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Quality Assessment for 3D Coloured Point Clouds in Autonomous Vehicles 1

Quality Assessment for 3D Coloured Point Clouds in Autonomou...

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatia, Sagar Brindha, M. National Institute of Technology Dept. of Computer Science and Engg. Tiruchirappalli India

ISBN: (纸本)9798350365016

This paper proposes a novel no-reference (NR) technique for evaluating the quality of 3D colored point clouds, notably for autonomous vehicle applications. Our technique intends to provide an automated and dependable framework for precisely measuring point cloud data quality by using color information, hence improving the assessment process's capacity to detect small deviations and abnormalities. Unlike existing approaches that rely heavily on geometric data, our methodology incorporates color information, which considerably enhances discrepancy detection and gives a thorough quality rating. Our proposed technique uses sophisticated deep learning models, specifically Convolutional Neural Networks (CNNs), to enhance the feature extraction and regression stages. By employing CNNs, we improve the model's capacity to detect detailed patterns in point cloud data, hence enhancing the accuracy and dependability of quality predictions. The technique is intended to be applicable across several domains, supporting high-fidelity applications such as entertainment, industrial quality control, and autonomous cars. It guarantees the consistency and dependability of input data, allowing for more accurate environmental perception and decision-making. The study also benchmarks against full-reference (FR) 3D-QA measures and compares the proposed NR methodology to existing methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Active Control for Resonance Mitigation in Electric Drive Applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

引用

15th International Symposium on Industrial Electronics and Applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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Water Region Extraction in Aerial Images Using Deep Learning

Water Region Extraction in Aerial Images Using Deep Learning

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Pandey, Nikita Kumar, Saumya Yadav, Satish Kumar Srivastava, Shashwat Singh, Shomil Gupta, Deepak Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350369175

Water bodies are essential component of Earth's surface and accurate water region mapping is crucial for disaster management and environmental monitoring. Existing methods to map water bodies relies on traditional modelling methods that need extensive manual calibration struggle with scalability and lack advanced pattern recognition. This paper evaluates the effectiveness of deep learning-based semantic segmentation models, DeepLabV3+ and U-Net, using aerial images resized to $\mathbf{2 5 6 x} 256$ pixels in mapping water regions in aerial images. DeepLabV3+, with a ResNet50 backbone, and U-Net are trained on a comprehensive flood-affected dataset with preprocessing techniques such as resizing and augmentation. Model performance is assessed using metrics like IoU, F1 score, accuracy, precision, and recall. The results indicate that DeepLabV3+ outperforms U-Net, showing promise for automating water region mapping and improving flood surveys and urban planning. © 2024 IEEE.

关键词： Semantic Segmentation

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Security Enabled Supply-Chain System Implementation Using Blockchain Integration with Hyperledger and IPFS Techniques 5

Security Enabled Supply-Chain System Implementation Using Bl...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Amritesh Kumar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350383522

One of the important areas that needs attention is supply chain in this fast expansion of globalization. There are numerous tiny parts that are summed up to obtain an end and furnished product be it agricultural goods in food supply chain or even other goods such as electronics. Now the quality of the final product depends upon the quality of the raw materials used. Since raw materials are manufactured, distributed, and sold globally, ensuring the integrity and security proposes a challenging task due to widespread counterfeit, cloned items, and other infiltration of untrusted items. The proposed work will counter these issues by not only providing traceability but also empowering transparency through Hyperledger. Along with these features, bridges are added to make the transfer of assets such as payments across different blockchains. © 2024 IEEE.

关键词： Blockchain Consensus Internet of Things (IoT) Smart Contracts Supply Chain management

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Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning Techniques

Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning...

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2024 IEEE International Women in Engineering (WIE) Conference on Electrical and computer Engineering, WIECON-ECE 2024

作者： Keerthana, T. Jayakumar, K. Iyyappan, V. Shopika, S. Sowmiya, X.L. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535476

Tuberculosis (TB) remains one of the world's major health problems. Detection with Chest X-rays is a mainstay tool in the early diagnosis of TB. Traditional methods used in the conventional detection of TB are very slow and susceptible to human errors. Thus, there is a need for an automated technique. The paper discusses the possibility of using advanced deep learning techniques, particularly techniques stemming from transfer learning, to enhance the detectability as well as efficiency of TB. Fine-tuned pre-trained convolutional neural networks, such as ResNet and VGG, on a dataset of chest X-ray images extracted high-level features relevant to the detection of TB. Superhuman performance was achieved with limited medical imaging data by transferring learned representations from large-scale image classification tasks. Experimental results illustrate high accuracy, precision, and recall rates, confirming that transfer learning is adequate for automating TB detection. This approach reduces the need for manual intervention and offers a scalable, cost-effective solution to enable rapid TB screening, particularly in low-resource settings. The novelty of the work comes from using transfer learning with fine-tuned pre-trained models (ResNet and VGG) in getting high generalization accuracy for tuberculosis detection from chest X-rays, even with limited data. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

