检索结果-内蒙古大学图书馆

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Isnan, Mahmud Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Lactose intolerance is a type of digestive problem that may threaten the population because milk and dairy products compose of nutrients that are essential for human body. Genetic tests possess a great potential to detect lactose intolerance as it can be used in children and even infants. However, a new approach to analyze the genetic test results is needed to elucidate the Single Nucleotide Polymorphisms (SNPs) that are related to lactose intolerance. In this work, we utilized the machine learning based feature selection to select the SNPs associated with lactose tolerance trait from genotyping samples of direct-to-customer (DTCG genetic tests, obtained from the public database. Recursive Feature Elimination (RFE) with XGBoost model was used to perform feature selection. We also compared three different models, such as XGBoost, support vector machine (SVM), and random forest (RF) for training the selected features. Our findings revealed that 20 SNPs (out of 3501) were chosen, with rs4394668 as the most important variables (F-score 0.009). Furthermore, when compared to the RF and SVM models, the XGBoost model had the highest accuracy (0.87). Further studies should be undertaken to elucidate how the selected SNPs may lead to the lactose intolerance trait. © 2023 The Authors. Published by Elsevier B.V.

关键词： feature selection genetic test lactose intolerance SNP

来源：评论

学校读者我要写书评

暂无评论

An integrated framework based deep learning for cancer classification using microarray datasets

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2249-2260页

作者： Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. Johor Johor Bahru81310 Malaysia Johor Skudai81310 Malaysia Department of Computer Science and IT University of Sargodha Sargodha40100 Pakistan Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of Information Technology Al Istiqlal University Jericho4728 Palestine Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator

arXiv

引用

arXiv 2024年

作者： Mukherjee, Amartya Stadt, Melissa M. Podina, Lena Kohandel, Mohammad Liu, Jun Department of Applied Mathematics University of Waterloo Canada Department of Computer Science University of Waterloo Canada

Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to partial differential equations (PDEs). However, they still struggle with inverse problems in the Laplacian operator, for instance, the Poisson equation, because the eigenvalues that are large in magnitude amplify the measurement noise. This paper presents a novel approach for the inverse and forward solution of PDEs through the use of denoising diffusion restoration models (DDRM). DDRMs were used in linear inverse problems to restore original clean signals by exploiting the singular value decomposition (SVD) of the linear operator. Equivalently, we present an approach to restore the solution and the parameters in the Poisson equation by exploiting the eigenvalues and the eigenfunctions of the Laplacian operator. Our results show that using denoising diffusion restoration significantly improves the estimation of the solution and parameters. Our research, as a result, pioneers the integration of diffusion models with the principles of underlying physics to solve PDEs. © 2024, CC BY.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids

引用

Discover Internet of Things 2023年第1期3卷 3页

作者： Manzoor, Awais Judge, Malik Ali Islam, Saif ul Neggaz, Nabil Abulaigh, Laith Ahmad, Ishfaq Department of Computer Science COMSATS University Islamabad Islamabad44550 Pakistan Department of Computer Sciences Institute of Space Technology Islamabad Pakistan Faculty of Mathematics and Computers University of Sciences and Technologies-USTO-MB Oran Algeria Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively. © The Author(s) 2023.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Bifurcation of Small Amplitude Dust-Acoustic Waves in Two-Temperature Ions Dissipative Plasmas 2nd

Bifurcation of Small Amplitude Dust-Acoustic Waves in Two-...

引用

2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Tamang, Jharna Pradhan, Manisha Prasad, Punam Kumari Department of Mathematics Sikkim University Sikkim Gangtok India Department of Mathematics School of Applied and Pure Sciences Sikkim Alpine University Kamrang Namchi India School of Computer Science Inspiria Knowledge Campus Himachal Vihar Phase-II West Bengal Siliguri Matigara734010 India

ISBN: (纸本)9783031668739

A study of small amplitude dust-acoustic waves is considered in a dissipative plasma system that is composed of mobile dust particles, q-nonextensive distributed cold and hot ions, and immobile electrons. The study is approached within the framework of the nonlinear Burgers equation and the modified Burgers equation, derived through the reductive perturbation technique. Using the wave transformation, the Burgers equation and the modified Burgers equation are transformed into the corresponding dynamical systems. Through the concept of planar dynamical systems, the bifurcation of dust-acoustic waves is studied using the phase plane analysis. By varying the q-nonextensive distribution parameter and the coefficient of viscosity, the changes in the shock waves and periodic waves are examined using numerical simulation. This study may be applicable to understand the dynamics of the dust-acoustic shock and periodic waves in q-nonextensive distributed cold and hot ions that may occur in dissipative plasmas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Shock waves

来源：评论

学校读者我要写书评

暂无评论

Enhancing Deep Reinforcement Learning with Executable Specifications

Enhancing Deep Reinforcement Learning with Executable Specif...

