检索结果-内蒙古大学图书馆

CoverGAN: cover photo generation from text story using layout guided GAN

Soft Computing 2025年第1期29卷 405-423页

作者： Cheema, Adeel Naeem, M. Asif Department of Data Science and Artificial Intelligence National University of Computer and Emerging Sciences Islamabad Pakistan

Generating cover photos from story text is a non trivial challenge to solve. Existing approaches focus on generating only images from given text prompt. To the best of our knowledge, non of these approaches focus on generating cover photos from a text story. The paper addresses this issue by introducing multi-object image generation with text title from a text story. We split the problem into three steps:- understanding semantics of text story, predicting layout of objects, and generating a cover photo. At start, a semantic relation was encoded between text story objects using a Scene graph, then features from graph neural network were concatenated with single object features from scene graph to create an object layout. All of these features were then passed on to the image generating part. Image generation was further divided into two phases. In the first phase, the image is generated using a scene graph image generation model. While in the second phase, the results of first phase were further enhanced using image translation model conditioned on the object layout. In final phase we generated title of the given story based on generated image. In our experiments, we used custom dataset of text stories with three animal categories along with the COCO dataset. For the image generating part, we evaluated our approach with state of the art models known as scene_gen and sg2im. Our method generated high-resolution informative cover photo with story title by positioning the objects at right locations as specified in the text story. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

引用

Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Image Recognition for Wildlife Conservation

Image Recognition for Wildlife Conservation

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Sridharan, S. Yuvan Shankar, S. Sriram, M. Yashvanth, K.P. Department of Artificial Intelligence and Machine Learning Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natural ecosystem, including images captured through camera traps, drones, and other monitoring devices. This comprehensive and diverse dataset is then processed using advanced Image Processing Tools. A key strength of this methodology lies in its use of Machine Learning algorithms, particularly YOLO v8 (You Only Look Once) and Support Vector Machines (SVMs). These techniques are suitable for wildlife conservation because of their capacity to analyze complex visual data and detect patterns indicative of species presence, behavior, and habitat conditions. The paper outlines the application of YOLO v8 and SVMs to the collected image data, enabling accurate identification, real-time detection, and classification of wildlife species. This capability is essential for monitoring populations, tracking movements, and identifying potential threats, thus facilitating timely conservation actions. Furthermore, the research extends its evaluation by comparing the results of the Machine Learning approach with traditional ecological survey methods, offering a comprehensive assessment of the effectiveness of image processing in wildlife conservation. © 2025 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines

引用

Discover artificial intelligence 2024年第1期4卷 73页

作者： Ahishakiye, Emmanuel Kanobe, Fredrick Department of Networks Data Science and Artificial Intelligence Kyambogo University Kampala Uganda Department of Computer Science Kyambogo University Kampala Uganda

Background: Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods: In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results: Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions: The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources. © The Author(s) 2024.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis on Climate Change using Twitter data 2

Sentiment Analysis on Climate Change using Twitter Data

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Thenmozhi, M. Shubigsha, G. Sindhuja, G. Dhinakar, V. Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365269

The objective is to illuminate the nuanced and complex public opinion surrounding climate change conversations on Twitter. The work employs advanced machine learning methods and natural language processing techniques, notably Support Vector Machines (SVM), to conduct a global-scale sentiment analysis using a sizable dataset that was obtained from a reliable third-party source. This study's main goal is to pinpoint the various ways that emotion is expressed in the context of climate change conversation;both positive and negative expressions are taken into account. The authenticity and utility of the selected third-party dataset - obtained through Kaggle and made possible by a Canadian Innovation Foundation JELF grant awarded to Chris Bausch at the University of Waterloo - are critically evaluated. The SVM-based sentiment analysis demonstrates how well the chosen methodology reflects the complexity of sentiment in climate change debates on Twitter, with an exceptional F1 score of 0.70. The implications of the research include communication strategies for legislators, organizations seeking to reach a global audience, and climate change advocates. By utilizing an external dataset and applying the Support Vector Machines algorithm's sentiment analysis, this study advances our comprehension of the intricate relationship between the public's perspective of climate change and social media conversation. To sum up, this study shows the value of SVM in detecting subtleties of sentiment in huge databases, contributing significant new knowledge to the developing subject at the nexus of environmental consciousness and social media dynamics. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Quantum Enhanced AI to Predict the Traffic Flow 2