引用

5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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Skin Lesion Classification using Deep Learning Architectures 2

Skin Lesion Classification using Deep Learning Architectures

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Gothivrekar, Siddhi Khanvilkar, Anuj Nandi, Utsab Singh, Kamal Maktum, Tabassum Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350372892

Skin cancer, originating from diverse dermatological conditions, can be classified based on texture, structure, and color. Identifying lesions from images aids in pre-diagnosis;expanding beyond melanoma, early detection of lesions like Actinic Keratosis and basal cell carcinoma is crucial for timely intervention. Recently, deep convolutional neural networks (DCNNs) have shown promising results in dermatological analysis. This paper presents a comparative study of various DCNNs for the categorization of skin lesions. The proposed method involves fine-tuning the popular architectures such as the VGG16, Inception V3, Inception ResNet V2, and DenseNet 201 trained using the HAM10000 dataset. The paper investigates fine-tuning just the top layers and all the layers of the models, aiming to identify the most effective approach. According to the work's experimental findings, fine-tuning the complete trained model performs significantly better. Moreover, the study finds that fewer epochs and less computational time are required for fine-tuning the entire model while achieving higher accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

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Revolutionizing Road Safety for Advanced Vehicular Accident Prediction 2

Revolutionizing Road Safety for Advanced Vehicular Accident ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Deepika, T. Yuvaraj, N. Sri Preethaa, K.R. KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350327533

The detection and tracking of vehicle speed are crucial for ensuring the safety of civilians and preventing accidents. Speed tracking plays a pivotal role in traffic surveillance, where effective management and the well-being of citizens take precedence. The approach leverages computer vision-based models, specifically Multi-scale Detector for object detection, and a fusion of the DeepSORT and Centroid Sort algorithms for vehicle tracking from streaming video. To cater to the requirements of these algorithms, a custom dataset was developed within a controlled setting. Dataset augmentation was employed to prevent overfitting, thereby enhancing the dataset's diversity and the model's ability to generalize. The method demonstrates the capacity to accurately detect and track vehicles, maintaining performance even in high-traffic conditions. Cross-validation at every epoch during the training phase was implemented to increase the model's accuracy. In addition to the technical aspects, an LED display board was integrated to provide real-time data to oncoming vehicles at road curves. This display showcases information such as vehicle types, passage time, and the number of vehicles within the curvy road. The proposed Multi-Scale Detector model, a customized version of the You Only Look Once (YOLO) architecture, achieved a mean average precision (mAP) of 0.954 at a confidence interval of 0.5. This overall research provides an effective and accurate solution for optimizing vehicle tracking, enhancing transportation safety, and enabling a variety of practical applications in traffic management and monitoring. © 2024 IEEE.

关键词： DeepSORT Euclidean Distance and Vehicle Intelligence Multi-scale Detector Object Detection Speed Tacking

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A Study on Apriori based Movie Recommendation 2

A Study on Apriori based Movie Recommendation

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Behera, Tapas Mahapatra, Sarita Mohanty, Kritik Ranjan Farhan, Mohammad Tripathy, Sachidananda Priyadarshini, Sushree Bibhuprada B. Siksha 'o' Anusandhan Dept. of Computer Science & Information Technology Bhubaneswar India

ISBN: (纸本)9798350370188

With so many movies available nowadays, it might be challenging for people to choose one that suit their preferences and tastes. We have created a system for suggesting movies to users based on their viewing interests and history. A dataset of user movie viewing history and preferences is first gathered and preprocessed by the movie recommendation system followed by employing an Apriori technique to find the common item sets from frequently watched movies. The recommendations are created using these item sets as a foundation. A real-world movie dataset is applied in extended trials for movie recommendation system while considering evaluation metrics like recall, precision, and accuracy. The experimental findings show that the suggested approach is capable of giving users precise and individualized movie suggestions by utilizing the Apriori algorithm. © 2024 IEEE.

关键词： Recommender systems

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