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检索条件"机构=Dep. of Math. and Computer Science"

共 67 条记录，以下是1-10 订阅

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Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A Novel Over-sampling Approach 8

Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A No...

引用

8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Azizian, Aref Yousefimehr, Behnam Ghatee, Mehdi Dep. of Math. & Computer Science Iran

ISBN: (纸本)9798350374674

Urbanization and the emergence of smart cities have led to increased industrial activities, raising concerns about safety and environmental hazards. Detecting and monitoring gas leaks accurately is crucial due to the significant risks they pose to public health and the environment. In this study, we propose a novel approach that combines thermal imaging with sensor data to enhance gas detection accuracy in urban and industrial settings. By employing advanced algorithms and analyzing extensive datasets, our methodology achieves an impressive accuracy rate of 99.32%, setting a new standard in gas leak detection. Our innovation lies in the utilization of a unique oversampling technique called Normal Synthetic Minority Oversampling Technique (NSMOTE), coupled with the simultaneous integration of thermal imaging and sensor data within a multimodal framework. This breakthrough not only underscores the potential of integrating diverse data sources for environmental monitoring but also represents a significant stride in mitigating the risks associated with industrial gas emissions. © 2024 IEEE.

关键词： Data integration

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing 10

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

引用

10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Azizian, Aref Ahmadian, Rouhollah Ghatee, Mehdi Shamsi, Mostafa Amirkabir University of Technology Dep. of Math. and Computer Science Tehran Iran

ISBN: (纸本)9798331532543

Overfitting and the presence of noisy labels are significant challenges in the training of machine learning models, particularly in complex datasets. This paper introduces a novel checkpointing method designed to mitigate overfitting while maintaining model calibration in the presence of noise. Our approach leverages the entropy of the two largest predicted probabilities from the model's output to determine when to save the model during training. This entropy-based criterion allows for effective checkpointing without necessitating a separate validation set. We evaluate our method across various datasets characterized by different noise levels and model architectures. The results demonstrate that our entropy-based checkpointing offers a more robust solution for managing model performance during training. This work contributes to the ongoing efforts to enhance model reliability in real-world applications where data imperfections are prevalent. © 2024 IEEE.

关键词： Entropy-Based Metric Noisy Labels Overfitting Prevention

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Real-Time Audio Analysis for Detection of Apnea Intervals in Health-Care System 10

Real-Time Audio Analysis for Detection of Apnea Intervals in...

引用

10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Vahdani, Alireza Rahmati, Mahdieh Sharifi, Mehdi Ghatee, Mehdi Amirkabir University of Technology Iran Dep. Of Math. And Computer Science Tehran Iran

ISBN: (纸本)9798331532543

Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oximetry and capnography, often fail to detect respiratory pauses in a timely manner, limiting their effectiveness in high-stakes clinical settings. To address this gap, we propose an automated method leveraging tracheal sound analysis using a deep learning model based on ResNet18. Our approach incorporates signal processing techniques, noise reduction, Mel-spectrogram extraction, and data augmentation, to optimize the model's performance. The model was evaluated using multiple performance metrics, including AUC of the precision-recall curve, F1 score, etc. It achieved an AUC of 92.5% and an F1 score of 89% for the apnea class, demonstrating the system's potential for precise, real-time respiratory monitoring in clinical environments. © 2024 IEEE.

关键词： Image coding

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The Impact of GPU on Containerized computer Vision Applications on Edge Nodes 8

The Impact of GPU on Containerized Computer Vision Applicati...

引用

8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Alqaisi, Osamah I. Tosun, Ali Saman Korkmaz, Turgay University of Texas at San Antonio Department of Computer Science San AntonioTX78249 United States The University of North Carolina at Pembroke Dep. of Math. and Computer Science NC28372 United States

ISBN: (纸本)9798350368499

Edge computing revolutionizes computer vision, but faces challenges due to computational demands and complexities in dep.oyment and platform integration. In addressing these hurdles, container technology emerges as a crucial solution, particularly in mitigating dep.oyment and security issues. However, the impact of GPU acceleration on containerized computer vision applications remains understudied. This paper explores containerized computer vision applications across ARM edge devices, analyzing performance on different CPUs and GPUs. Insights cover processing time, CPU usage, memory utilization, and application performance, highlighting the importance of ARM processor selection. Integrating CPU and GPU enhances execution time but leads to significant memory usage increases. Workload escalation strains the GPU, resulting in slowed processing, and its performance becomes comparable to that of the CPU. Furthermore, the performance of computer vision applications varies with different containers. These findings underscore the delicate balance between CPU and GPU utilization and the pivotal role of algorithm selection in optimizing containerized computer vision applications on edge devices. © 2024 IEEE.

关键词： ARM processors

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Towards a Greener Horizon: The Evolution of Farming through Green IoT Applications 24

Towards a Greener Horizon: The Evolution of Farming through ...

