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检索条件"任意字段=International Conference on Computer and Automation Engineering, ICCAE"

共 41680 条记录，以下是71-80 订阅

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AI Driven Wildfire Prediction in Australia Using Machine Learning for Effective Disaster Prevention

AI Driven Wildfire Prediction in Australia Using Machine Lea...

引用

international conference on computer and automation engineering, iccae

作者： Zina Abohaia Abeer Elkhouly Mai Elbarachi School of Computer Science University of Wollongong in Dubai Dubai UAE School of Engineering University of Wollongong in Dubai Dubai UAE

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Wildfires pose significant ecological, economic, and societal challenges in Australia, with their frequency and intensity exacerbated by climate change. Accurate wildfire prediction is critical for effective fire management strategies. This study investigates the performance of machine learning models incorporating time-series data for wildfire prediction across seven Australian regions. Comparisons between models with and without SMOTE and ANOVA revealed that these techniques did not consistently improve performance. Without SMOTE and ANOVA, CatBoost achieved the highest overall accuracy (84.35 %), followed closely by HXGBoost (84.32%}) and LightGBM (84.16%). After applying SMOTE and ANOVA, all models experienced slight declines in accuracy, sensitivity, and F1 scores, indicating that oversampling and feature selection may have introduced noise rather than meaningful improvements. Regionspecific models significantly outperformed a single model for all of Australia, with exceptional accuracies observed in Queensland (96.36%) and Western Australia (97.95%). However, challenges persisted in regions like South Australia, where accuracy dropped further after applying SMOTE and ANOVA (from 70.16% to (65.03%), highlighting the need for more targeted feature selection and data-balancing techniques.

关键词： Wildfires Accuracy Biological system modeling Computational modeling Predictive models Feature extraction Data models Australia Analysis of variance Tuning

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Classification of Alcoholic Beverage Using Electronic Nose and Support Vector Machine

Classification of Alcoholic Beverage Using Electronic Nose a...

引用

international conference on computer and automation engineering, iccae

作者： Jon Dave V. Bautista Marc Julian M. Elizarde Meo Vincent C. Caya School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

In recent years, machine learning algorithms have seen significant development and implementation within the technological industry as ways to optimize training data and classification. The study aims to make use of an electronic nose system that is designed to classify five types of alcoholic beverages. Beer, gin, red wine, whiskey, and rum by using their distinct gas signatures. The system uses a Raspberry Pi 4 microcontroller, integrated with MQ gas sensors (MQ-2, MQ-3, MQ-4, MQ-7, and MQ-135) to detect ethanol, carbon dioxide. And other distinct compounds that are emitted by the beverages. A Support Vector Machine (SVM) algorithm was implemented to analyze and classify the data gathered from the sensors. By using 100 trials with 20 samples for each beverage, were used in training and evaluating the model. The results yielded an overall classification accuracy of $\mathbf{9 0 \%}$ which was verified by a confusion matrix. The findings demonstrate the potential of the system to identify between different alcoholic beverage types with precision which may have a potential application in quality control and alcoholic beverage authentication.

关键词： Support vector machines Training Electric potential Microcontrollers Training data Quality control Alcoholic beverages Electronic noses Sensors Gas detectors

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Enhancing Low-Resolution Images for FaceNet Facial Recognition Using GFPGAN on Raspberry Pi 5

Enhancing Low-Resolution Images for FaceNet Facial Recogniti...

引用

international conference on computer and automation engineering, iccae

作者： Azrael C. Anonas Godwyn Summer J. Mercado Isagani V. Villamor School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Throughout the years, significant advancements have been made in the field of photography and facial recognition technologies. And these technological advancements have become essential for numerous applications. However, traditional image enhancement algorithms often struggle to improve the image quality required for reliable feature extraction in facial recognition. In response, this study proposes the use of a Generative Adversarial Network (GAN), specifically GFPGAN (Generative Facial-Prior GAN), to enhance low-resolution images for facial recognition using FaceNET on Raspberry Pi 5. The system utilizes a Raspberry Pi V3 Camera Module to capture the images of participants at varying distances and angles. A total of 500 images underwent recognition, 250 of which were enhanced by GFPGAN. The system achieved an overall accuracy of 86.80% with GFPGAN-Assisted Facial Recognition, outperforming Standard Facial Recognition with an accuracy of 57.60 %. The results of the study emphasize the capability of GFPGAN in enhancing low-resolution images for facial recognition, which enables the development of facial recognition systems that can maintain high accuracy under suboptimal imaging conditions and the development of more dependable and secure applications that concern privacy and security.

关键词： Photography Image quality Privacy Accuracy Image recognition Face recognition Generative adversarial networks Reliability Standards Image enhancement

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A Multi-Gpu Edge Server-Based Uav Trajectory Optimization for Emergencies and Disasters

A Multi-Gpu Edge Server-Based Uav Trajectory Optimization fo...

