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共 35802 条记录，以下是101-110 订阅

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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Next-Generation Traffic control: Adaptive Timer and Emergency Vehicle Priority in Intelligent Traffic Management

Next-Generation Traffic Control: Adaptive Timer and Emergenc...

引用

2024 International Conference on E-Mobility, Power control and Smart Systems, ICEMPS 2024

作者： Devika, S.G. Govind, A. Lekshmi, D. Sct College of Engineering Dept. of Computer Science Engineering Trivandrum India

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394399

Traffic has always been a troubling issue in our society. The increased number of automobiles on roads has sub-stantially increased traffic congestion polluting the environment and causing stress and anxiety to passengers in the traffic clog. The existing system of fixed traffic control system even though widely used, lacks adaptability and operates on predefined signal timers. The lack of adaptability can lead to inefficient traffic flow, especially during high traffic congestion. To address this issue, we propose a more adaptive and intelligent system that leverages adaptive timer control and prioritizes emergency vehicles like, ambulance. The adaptive timer control system considers the count of vehicles at the previous junction as well as the count of vehicles currently arriving at the intersection to update the signal timers. Additionally, to prevent delays in emergency response times and to provide a better impact on public safety and health outcomes we have provided an added feature to prioritize emergency vehicles and release the traffic upon its arrival at the respective signal. The detection of the emergency vehicle is to be carried out using the YOLO v8(You Look Only Once) algorithm along with siren recognition using a classifier model. The proposed system offers a significant improvement over the existing traffic control infrastructures and has the potential for aiding resilient and smooth transportation. © 2024 IEEE.

关键词： Machine learning

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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features

Sparse Oblique Decision Trees: A Tool to Understand and Mani...

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KDD Workshop on Human-Interpretable AI 2024, HI-AI 2024

作者： Hada, Suryabhan Singh Carreira-Perpiñán, Miguel Á. Zharmagambetov, Arman LinkedIn Dept. of Computer Science & Engineering University of California Merced United States Dept. of Computer Science & Engineering University of California Merced United States

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding what internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the net with a decision tree having sparse weight vectors at the nodes. We are able to learn trees that are both highly accurate and interpretable, so they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. We demonstrate this robustly in MNIST and ImageNet with LeNet5 and VGG networks. © 2024 Copyright for this paper by its authors.

关键词： Deep neural networks

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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An Approach for XAI Visualizations for Explainability of Alzheimer's Detection 2

An Approach for XAI Visualizations for Explainability of Alz...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Khanapur, Shraddha Bharadwaj, Chirag B. Bhardwaj, Raghav Nayak, Jyothi S. BMS College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367461

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer's disease. However, the opacity of these models, often termed as "black boxes,"raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Species Density Analysis Using Deep Learning

Species Density Analysis Using Deep Learning

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Rekha, K.S. Shamlin, A Harini Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

The primary objective of this project is to develop a machine learning model, specifically Convolutional Neural Network (CNN), to count the density of several particular species in an audio clip. The model is trained on a dataset containing calls of the specified species and other species. This innovation has significant implications for wildlife monitoring and ecological research, as it can help researchers track and understand the behavior of organisms in forest regions more efficiently. Manual effort in audio analysis can be reduced by machine learning, thereby saving time. By improving and broadening techniques, algorithms, and training methodologies, researchers can uncover more about animal behavior and contribute to our collective understanding of ecosystems. The application of this technology can be useful for analyzing the total number of species in a certain rainforest region, eventually reducing the burden on researchers and streamlining the entire process. Overall, this project has immense potential to pave the way for more efficient and accurate wildlife monitoring and ecological research that can ultimately benefit our planet's biodiversity. © 2024 IEEE.

关键词： Forest ecology

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TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions

TexAVi: Generating Stereoscopic VR Video Clips from Text Des...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Jayaraman, Shruti Bhavya, R. Srihari, Vriksha Mary Anita Rajam, V. College of Engineering Dept. of Computer Science and Engineering Guindy Chennai India

ISBN: (纸本)9798350376876

While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic dept. and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with dept. estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.

关键词： Virtual environments

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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

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1st IEEE International Conference on Smart Power control and Renewable Energy, ICSPCRE 2024

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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