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检索条件"丛书名=Communications in Computer and Information Science"

共 43057 条记录，以下是571-580 订阅

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Automated Segmentation of Ischemic Stroke Lesions in Non-contrast Computed Tomography Images for Enhanced Treatment and Prognosis 1st

Automated Segmentation of Ischemic Stroke Lesions in Non-con...

引用

1st MICCAI Meets Africa Workshop

作者： Musah, Toufiq Adjei, Prince Ebenezer Otoo, Kojo Obed Kwame Nkrumah Univ Sci & Technol Dept Comp Engn Kumasi Ghana Kumasi Ctr Collaborat Res Trop Med Kumasi Ghana Komfo Anokye Teaching Hosp Dept Radiol Kumasi Ghana

ISBN: (纸本)9783031791024;9783031791031

Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming strokes and their sub-types, the NCCT, is more challenging and time-consuming to employ in cases of ischemic stroke. For this reason, we developed an automated method for ischemic stroke lesion segmentation in NCCTs using the nnU-Net framework, aimed at enhancing early treatment and improving the prognosis of ischemic stroke patients. We achieved Dice scores of 0.596 and Intersection over Union (IoU) scores of 0.501 on the sampled dataset. After adjusting for outliers, these scores improved to 0.752 for the Dice score and 0.643 for the IoU. Proper delineation of the region of infarction can help clinicians better assess the potential impact of the infarction, and guide treatment procedures.

关键词： Ischemic Stroke Acute infarcts Non-Contrast CT nnU-Net

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EnigmaArt: Dual Image Encryption and Compression via Autoencoding and Pixel Conversion 4th

EnigmaArt: Dual Image Encryption and Compression via Autoenc...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Tiwari, Naveen Kumar Rajput, Shyam Singh Yadav, Aditya Natl Inst Technol Patna Patna Bihar India Rajkiya Engn Coll Kannauj Kannauj UP India

ISBN: (纸本)9783031837890;9783031837906

Securing sensitive visual data is essential in the digital age. Our research presents a sophisticated model of image encryption that combines RNA pixel conversion with an autoencoder. A convolutional neural network (CNN) is used for efficient feature extraction. Convolutional encoding and RNA pixel conversion, which map pixels to RNA sequences (Adenine, Cytosine, Guanine, Uracil) enabling stronger encryption, are the model's primary innovations. By guaranteeing that every encryption instance is distinct, this dynamic technique strengthens security. Our approach ensures security and picture integrity by demonstrating robust encryption capabilities with minimum reconstruction loss (0.0617), as tested on the MNIST dataset. Furthermore, the encryption procedure is optimized by a fractional order optimizer, FGL Adam, which incorporates weight decay, learning rate modifications, and a security improvement factor.

关键词： Image Encryption Autoencoder Fractional Optimizer

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Semantic Abstractions for Multi-label Classification 4th

Semantic Abstractions for Multi-label Classification

引用

4th International Conference on Artificial Intelligence Logic and Applications

作者： Wang, Xiaomei Xuan, Xiaohua Xu, Qing Cai, Hua Shen, Weilin UniDT Shanghai Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Recent advances in multi-label classification (MLC) have achieved promising performance based on visual representations from convolutional neural networks. Since images for MLC usually contain complicated and abundant content, utilizing only visual information is insufficient and may deviate from global context. In this paper, we integrate textual representations, denoted as semantic abstractions, with visual features to comprehensively characterize the image content. With the multi-modal information, a novel Visual-Semantic (Vise) Transformer architecture is designed for investigating the ability of the proposed abstractive semantic representations. Furthermore, we inject global context information into each local visual features, which attempts to capture the significant and concrete expressions of regions under the premise of the whole image. Empirically, extensive experiments and comparisons are conducted on the challenging benchmarks of MSCOCO and NUS-WIDE datasets. The results demonstrate the superiority of our proposed method compared with prior works with significant performance improvement for multi-label classification task.