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international conference on computer and automation engineering, iccae

作者： Umar Adeel Ali Alnoman Department of Computer Science and Engineering American University of Ras Al Khaimah UAE

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

This paper investigates the utilization of powerful edge computing servers to significantly reduce the computation time needed to calculate the optimal unmanned aerial vehicle (UAV) trajectory. This is especially important for UAVs in order to assist authorities and first responders reach the large number of geographically distributed users who need help during emergencies and disasters. In such hard circumstances, UAVs can help in search and rescue, monitoring, delivering supplies, and providing radio access links. The optimized UAV trajectory is computed using the Travelling Salesman Problem (TSP) approach which is NP-hard and imposes computational challenges. Hence, the TSP algorithm computations are intended to be processed in a ground multigraphics processing unit (GPU) server. In this way, both computational delays and energy consumption will be reduced for the UAV especially with the dynamic environment and large number nodes required to be visited in disaster scenarios. The proposed approach is tested via simulations considering a variable number of visited nodes, and a variable number of GPUs on the edge server. Results show a significant improvement in the computation time when multiple GPUs were used compared to the traditional central processing unit (CPU); however, using more GPUs can also lead to a slightly higher computation time compared to fewer GPUs.

关键词： Disasters Graphics processing units Autonomous aerial vehicles Public security Central Processing Unit Servers Vehicle dynamics Trajectory optimization Monitoring Edge computing

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Fire Detection System Using Infrared Thermometer and Image Processing with Level Identification and SMS Notification

Fire Detection System Using Infrared Thermometer and Image P...

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international conference on computer and automation engineering, iccae

作者： Maribelle Dequilla-Nicerio Jeremy Edward Cobar Rochelle Oserio Giann Paolo Velasco Computer Engineering Mapua Institute of Technology at Laguna Mapua Malayan Colleges Laguna Laguna Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Fire is the most common disaster that can happen daily. More people die from fire than any other kind of disaster. However, if there is an early warning from fire detection, and every person knows how to evacuate properly and calmly, a fire doesn't have to be fatal. Developing and implementing a Fire Detection System using image processing with fire level identification and SMS notification is to provide timely SMS notification and to detect fire to alert nearby personnel or authorities about the fire situation. To achieve this, image processing techniques are incorporated using a HuskyLens AI Machine Vision Sensor to detect fire from live video streams. The system is also capable of classifying the level of fire based on the intensity of the flame and sending the notification depending on the level identified. Arduino MEGA 2560 was used as the hardware platform along with the HuskyLens camera. The Arduino IDE 1.8.19 software was installed and used along with the HuskyLens library, Adafruit FONA library, Adafruit MLX90614 library, and Adafruit BusIO library. With the implemented system, fire detection and level identification with SMS notifications can be achieved with high accuracy and in real time.

关键词： GSM Temperature measurement Thermometers Image recognition Accuracy Image processing Prototypes Libraries Liquid crystal displays Monitoring

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Study of PV Systems Integration on the Lebanese Grid

Study of PV Systems Integration on the Lebanese Grid

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

作者： Nehme, Bechara Rached, Jad Abi Akiki, Tilda Electrical Telecommunication and Computer Engineering Department Jounieh Lebanon

ISBN: (纸本)9798331529604

This project will be focused on the effect of private level solar systems on the EDL grid, specifically on the distribution part of the entire EDL network. In order to show the said effects of the integration of solar panels into the EDL network, we created models using 'power world simulator' to visually represent the changes and modifications happening in the network, in addition to the motion of power through the lines. © 2025 IEEE.

关键词： DC distribution systems

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Mitigating Bias in Home Maintenance Recommendations: A Fairness-Aware Approach Using Memory-Based Collaborative Filtering

Mitigating Bias in Home Maintenance Recommendations: A Fairn...

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international conference on computer and automation engineering, iccae

作者： Jonalyn G. Ebron Karlo Miguel F. Palisoc Gene Anthony C. Reyes Stephen Paul V. Tongol College of Computer and Information Science (CCIS) Mapúa Malayan Colleges Laguna Cabuyao City Laguna Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Recommendation systems, while beneficial, often perpetuate biases, leading to unfair outcomes. This study focuses on mitigating popularity and gender biases within a mobile application. Aiming to develop a fairness-aware system, the research utilizes the MovieLens 1M dataset, employing three approaches. First, a personalized recommendation algorithm was developed, combining collaborative filtering with cosine similarity and demographic filtering. Second, a fairness-aware re-ranking algorithm, Deterministic Greedy, was designed to balance item popularity and gender representation. Third, these algorithms were integrated into a prototype mobile application. Usability testing and unit testing confirmed the application's effectiveness and adherence to ISO 9126 guidelines. This research provides a practical solution for mitigating multiple biases, demonstrating the feasibility of creating more equitable and ethical artificial intelligence systems. The findings contribute to the broader discourse on fairness in artificial intelligence, offering a template for developing inclusive recommendation platforms.

