检索结果-内蒙古大学图书馆

Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic-Matrix-Encryption Based Secure Strong PUF for Device Authentication Protocols 17

Dynamic-Matrix-Encryption Based Secure Strong PUF for Device...

引用

17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Zhao, Liangxiao Li, Gang Wang, Pengjun Ma, Xuejiao Zhou, Ziyu The College of Electrical and Electronic Engineering Wenzhou University Wenzhou325000 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325035 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Strong physical unsolvable function (PUF) has a wide range of applications in internet of things (IoT) security. However, it is vulnerable to machine learning (ML) modeling attacks. This paper proposes a strong PUF anti-ML attack method based on dynamic-matrix encryption. It obfuscates the challenge by multiplying the dynamic encryption matrix (DEM) with the challenge matrix synchronizing initial values during the registration phase of the device security authentication protocol prevents our obfuscation from being invalidated when the algorithms and structures motivating the obfuscation are made public. Each round of DEM is updated with respect to each previous round of challenge which is similar to the properties of sequential logic circuits in that the current response does not depend only on the current challenge but is related to previous challenge. Experimental results show that even if an attacker collects 1 million challenge response pairs, the prediction accuracies of the several ML attacks we use are below 53%, with negligible effects on randomness, reliability, and uniqueness. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

来源：评论

学校读者我要写书评

暂无评论

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

arXiv

引用

arXiv 2023年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science UniVie Doctoral School Computer Science DoCS Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems’ adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants’ thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants’ perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems. © 2023, CC BY.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

arXiv

引用

arXiv 2023年

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" [24] framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach. © 2023, CC BY.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases 8

A Novel CAD Framework with Visual and Textual Interpretabili...

引用

8th International Artificial Intelligence and data Processing Symposium, IDAP 2024

作者： Mukhlis, Raza Saleem, Saied Kwon, Hyunwook Hussain, Jamil Aydin, Ahmet Arif Al-Antari, Mugahed A. Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of Inonu University Faculty of Engineering Department of Computer Engineering Malatya Turkey

ISBN: (纸本)9798331531492

Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: data Collection and Annotation, data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of 70 %, precision of 70 %, and F1-score of 0.60 %, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Smart Grid Application: Addressing data Scarcity Challenges and Enhancing Load Forecasting Efficiency

Deep Learning for Smart Grid Application: Addressing Data Sc...

引用

International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Amalou, Ibtissam Mouhni, Naoual Abdali, Abdelmounim Jakimi, Abdeslam Tourad, Mohamedou Cheikh CISIEV FSTG University Cadi Ayyad Marrakech Morocco LAMIGEP EMSI Marrakech Marrakech Morocco GL-ISI Team Faculty of Science and Technology Errachidia Moulay Ismail University Meknes Morocco Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania

ISBN: (纸本)9783031714252

The surge in domestic electricity consumption necessitates innovative solutions for the integration of smart grids. Traditional automation methods prove insufficient for handling unforeseen challenges and sustainability issues, especially with the increasing adoption of renewable energy sources. This paper reviews the application of deep learning (DL) methods to smart grids. The research explores various DL approaches employed in smart grid data collection, such as generative adversarial networks, convolutional neural networks (CNNs), auto-encoders, restricted Boltzmann machines, and multilayer perceptron models. These techniques aim to refine features, detect electricity theft, and identify patterns related to false data injection attacks and energy consumption. data scarcity emerges as a critical challenge impacting smart grid data management, affecting forecasting accuracy, operational insights, efficiency, and innovation. The study advocates for investments in advanced data collection technologies and taking advantage of machine learning techniques to optimize the use of available data. To demonstrate the impact of data scarcity on DL model efficiency, a Machine learning benchmark models are trained for energy consumption forecasting. Results reveal the significance of training models with sufficient data reaching a minimization of 3.1149 for validation mean squared error of one of the models (CNN_LSTM) by increasing the use of training data to 75% of the Individual household electric power consumption dataset, emphasizing the need to address data scarcity challenges for effective model training. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

引用

SN computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Gamification using Octalysis Framework in Knowledge Management System for Vocational High Schools during the Covid-19 Pandemic 7

Gamification using Octalysis Framework in Knowledge Manageme...

引用

7th International Conference on Informatics and Computing, ICIC 2022

作者： Firdaus, Mgs. Afriyan Indah, Dwi Rosa Sazaki, Yoppy Ariefin, Eka Prasetyo Nuriza, Muhammad Fachri Rafly, Muhammad Universitas Sriwijaya Faculty of Computer Science Information System Department Palembang Indonesia Universitas Sriwijaya Data Structure and Accounting Information System Laboratory Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Informatics Management Department Palembang Indonesia

ISBN: (纸本)9798350345711

Knowledge management systems enable teachers and students to interact directly in the knowledge transfer process during the Covid-19 pandemic. In addition, applying the gamification concept in vocational disciplines could enhance the quality of teaching to increase motivation and student learning outcomes which tend to decline. Octalysis Framework, which supports the gamification concept, consists of eight cores that focus on creativity, self-expression, social dynamics, logic, thinking, and ownership depicted in an octagon making an application more fun and helpful to motivate users to enjoy and be involved in an activity. This condition is crucial and urgent in the Covid-19 pandemic that requires online and student-centered learning. This study discussed the application of Octalysis Framework-based Gamification in a computer Learning Knowledge Management system in Vocational High Schools during the Covid-19 Pandemic. The Gamification concept with the Octalysis Framework was implemented in the Knowledge Management System. Some game mechanics in a non-game context consisting of four phases, namely the Discovery, Onboarding, Scaffolding, and Endgame was applied to improve student performance and motivation in learning. Meanwhile, The Knowledge Management System software achieved an overall quality score of 5.14 out of 6 (85.6 percent of quality achieved) covering functionality, reliability, usability, efficiency, maintainability, and portability characteristics of the ISO 9126 quality indicator. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

An Exploration of Interpolation - Machine Learning Model for Climate Model Downscaling Under the Limitation of data Quantity

An Exploration of Interpolation - Machine Learning Model for...

引用

2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Prathom, Chotirose Champrasert, Paskorn Chiang Mai University Data Science Consortium Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai Thailand

ISBN: (纸本)9798350326413

In order to response to climate change, climate model is essential for future prediction, which is advantage for adaptation planning. With the fact that the output of the climate model is in a coarse spatial resolution, it is poorly representing data on local level and requires a downscaling process to increase its resolution. To perform the downscaling, high amount of historical observed data is necessary for calibrating the model output. However, the data may not be available in some area, also Thailand. To overcome the limitation of data Quantity, four combination methods between two interpolation (Inverse Distance Weight and Triangular Irregular Network) and two machine learning models (Artificial Neural Network and Gradient Boosting Regression Tree) are experimented to explore the proper one for downscaling data on Thailand area. By considering the performance of both interpolation and machine learning model, the combination between Inverse Distance Weight and Artificial Neural Network shows the best performance in down scaling process under data Quantity limitation. © 2023 IEEE.

关键词： Climate models

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：