引用

International Conference on Software Engineering - Companion, ICSE, Companion

作者： Raz Yerushalmi Department of Computer Science and Applied Mathematics the Weizmann Institute of Science Israel

Deep reinforcement learning (DRL) has become a dominant paradigm for using deep learning to carry out tasks where complex policies are learned for reactive systems. However, these policies are “black-boxes”, e.g., opaque to humans and known to be susceptible to bugs. For example, it is hard - if not impossible - to guarantee that the trained DRL agent adheres to specific safety and fairness properties that may be required. This doctoral dissertation's first and primary contribution is a novel approach to developing DRL agents, which will improve the DRL training process by pushing the learned policy toward high performance on its main task and compliance with such safety and fairness properties, guaranteeing a high probability of compliance while not compromising the performance of the resulting agent. The approach is realized by incorporating domain-specific knowledge captured as key properties defined by domain experts directly into the DRL optimization process while leveraging behavioral languages that are natural to the domain experts. We have validated the proposed approach by extending the AI-Gym Python framework [1] for training DRL agents and integrating it with the BP-Py framework [2] for specifying scenario-based models [3] in a way that allows scenario objects to affect the training process through reward and cost functions, demonstrating dramatic improvement in the safety and performance of the agent. In addition, we have validated the resulting DRL agents using the Marabou verifier [4], confirming that the resulting agents indeed comply (in full) with the required safety and fairness properties. We have applied the approach, training DRL agents for use cases from network communication and robotic navigation domains, exhibiting strong results. A second contribution of this doctoral dissertation is to develop and leverage probabilistic verification methods for deep neural networks to overcome the current scalability limitations of neural network verificat

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification 6

Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

引用

6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Liu, Shu Wu, Qiang Yang, Xin Computational and Data Science PhD Program Middle Tennessee State University MurfreesboroTN United States University of Tennessee Department of Mathematics KnoxvilleTN United States Middle Tennessee State University Department of Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350396133

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Developer-Centric Task Allocation: An Intelligent Approach for Enhanced Software Productivity 15

Developer-Centric Task Allocation: An Intelligent Approach f...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Fensterseifer Filho, Evandro L. R. Assuncao, Joaquim V. C. Fontoura, Lisandra M. Federal University of Santa Maria - Ufsm Graduate Program in Computer Science RS Santa Maria Brazil Federal University of Santa Maria - Ufsm Applied Computing Department RS Santa Maria Brazil

ISBN: (纸本)9798350368833

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks. © 2024 IEEE.

关键词： Project management

来源：评论

学校读者我要写书评

暂无评论

Dermatologist-Level Classification of Skin Cancer with Level Set Method and Isolation Forest 3rd

Dermatologist-Level Classification of Skin Cancer with Lev...

引用

3rd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： Bellaj, Khalid Boujena, Soumaya Benmir, Mohammed Fundamental and Applied Mathematics Laboratory Department of Mathematics and Computer Sciences Ain Chock Science Faculty Hassan II University of Casablanca Km 8 Route El Jadida POB 5366 Maarif Casablanca Morocco

ISBN: (纸本)9789811962226

Cancer classification (CC) by machine learning (ML), a research area that combines ML and scientific computing techniques, has experienced considerable growth in recent years. This article reviews existing and emerging approaches at CC and presents some well-known results in a unified framework. We study the combination of segmentation methods (SM) with ML techniques for solving partial differential equations (PDE). As a concrete example of SM, improved by ML, we present a method that uses isolation forest (IF) to reduce the computational cost in our SM while maintaining accuracy. Finally, experimental results show that our method significantly outperforms existing solutions for CC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Wildcat: Educational RISC-V Microprocessors

arXiv

引用

arXiv 2025年

作者： Schoeberl, Martin Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

In computer architecture courses, we usually teach RISC processors using a five-stage pipeline, neglecting alternative organizations. This design choice, rooted in the 1980s technology, may not be optimal today, and it is certainly not the easiest pipeline for education. This paper examines more straightforward pipeline organizations for RISC processors that are suitable for educational purposes and for implementing embedded processors in FPGAs and ASICs. We analyze resource costs and maximum clock frequency of various designs implemented in an FPGA, using clock frequency as a performance proxy. Additionally, we validate these results with ASIC designs synthesized using the open-source SkyWater130 process. Contradictory to common wisdom, a longer pipeline (up to 5 stages) does not necessarily always increase the maximum clock frequency. In two FPGA and one ASIC implementation, we discovered that a four- or five-stage pipeline leads to a slower clock frequency than a three-stage implementation. The reason is that the width of the forwarding multiplexer in the execution stage increases with longer pipelines, which is on the critical path. We also argue that a 3-stage pipeline organization is more adequate for teaching a pipeline organization of a microprocessor. © 2025, CC BY.

关键词： Teaching

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：