关键词： Multi-label classification Semantic abstractions

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Machine Learning for Waste-to-Energy: Optimization and Predictive Analytics 4th

Machine Learning for Waste-to-Energy: Optimization and Predi...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Rana, Sanskar Thakur, Narender Singh Jyoti, Divya Mahato, Dharamender Prasad Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837951;9783031837968

The burgeoning demand for sustainable energy sources has catalyzed interest in Waste-to-Energy solutions offering both environmental benefits and energy generation potential. This paper explores the integration of machine learning (ML) techniques to optimize and predict key aspects of the Waste-to-Energy process. The study leverages historical data from waste facilities incorporating variables such as waste composition, operational parameters, and environmental conditions. In this paper, machine learning models, including regression, classification, and ensemble methods, are employed to optimize combustion efficiency, predict energy output, and enhance the overall operational performance of Waste-to-Energy conversion process. Furthermore, the predictive analytics are employed to anticipate maintenance needs for mitigating the downtime and optimizing the resource allocation. The findings contribute to the growing field of the sustainable energy by showcasing the efficacy of machine learning in Waste-to-Energy systems providing a scalable and adaptive solution for the challenges inherent in this dynamic and complex process.

关键词： Combustion Efficiency Energy Output Prediction Operational Performance Waste Composition Machine Learning

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Identification of Diabetic Retinopathy from Retinography Images Using a Convolutional Neural Network 10th

Identification of Diabetic Retinopathy from Retinography Ima...

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10th International Conference on Technologies and Innovation

作者： Ulloa, Francisco Sandoval-Pillajo, Lucia Landeta-Lopez, Pablo Grande-Penafiel, Natalia Pusda-Chulde, Marco Garcia-Santillian, Ivan Univ Tecn Norte Ibarra Ecuador Univ Seville Seville Spain

ISBN: (纸本)9783031757013;9783031757020

Diabetic retinopathy (DR) is a prevalent eye disease in people with diabetes worldwide and can cause vision loss or blindness. Conventional diagnostic imaging requires time, effort and specific skills of ophthalmologists. This study proposes the use of a convolutional neural network (CNN) based on the ResNet152V2 architecture to automatically analyze color images of the retina of the eye and identify DR. The Knowledge Discovery in Databases (KDD) methodology was applied for data management and analysis. Datasets of RGB images were acquired, both private from the Ecuadorian Diabetes Association (EDA) and public (EyePACS) available on the Internet. Training and validation of the model were performed with Python, the TensorFlow framework and the Keras library. The results showed that the model has an accuracy in DR identification of 80% comparable to that of ophthalmologists (specialists), showing a statistically significant association according to the chi-square test and a very high Spearman correlation (rho = 0.857). This resulted in a high concordance between both evaluations (model vs. specialists). In addition, the CNN model significantly reduced the manual DR diagnosis time from 5-10 min to 15-30 s. The implementation of this tool could potentially improve the diagnosis of DR and the prescription of appropriate clinical treatments for affected patients.

关键词： diabetic retinopathy retinography CNN ResNet Deep learning Tensorflow-Keras

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Reducing Intuitive-Physics Prediction Error Through Playing 5th

Reducing Intuitive-Physics Prediction Error Through Playing

引用

5th International Workshop on Active Inference

作者： Georgeon, Olivier L. de Montera, Beatrice Robertson, Paul UCLy UR CONFLUENCE Sci & Humanites EA 1598 Lyon France CNRS LIRIS SyCoSMA Villeurbanne France INRAE MetaGenoPolis Unit Jouy En Josas France DOLL Labs Lexington MA USA

ISBN: (纸本)9783031771378;9783031771385

We present a mobile robot that autonomously generates behaviors to calibrate its intuitive-physics engine, also known as the "Game Engine in the Head" (GEITH). Most POMDP and Active Inference learning techniques operate in a closed world in which the set of states is defined a priori. However, implementing an "innate" GEITH and a set of interactive behaviors allowed us to avoid these limitations and design a mechanism for information search and learning in an open world. The results show that over a few tens of interaction cycles, the robot's prediction errors decrease, which shows an improvement in the GEITH calibration. Moreover, the robot generates behaviors that human observers describe as playful.

关键词： Active inference developmental learning enaction intrinsic motivation robotics core knowledge

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Towards Better Modeling of Bicycle Lateral Motion: Model Development, Naturalistic Data Acquisition, and Model Validation 12th

Towards Better Modeling of Bicycle Lateral Motion: Model Dev...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Alazemi, Fahd Fadhloun, Karim Rakha, Hesham Virginia Tech Virginia Tech Transportat Inst 3500 Transportat Res Plaza Blacksburg VA 24061 USA

