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共 729 条记录，以下是41-50 订阅

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Prediction Of Autism Spectrum Disorder Using machine learning 3

Prediction Of Autism Spectrum Disorder Using Machine Learnin...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Arunprasath, T. Niranjana, M. Sharumathi, M. Buvanesh Pandian, V. Rajasekaran, M Pallikonda Ramaraj, Kottaimalai Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Eee Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Ece Krishnankoil621 216 India

ISBN: (纸本)9798350361186

This is to investigate the predictive potential of electroencephalogram (EEG) signals extracted from children diagnosed with autism spectrum disorder (ASD). The objective is to utilize machine learning techniques, specifically logistic regression, to discern patterns within EEG data that could aid in the early prediction of ASD. EEG signals collected from autism-affected children are analyzed and processed to extract relevant features. Subsequently, these features are used as inputs to train a logistic regression model. Thestudy evaluates the model's performance in distinguishing between ASD and typically developing individuals, aiming to provide a reliable predictive toolfor identifying ASD based on EEG signals. Results suggest promising prospects for leveraging EEG data and machine learning methods in the early detection and potential prediction of autism spectrum disorder in children. © 2024 IEEE.

关键词： Electroencephalography

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Classification of Phishing Websites using machine learning Models 3

Classification of Phishing Websites using Machine Learning M...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Raminedi, Santhosh Pandey, Trilok Nath Woonna, Venkat Amith Mascarenhas, Sletzer Concy Bharani, Arjun Vellore Institute of Technology Department of Computer Science Chennai India

ISBN: (纸本)9798350320749

Phishing attacks have become increasingly and it is a serious threat to security of the user information. It targets unsuspecting individuals through deceptive websites that appear to be legitimate. These attacks can compromise sensitive information and also have serious consequences. machine learning has a potential solution to address the growing threat of phishing. By training algorithms to recognize the tell-tale signs of phishing websites, it may be possible to detect and prevent these attacks before they can cause harm. In our paper, we explored the features of detecting the phishing url with the use of ML algorithms such as SVM, Logistic Regression, Naive Bayes classifier, and also ensembling algorithm such as Random Forest Classifier. We examine the features that are commonly used in these algorithms and compare their effectiveness in accurately classifying websites as either Phishing or Legitimate © 2023 IEEE.

关键词： Websites

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Strengthen motor imagery for motor disorders patients in metaverse through brain computer interfaces 3

Strengthen motor imagery for motor disorders patients in met...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Lei, Jingyu Department of Computer Science University College London 295 Holloway Rd. LondonN7 8HS United Kingdom

Both brain-computer interface (BCI) and metaverse technology are emerging research fields involving computer science, neuroscience, psychological, cognitive science, biomedical engineering, mathematics, signal processing, clinical medicine, automatic control, etc. This paper focuses on the role of both in the medical field for patients with motor disorders (MD). MD is mostly caused by neurological diseases or accidental trauma, which often results in the death of nerve cells, loss of synapses and localised neuronal conduction blockage, thereby damaging the neural network's structural and functional integrity in the brain and disrupting the brain's information processing processes, resulting in motor paralysis of the limbs. Approximately 3% of the world's population is currently suffering from severe disabilities, and approximately 110-190 million people are unable to live independently. Of these, nearly 100 million suffer from motor paralysis caused by stroke, cerebrospinal cord injury and amyotrophic lateral sclerosis, which has a serious impact on their daily work and life. Scientists are already using BCI to help people with paralysis caused by neurological damage to the brain, with a lesser contribution from the metaverse. This paper draws on past BCI and biological experiments to logically reason about methodologies such as neurofeedback, motor imagery, visual prosthesis implantation and training to explore future possibilities for treating MD in the interactive world of BCI and the Metaverse. © Published under licence by IOP Publishing Ltd.

关键词： Brain computer interface

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Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 signal processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 IEEE.

关键词： feature engineering machine learning radio maps Wireless communications

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Comparison of machine learning Methods for Classification and Diagnosis of Skin Disease 3

Comparison of Machine Learning Methods for Classification an...