引用

7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Ibnoukhattab, Abdelmouttalib Abdesamad, El Rharras Rachid, Saadane Abdellah, Chehri Mohamed, Wahbi SIRC/LaGeS-EHTP Hassania School of Public Works Casablanca Morocco Dep. of Math. and Computer Royal Military College Kingston Canada

ISBN: (纸本)9798400709296

In an era marked by the rapid evolution of Internet of Things (IoT) technologies, the agricultural sector stands at the cusp of a significant transformation. This paradigm shift, driven by the escalation of the global population and the consequent demand for increased agricultural productivity, necessitates a harmonious balance between technological advancement and environmental stewardship. This manuscript examines the integration of IoT within agricultural practices through the lens of Green IoT. This novel approach synergizes IoT technologies with sustainable agricultural practices to champion environmental sustainability. We scrutinize the economic and environmental ramifications of dep.oying billions of interconnected IoT devices, emphasizing their potential to redefine agricultural methodologies by promoting efficiency and optimizing resource utilization. The discourse extends to an in-dep.h analysis of groundbreaking IoT applications in smart farming realms, such as precision agriculture, automated irrigation systems, and crop health surveillance. It underscores their capacity to elevate agricultural output while concurrently striving to mitigate water usage, curtail chemical dep.ndency, and diminish carbon footprints. The manuscript also delineates avenues for future investigations, spotlighting the imperative for more energy-conservative IoT solutions, the augmentation of data analytics for informed decision-making, and the encouragement of cross-disciplinary alliances to navigate the complex interface of technology and sustainable agriculture effectively. Through this exploration, the paper aspires to enrich the discourse on Green IoT and underscore its instrumental role in propelling sustainable agricultural practices amidst escalating environmental predicaments. © 2024 ACM.

关键词： Smart agriculture

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IoT Based Hydroponics : A Comparative Review 24

IoT Based Hydroponics : A Comparative Review

引用

7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Kamal, Sail Abdesamad, El Rharras Rachid, Saadane Abdellah, Chehri Mohamed, Wahbi SIRC/LaGeS-EHTP Hassania School of Public Works Casablanca Morocco Morocco Dep. of Math. and Computer Royal Military College Kingston Canada Canada

ISBN: (纸本)9798400709296

In the past few decades, there has been a substantial shift in agricultural practices, with a significant focus on cultivating plants without soil through hydroponics, a technique that supplies nutrients manually to enhance yields and minimize costs. However, hydroponics initially faced challenges such as high costs and the need for skilled maintenance, leading to the integration of the Internet of Things (IoT) for remote control and monitoring. Despite these advancements, hydroponics continued to have high water usage compared to aeroponics, a more recent development. To address this, further enhancements were made by incorporating machine learning algorithms into hydroponics, optimizing nutrient supply, and reducing water consumption, although it remained higher than desired. The recent integration of hydroponics with automation and IoT has marked a significant improvement, drastically cutting water usage by nearly 90% compared to other methods. This paper examines existing technologies in this field, introducing an innovative IoT-based hydroponics technique that considers various parameters like humidity, temperature, airflow, nutrients, carbon dioxide, and light intensity. This advanced approach aims for precise plant growth with minimal water and nutrient needs, representing a significant step towards sustainable, environmentally friendly, and economically viable agricultural practices. © 2024 ACM.

关键词： Hydroponics

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Aref Azizian Rouhollah Ahmadian Mehdi Ghatee Mostafa Shamsi Dep. of Math. & Computer Science Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

关键词： Checkpointing Training Adaptation models Prevention and mitigation Noise Machine learning Predictive models Robustness Noise measurement Overfitting

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Real-Time Audio Analysis for Detection of Apnea Intervals in Health-Care System

Real-Time Audio Analysis for Detection of Apnea Intervals in...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Alireza Vahdani Mahdieh Rahmati Mehdi Sharifi Mehdi Ghatee Dep. Of Math. And Computer Science Amirkabir University of Technology Iran Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

关键词： Time-frequency analysis Accuracy Smoothing methods Noise reduction Surgery Data augmentation Data models Real-time systems Data mining Monitoring

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Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A Novel Over-sampling Approach

Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A No...

引用

International Conference on Internet of Things and Applications (IoT)

作者： Aref Azizian Behnam Yousefimehr Mehdi Ghatee Dep. of Math. & Computer Science Amirkabir University of Technology (Tehran Polytechnic) Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

关键词： Technological innovation Accuracy Smart cities Soft sensors Surveillance Imaging Thermal sensors

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Improving ADHD Detection with Cost-Sensitive LightGBM

Improving ADHD Detection with Cost-Sensitive LightGBM

引用

International eConference on computer and Knowledge Engineering (ICCKE)

作者： Behnam Yousefimehr Mehdi Ghatee Ali Heydari Dep. of Math. & Computer Science Amirkabir University of Technology (Tehran Polytechnic) Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

One common neurodevelopmental issue that needs prompt and precise diagnosis for successful treatment is attention-deficit/hyperactivity disorder (ADHD). In this study, we present a novel approach using a cost-sensitive LightGBM (Light Gradient Boosting Machine) model to enhance the prediction of ADHD. Our key contribution lies in the integration of cost-sensitive learning, which prioritizes minimizing the misclassification costs associated with false negatives and false positives, critical in medical diagnostics. Additionally, we leverage the computational efficiency and speed of LightGBM, a gradient boosting framework known for its high performance in handling complex data structures. Our results demonstrate that the cost-sensitive LightGBM model achieves a predictive accuracy of 94% and an F1-score of 94%, while significantly reducing the time required for training and inference. This makes it a viable tool for real-world ADHD screening applications. This study highlights the importance of incorporating cost-sensitive algorithms in medical predictions to achieve a balanced trade-off between accuracy and resource efficiency.

关键词： Training Knowledge engineering Accuracy Computational modeling Predictive models Boosting Prediction algorithms Inference algorithms Medical diagnosis Medical diagnostic imaging

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