Quantum Enhanced AI to Predict the Traffic Flow

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thenmozhi, M. Abishake, S. Dhayal, R.S. Rohit Kanna, R.P. Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331534950

This research work proposes a quantum traffic flow prediction system based on the difficulty of traffic congestion in cities. The system uses real-time traffic data, details on weather condition, and the behavioral patterns of drivers flexibly by incorporating machine learning along with quantum algorithms as an integrated approach. The data is collected through different data collection instruments namely, traffic sensors, GPS devices, and weather stations, making the data set holistic for analysis. In this model, the basic traditional algorithms of Long Short- Term Memory (LSTM) and Convolutional Neural Networks (CNN) are applied together with quantum-based optimization approaches including the Quantum Genetic Algorithm (QGA) and Learning Vector Quantization (LVQ). The QGA component is involved in the process of feature selection and modeling parameters, LVQ operates on traffic patterns classification. Together, these quantum-enhanced algorithms increase system efficiency and ensure efficient input-output work, especially when working with extensive and variable data arrays. Hence, the present research captures the flow of the model development process and establishes the evaluation of the current model's efficiency over the conventional prediction models. Outcomes indicate considerable enhancements in predicting novelties and regulating congestion characteristic of the system's suitability for smart city structures and urban traffic applications. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

data science and artificial intelligence for Life science at the University of Trieste

Data Science and Artificial Intelligence for Life Science at...

引用

2023 Italia Intelligenza artificiale - Thematic Workshops, Ital-IA 2023

作者： Barbati, Giulia Borbussi, Luca Caravagna, Giulio Artificial Intelligence and Cyber Physical Systems Laboratory Department of Mathematics and Geoscience University of Trieste Italy Cancer Data Science Laboratory Department of Mathematics and Geoscience University of Trieste Italy

This document reports data science and artificial intelligence initiatives oriented towards Life sciences applications that are in place at the Trieste node for the national AIIS Laboratory. © 2023 Copyright for ... 详细信息

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

引用

Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

artificial intelligence Cultivation: Transforming Agriculture for a Smart and Sustainable Future 2

Artificial Intelligence Cultivation: Transforming Agricultur...

引用

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

作者： Fulkar, Bhushan Mendhe, Samir Patil, Pawan Department of Artificial Intelligence and Data Science Maharashtra Wardha India Maharashtra Wardha India Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798350327533

The growing global population is posing unprecedented challenges for the agriculture industry, requiring it to produce more food while also addressing resource constraints and environmental concerns. artificial intelligence (AI) has changed the game in agriculture by offering creative solutions to increase productivity, optimize resource use, and promote sustainability. artificial intelligence (AI) has been used in the agriculture sector more and more recently. A few of the problems the industry faces in attempting to maximize its produce are improper soil treatment, disease and pest infestation, the use of driverless tractors, big data requirements, and the knowledge gap between farmers and technology. This work provides an overview of the applications of artificial intelligence in crop, weed, and disease management. The proposed work aims to review various AI techniques, including artificial neural networks (ANN) and fuzzy logic (FL). © 2024 IEEE.

关键词： artificial intelligence Crop disease prediction Fuzzy logic and artificial neural network soil management weed management

来源：评论

学校读者我要写书评

暂无评论

Efficient and Exact Public Transport Routing via a Transfer Connection databas 17th

Efficient and Exact Public Transport Routing via a Transfer ...

引用

17th International Symposium on Combinatorial Search, SoCS 2024

作者： Abuaisha, Abdallah Wallace, Mark Harabor, Daniel Shen, Bojie Department of Data Science and Artificial Intelligence Monash University Australia

ISBN: (纸本)9781577358916

We explore the earliest arrival time problem in public transport journey planning. A journey typically consists of multiple scheduled public transport legs. The actual time required to transfer between these legs can substantially influence route planning. Therefore, we properly model transfers by incorporating their exact costs. We then introduce a novel oracle-based routing algorithm that constructs an efficient transfer database, considering the proposed transfer model. The database is leveraged online to quickly reconstruct the optimal journey in response to an earliest arrival time query. Our experimental results show that neglecting exact transfer costs often lead to either infeasible or suboptimal route plans. Furthermore, the findings highlight the efficiency of our algorithm in handling queries, demonstrated by response times within mere microseconds. © 2024, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Query processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：