引用

3rd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2023

作者： Deepti, Kakarla Deepthi, S. Aruna Vasavi College of Engineering Department of Ece Hyderabad India

ISBN: (纸本)9798350358742

Skin disease is one of the most prevalent diseases around the world. To ease the complexity of conventional computational methods, deep learning techniques are adopted. By leveraging transfer learning and deep learning models, Convolution neural networks have been built, trained and evaluated. From the available data set of 10,082 using CycleGAN data augmentation a total of 39507 images are generated. One challenge faced during the implementation is mainly to improve the stability of CycleGAN during training phase. To analyze the performance of CycleGAN, three CNN models based on architecture of MobileNet, LeNet and modified LeNet are considered. A comparative analysis based on performance of three architectures is done and it is concluded that modified LeNet has outperformed with a higher accuracy of 95.03%. © 2023 IEEE.

关键词： Dermatology

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Faster R-CNN and YOLOv3: A general analysis between popular object detection networks 3

Faster R-CNN and YOLOv3: A general analysis between popular ...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Dong, Wenbo Computer Science University of Science and Technology of China Anhui Hefei230026 China

Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Skin Cancer Detection using modified CNN with GLCME Features 3

Skin Cancer Detection using modified CNN with GLCME Features

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3rd Odisha international conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Das, Subhadarshini Das, Goutami Mohanty, Mihir Narayan Applied Signal Processing Lab Iter Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

Skin cancer detection is a critical application of computer vision in the medical field. Early detection of skin cancer, including melanoma and non-melanoma types, significantly improves treatment outcomes. Automated detection methods can assist dermatologists by providing consistent and objective analysis. Traditional methods rely on clinical expertise and manual examination, which can be subjective and error-prone. machine learning is the subset of AI that offers promising results for automatic skin cancer detection using image analysis. This report explores a hybrid approach combining Convolutional Neural Networks (CNNs) with GLCM and Energy features as one set of feature to enhance diagnostic accuracy. It is standard clinical criteria for evaluating skin lesions. The combination of CNNs with GLCME features enhances diagnostic accuracy by leveraging both learned and handcrafted features. © 2024 IEEE.

关键词： Convolutional neural networks

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Impact of multi-GPUs and hyperparameters on parallel algorithms based on pytorch distributed 3

Impact of multi-GPUs and hyperparameters on parallel algorit...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Wei, Shiyu SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Chengdu610000 China

Neural networks, as powerful models for solving many genuine problems, are suffering from the issue of being too computationally intensive due to the excessive size of the models themselves and the large datasets used for training. This paper's work focuses on a parallel algorithm, experimented on the classical convolutional neural network LeNet-5, aiming to reduce the time it takes for the neural network to train. This applies a massively parallel GPU structure to take full advantage of its high computing power. Among the approaches to improve the performance of parallel algorithms, many efforts have been made to improve the design of parallel structures, while this work focuses more on the enhancement of parallel algorithms by reasonable settings of neural network hyperparameters. This paper tries to approach (1) reducing the running time by testing parallelism with different numbers of GPUs and (2) further investigating the relationship between parallelism and model accuracy degradation and experimentally deriving a parallel scheme that can reduce the training time while guaranteeing accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Graphics processing unit

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Plant Diseases Detection Using Deep learning and machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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The configuration optimization of the plants in vertical farming by 3D modeling 3

The configuration optimization of the plants in vertical far...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Tan, Kege Zhang, Ying Department of Architecture School of Architecture and Design Southwest Jiaotong University Chengdu611756 China Department Geography Planning and Environment Faculty of Arts and Science Concordia University Montreal Canada

Vertical farming, a terminology in landscape architecture, is designed to cater for the demand of vegetation under the impact of excessive exploid of land, especially in densely-populated areas. The study takes vertical farming in Singapore as an example, applies 3D modeling to investigate the effect of different arrangement patterns on plant light absorption in vertical farms, and proposes some measures. Through the 3D simulation process, each square of the soil for planting can be quantified in sunlight-exposure hour. The experiment result shows that configuration with an array piling up is judicious and feasible for greenhouse growth, especially for commercial usages. Different configurations of plants influence the efficiency of utilizing sunlight, and there is a better layout for a particular site in different usages. For some cities far away from tropical areas, this configuration may alleviate the impact of lacking sunlight exposure. The main advantage of vertical farms is efficiency, as they require less land and water with more yield per acre compared to conventional farms. © Published under licence by IOP Publishing Ltd.

关键词： Tropics

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