检索结果-内蒙古大学图书馆

4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Khan, Kainat Katarya, Rahul Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665494144

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD. © 2023 IEEE.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits 38

Almost Minimax Optimal Best Arm Identification in Piecewise ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hou, Yunlong Tan, Vincent Y.F. Zhong, Zixin Department of Mathematics National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design Piecewise-Stationary Ε-Best Arm Identification+ (PSΕBAI+), an algorithm that is guaranteed to identify an Ε-optimal arm with probability ≥ 1 − δ and with a minimal number of samples. PSΕBAI+ consists of two subroutines, PSΕBAI and NAÏVE Ε-BAI (NΕBAI), which are executed in parallel. PSΕBAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PSΕBAI and NΕBAI are utilized judiciously in parallel, PSΕBAI+ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PSΕBAI+ is optimal up to a logarithmic factor. We compare PSΕBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PSΕBAI+ is due to the delicate change detection and context alignment procedures embedded in PSΕBAI. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

引用

computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

来源：评论

学校读者我要写书评

暂无评论

Exploring the Intersection of Blockchain and Distributed Quantum Computing: Synergies, Challenges, and Future Directions 2

Exploring the Intersection of Blockchain and Distributed Qua...

引用

2nd International Conference on Microwave, Antenna and Communication, MAC 2024

作者： Singh, Madhusudan Rawal, Bharat Long Island University Brooklyn Department of Data Analytics New York United States Granbling State University Dept. of Computer Science LA United States

ISBN: (纸本)9798350350104

Distributed quantum computing has become a potential way to solve hard computing problems by combining the power of many quantum devices that are connected to each other. But it's hard to make sure that distributed quantum computer nodes can trust each other, stay safe, and work together well. The decentralized and open nature of blockchain technology holds significant potential in addressing these challenges and revolutionizing the domain of distributed quantum computing. This scholarly essay examines the potential of blockchain technology in facilitating the coordination, resource sharing, and data management within distributed quantum computing networks, with a focus on ensuring safety and efficiency. We provide an overview of the primary advantages, challenges, and potential applications of integrating blockchain technology into distributed quantum computing frameworks. This has the potential to result in a quantum computing environment that is both more stable and scalable. The objective of this scholarly essay is to elucidate the potential impact of integrating blockchain technology into distributed quantum computing. Distributed quantum computing networks may effectively coordinate, share resources, and manage data by using the decentralized, transparent, and secure nature of blockchain technology. © 2024 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

引用

International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods 3rd

Automated Detection of Type 2 Diabetes with Imbalanced and M...

引用

3rd International Conference on Machine Learning, Image Processing, Network Security and data sciences, MIND 2021

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9789811958670

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Prediction of Rice Leaf Diseases at an Early Stage Using Deep Neural Networks 1

引用

8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Srilatha, Y. Doppala, Bhanu Prakash Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Data Analytics Generation Australia SydneyNSW Australia

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Rice is the major crop in India, and India has been the biggest exporter and the second-largest producer in the entire world, so it is heavily reliant on rice for its economy and food supply. There has been an increase in rice production from 53.6 million tons in fiscal to 120 million tons over the last 40 years. Healthy and proper rice plant growth is required to maintain food production and supply in accordance with people’s needs and demands. The major diseases affecting these rice plants are leaf blast, hispa, and brown spot. The advancement of deep learning paved the way for these diseases to be detected using computer vision. Many researches have been conducted in the detection of rice leaf diseases, where the rice leaves have been deeply affected at a later stage. In this chapter, we propose an effective deep learning model for an early-stage disease detection of rice leaves. The proposed model was developed by an ensemble of the pretrained DenseNet-201 with the Naïve Inception module. The model was trained and tested over a Rice Diseases Image dataset;it attained an overall disease detection accuracy of 87.69% and surpasseed the existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Food supply

来源：评论

学校读者我要写书评

暂无评论

A language model's guide through latent space 24

A language model's guide through latent space

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Dimitri von Rütte Sotiris Anagnostidis Gregor Bachmann Thomas Hofmann Data Analytics Lab Department of Computer Science ETH Zurich

Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work has largely been on truthfulness, in this paper we extend this framework to a richer set of concepts such as appropriateness, humor, creativity and quality, and explore to what degree current detection and guidance strategies work in these challenging settings. To facilitate evaluation, we develop a novel metric for concept guidance that takes into account both the success of concept elicitation as well as the potential degradation in fluency of the guided model. Our extensive experiments reveal that while some concepts such as truthfulness more easily allow for guidance with current techniques, novel concepts such as appropriateness or humor either remain difficult to elicit, need extensive tuning to work, or even experience confusion. Moreover, we find that probes with optimal detection accuracies do not necessarily make for the optimal guides, contradicting previous observations for truthfulness. Our work warrants a deeper investigation into the interplay between detectability, guidability, and the nature of the concept, and we hope that our rich experimental test-bed for guidance research inspires stronger follow-up approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals

引用

Frontiers of computer science 2024年第3期18卷 199-216页

作者： Antonio SANTOS-OLMO Luis Enrique SÁNCHEZ David G.ROSADO Manuel A.SERRANO Carlos BLANCO Haralambos MOURATIDIS Eduardo FERNÁNDEZ-MEDINA GSyA Research Group University of Castilla-La ManchaCiudad Real 13071Spain Institute for Analytics and Data Science University of EssexColchester CO43SQUK Alarcos Research Group University of Castilla-La ManchaCiudad Real 13071Spain ISTR Research group Department of Computer Science and ElectronicsUniversity of CantabriaSantander 39005Spain

The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information *** availability of these systems is now vital for the protection and evolution of ***,several factors have led to an increasing need for more accurate risk analysis *** are:the speed at which technologies evolve,their global impact and the growing requirement for companies to *** analysis processes must consequently adapt to these new circumstances and new technological *** objective of this paper is,therefore,to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management *** analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new *** paper also presents a summary of MARISMA,the risk analysis and management framework designed by our research *** basis of our framework is the main existing risk standards and proposals,and it seeks to address the weaknesses found in these *** is in a process of continuous improvement,as is being applied by customers in several European and American *** consists of a risk data management module,a methodology for its systematic application and a tool that automates the process.

关键词： information security management security system security risk assessment and management

来源：评论

学校读者我要写书评

暂无评论

Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms 8

Detection of Plant Disease Using Machine Learning and Deep L...

引用

8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Gollapudi, Kalyani Varigonda, Preethi Jancy, S. Pushpalatha, S. Marya, Viji Amutha Helen, Suji Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats Department of Data Analytics Chennai602105 India

ISBN: (纸本)9798350347791

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized. © 2023 IEEE.

关键词： Recurrent neural networks

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：