关键词： Measurement Ethics Collaborative filtering Prototypes Mobile applications Maintenance Artificial intelligence Usability Recommender systems Testing

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Optimizing Cost Efficiency in Cloud-Based automation with DevOps Techniques 3

Optimizing Cost Efficiency in Cloud-Based Automation with De...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Garg, Avni Singh, Tribhuvan Rajiv, Asha Dhamodaran, S. Sahoo, Girija Shankar Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Cloud automation has created a trend offering several advantages to businesses, including scalability and cost-effectiveness. Although deploying such automation in a cloud setting is fundamental, optimizing its cost efficiency proves challenging, particularly with DEVOPS techniques. DEVOPS integrates development and operations to promote collaboration and automate different parts of the software delivery process. It is also possible to elaborate the DEVOPS techniques by implementing them through cloud-based automation, which can greatly benefit both efficiency and cost. A key area for cost optimization is infrastructure management. By using DEVOPS techniques for the provisioning and deployment of resources, organizations can automate what have previously been tedious and manual processes, which can lead to lower infrastructure costs and more efficiency. Utilization of resources can be enhanced through DEVOPS techniques. Auto-scaling automatically adapts resources based on demand, while load balancing distributes workloads across resources to improve performance and costs. However, one area in which DEVOPS can dramatically enhance companies' cost efficiency is continuous monitoring and optimizing. Automated monitoring tools fire alerts notifying businesses about the most precise area where work is being done inefficiently in real-time, helping companies to save money. © 2025 IEEE.

关键词： automation Businesses Collaboration Dramatically Efficiency

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SIGNSPEAK: Integrating computer Vision and Speech Synthesis for Alphabet Filipino Sign Language (FSL)

SIGNSPEAK: Integrating Computer Vision and Speech Synthesis ...

引用

international conference on computer and automation engineering, iccae

作者： Jayvee N. Mapote Maribelle Dequilla-Nicerio Mapúa Institute of Technology at Laguna Mapúa Malayan Colleges Laguna Cabuyao City Laguna Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

In our interconnected world, effective communication is a fundamental human right fostering social interaction, education, and personal growth. However, individuals with sensory impairments, such as hearing loss, encounter significant barriers in communication, hindering their participation in society. This challenge is pronounced in the Philippines, where Filipino Sign Language (FSL) is vital for the Deaf community. The “SignSpeak” study addresses these challenges by integrating computer vision and speech synthesis technologies. Utilizing platforms like Raspberry Pi and Jetson Nano, SignSpeak interprets hand gestures in real-time, converting them into audible speech, and translates spoken language into text, enabling bidirectional communication. This approach empowers individuals with varying sensory abilities, fostering inclusivity and enhancing educational opportunities. SignSpeak promotes social equity and empowers the Deaf community in the Philippines through rigorous software and hardware development and a commitment to linguistic inclusivity. Beyond technical innovations, SignSpeak envisions a future where individuals of all abilities can communicate freely and participate in society. The study results were highly successful, demonstrating 95% accuracy, 96% precision, 94% recall, and a 95% F1 score. Additionally, the prototype received positive feedback, with an overall mean score of 4.845. These outcomes highlight SignSpeak's effectiveness and potential impact in improving communication accessibility for the Deaf community in the Philippines.

关键词： Hands Sign language computer vision Technological innovation Translation Bidirectional control Real-time systems Software Speech synthesis Sustainable development

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Decision-Making in Fog Computing: A Comparative Analysis of Pareto-Based Optimization and Weighted Objective Models for Application Placement

Decision-Making in Fog Computing: A Comparative Analysis of ...

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international conference on computer and automation engineering, iccae

作者： Zahra Farhadpour Tan Fong Ang Chee Sun Liew dept. Computer System and Technology faculty of Computer Science and Infromation Technology universiti malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The expanding adoption of Internet of Things (IoT) applications has amplified the challenge of identifying optimal solutions for resource allocation while balancing multiple competing objectives. As devices and applications proliferate, the complexity of resource management in fog computing escalates, necessitating advanced optimization techniques capable of adapting to evolving conditions and requirements. This paper presents a comparative analysis of two optimization strategies: Pareto-based multi-objective methods, exemplified by FP-NSGA-II (Fog Placement NSGA-II), a customized version of the Non-dominated Sorting Genetic Algorithm II tailored for task placement in fog environments, and weighted sum objective approaches, represented by Time Cost aware Scheduling (TCaS) and Weighted Sum-Genetic Algorithm (WS-GA). Through simulations and performance evaluations, this study compares these approaches in terms of trade-off management, scalability, and solution quality across varying system workloads. The experimental results highlight the strengths of the Pareto-based method, emphasizing its superior scalability and solution quality compared to weighted objective methods, along with its enhanced ability to balance multiple objectives while maintaining solution quality. The findings indicate that the Pareto-based approach is particularly effective in scenarios with competing objectives, as it provides a diverse set of solutions that can be tailored to specific application requirements.

关键词： Performance evaluation Costs Scalability Decision making Optimization methods Scheduling Resource management Internet of Things Edge computing Sorting

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