ISBN: (纸本)9783031709654;9783031709661

This research effort starts by developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycles. In that regard, the research team extends the Fadhloun-Rakha bicycle-following longitudinal motion model through complementing it with a lateral motion strategy;thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except for the activation conditions of the collision avoidance strategy, which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions. In fact, by having the FR bicycle-following model as the governing module of longitudinal behavior and a dynamic lateral module, the proposed model is found to be both robust and able to model bicyclist behavior variability. Furthermore, it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. Next, this study describes a new framework for the collection of naturalistic cycling data. In that process, a new naturalistic cycling dataset is collected for the purpose of validating the developed bicycle lateral motion model. Given that the collection of naturalistic cycling data is not achievable in the traditional vehicle approach, machine learning and computer vision techniques were used to construct the naturalistic dataset from existing video feeds

关键词： Lateral bicycle motion model Naturalistic cycling behavior Data collection computer vision

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Image Processing Based Wood Defect Detection 2nd

Image Processing Based Wood Defect Detection

引用

2nd International Conference on information Technologies and Their Applications

作者： Ozkan, Merve Ozcan, Caner Karabuk Univ Inst Postgrad Educ Comp Engn Karabuk Turkiye Karabuk Univ Software Engn Fac Engn Karabuk Turkiye

ISBN: (纸本)9783031734199;9783031734205

Detection of defects in wooden structures in the forestry industry has become a crucial area of research. Existing studies have focused on specific categories of wood defects, failing to provide a comprehensive classification for high-quality wood. Trained human operators currently perform a variety of wood quality in wood processing facilities. However, this human-dependent process leads to time and performance losses and inaccurate type. This study aims to address all these challenges in future intelligent production systems by targeting the detection of the fungus in oak wood, one of the wood defect classes. The algorithm created based on image processing utilizes median filtering, Canny edge detection, and masking technologies using the HSV color space. The algorithm then calculates the fungal area ratio to the wooden piece's surface area on the masked image to reach the final result. While existing studies in the literature are primarily based on deep learning methods, there has been limited focus on fungus detection. The novelty of this study, conducted on oak wood, lies in its use of a specific dataset, fungal detection, and image processing. An algorithm has been developed and presented in the literature that can be used in the software of future intelligent production systems in the forestry industry.

关键词： Image Processing Wood Material Object Detection Canny Edge Detection HSV

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Representation Dimension of Quantitative Reasoning and Cooperative Learning 4th

Representation Dimension of Quantitative Reasoning and Coope...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ricra-Mayorca, Juan Manuel Garcia-Chauca, Jeniffer Doris Romero-Flores, Lisseth Angela Technol Univ Peru Lima Peru Private Univ North Lima Peru San Martin de Porres Univ Lima Peru

ISBN: (纸本)9783031832062;9783031832079

The aim of this study was to establish the correlation between cooperative learning and the representational dimension of quantitative reasoning in university students. The study is based on a quantitative research approach, a correlational level and a non-experimental cross-sectional design. The sample consisted of 121 students from a private university in Lima. To evaluate cooperative learning, a questionnaire composed of 29 questions was used. On the other hand, to evaluate the representation dimension of quantitative reasoning, an evaluation was carried out through a real context situation using a rubric. The results indicated a significance level of 0.000, which led to the conclusion that there is a significant relationship between cooperative learning and the representation dimension of quantitative reasoning.

关键词： Learning cooperative learning quantitative reasoning representation

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Kisaan Margadarshak: An AI-Based Decision Support System for Farmers in India 2nd

Kisaan Margadarshak: An AI-Based Decision Support System for...

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2nd International Conference on Agriculture-Centric Computation

作者： Trimukhe, Aryan Gokarakonda, Asish Khandeparkar, Kedar Anirudh, D. Dolli, Shrishail Indian Inst Technol Dharwad Dharwad Karnataka India Univ Agr Sci Dharwad Karnataka India

ISBN: (纸本)9783031744396;9783031744402

Crop health and market price play a crucial role in the yield and profitability of small-scale farming. The lack of counselling facilities, logistical costs associated with diagnosis, and overall health monitoring of the farm result in sub-optimal management of the farm. This paper presents an android-based decision support system (DSS) for farmers, Kisaan Margadarshark developed to monitor crop health from camera images of the crops sent periodically by the registered farmers. Using deep learning algorithms, the system assesses crop health by detecting pest attacks, diseases, deficiencies, etc., and provides standard practices to be adopted by the farmer. The application also provides the farmer with both current and forecasted prices in the neighbourhood markets for better planning the harvesting of the yield. The application is farmer-friendly and provides multi-language support. The application also provides the farmer with current and forecasted weather information for planning during phases from crop sowing to harvesting.

关键词： AI/ML Crop Health Mobile Application

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