road patterns and vehicular movements and note them clearly. © 2025 ieee.

关键词： Adaptive algorithms

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IOT Based Smart Irrigation Water Management System 4

IOT Based Smart Irrigation Water Management System

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Baranitharan, B. Sekhar Dhanush, Ch. Chandra Yuvaraj, K. Gowtham, Bireddy Mahalakshmi, R. Madasamy, K. Kalasalingam Academy of Research and Education Department of Agricultural Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication and Engineering Virudhunagar India

ISBN: (纸本)9798331523923

The world's most urgent problems are water scarcity and ineffective resource management brought on by population increase, climate change, and aging infrastructure. Using IoT technology, the 'IoT-Based Smart Water Management System' integrates sensors and microcontrollers to monitor water levels, flow rates, and quality in real-time. Using data analytics and automation guarantees effective water distribution, leak detection, predictive maintenance, and optimize usage, minimizing waste. Precise irrigation based on soil and meteorological data is supported by essential components like the Arduino Uno and ultrasonic sensors, which allow for smooth automation and remote connectivity through mobile or web applications. This scalable system provides a strong framework for water resource conservation, encourages sustainable practices, and improves agricultural efficiency. © 2025 ieee.

关键词： Smart agriculture

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