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Optimization of machine learning and Deep learning Algorithms for Diagnosis of Cancer 1

Optimization of Machine Learning and Deep Learning Algorithm...

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1st international Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Krishna, Hari Anand, M. Saravanan, D. Pushpalatha, K. Department of ECE B V Raju Institute of Technology Telangana India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of CSE IFET College of Engineering Villupuram India Department of Mathematics KoneruLakshmaiah Educational Foundation Vadedeswaram India

ISBN: (纸本)9781607685395

machine learning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in pattern recognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge data sets for medical pictures. An introduction to deep learning in medical image processing, from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a collection of computer science advances. The next step was to learn the basics of neural networks. That explains the use of deep learning and CNNs. So we can see why deep learning is rapidly advancing in various application fields, including medical image processing. The goal of this research was to use innovative methodologies on cancer datasets to explore the feasibility of combining machine learning and deep learning algorithms for cancer detection. This study used text and picture databases to classify cancer. The datasets are the Liver BUPA disorder database and brain MRI pictures. This article provides optimization methods that outperform the suggested approaches' accuracy. Using two alternative training methods, Levenberg Marquardt (lm) and Resilient back propagation (rp), two classification algorithms were evaluated with different groups of neurons to identify benign and malignant patients. Cascade correlation utilizing the train (rp) outperformed feed forward back propagation using the train (lm). The second deep neural network model presented a technique (based on CNN) for automated brain tumour identification using MRI data. The Water Cycle Algorithm is used to optimise CNN. The established approach is very accurate. The suggested framework examines innovative texture classification algorithms using Discrete Wavelet Transform (DWT) and the Gray Level Co occurrence

关键词： Artificial Neural Network Batch Normalization Deep learning Gray Level Co-occurrence Matrix Water Cycle Algorithm

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Topological approach to Wikipedia page recommendation 1

Topological approach to Wikipedia page recommendation

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Opirskyi, Maksym Sarkanych, Petro Ukrainian Catholic University Lviv Ukraine Institute for Condensed Matter Physics Lviv Ukraine Centre for Fluid and Complex Systems Coventry University CoventryCV1 5FB United Kingdom L4 Collaboration and Doctoral College for the Statistical Physics of Complex Systems Leipzig-Lorraine-Lviv-Coventry Europe Germany

Human navigation in information spaces has increasing importance in ever-growing data sources we possess. Therefore, an efficient navigation strategy would give a huge benefit to the satisfaction of human information needs. Often, the search space can be understood as a network and navigation can be seen as a walk on this network. Previous studies have shown that despite not knowing the global network structure people tend to be efficient at finding what they need. This is usually explained by the fact that people possess some background knowledge. In this work, we explore an adapted version of the network consisting of Wikipedia pages and links between them as well as human trails on it. The goal of our research is to find a procedure to label articles that are similar to a given one. Among others, this would lay a foundation for a recommender system for Wikipedia editors, which will suggest links from the given page to the related articles. Our work is therefore providing a basement for enhancing the Wikipedia navigation process making it more user-friendly. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Recommender systems

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1st international Conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019

1st International Conference on Applied Computing Research t...

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1st international Conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019

ISBN: (纸本)9783030387518

The proceedings contain 39 papers. The special focus in this conference is on Applied Computing Research to Support Industry: Innovation and Technology. The topics include: Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication;deep learning Approach Based Dominant Age Group Based Classification for Social Network;a Novel Detection System for Human Retina Based on Fuzzification Neural Network;a Novel Car-Pooling Optimization Method Using Ant Colony Optimization Based on Network Analysis (Case study: Tehran);A Trusted MANET Routing Algorithm Based on Fuzzy Logic;Minimum Array Elements and Snapshots for Performance Analysis Based on Direction Of Arrival (DOA) Estimation Methods;experimenting Two machine learning Methods in Classifying River Water Quality;multi View Face Detection in Cattle Using Infrared Thermography;deep learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory;Detection of Copy-Move Forgery in Digital Image Based on SIFT Features and Automatic Matching Thresholds;the Application of Artificial Intelligence Technology in Healthcare: A Systematic Review;Gain Scheduling Fuzzy PID Controller for Distributed Control Systems;comparative Analysis for Arabic Sentiment Classification;political Articles Categorization Based on Different Naïve Bayes Models;single Runway Aircraft Landing Scheduling Using Simulated Annealing and Exact Timing Method;towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions;prediction of Compressive strength of High-Performance Concrete: Hybrid Artificial Intelligence Technique;cancellable Face Biometrics Template Using AlexNet;pattern of Diffusion recognition in a Molecular Communication Model;querying Encrypted data in Graph databases;an Effective Protein Multiple structure Alignment Using Parallel Computing.

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Integrating machine learning and Biological Context for Single-Cell Gene Regulatory Network Inference 13th

Integrating Machine Learning and Biological Context for Sing...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Koumadorakis, Dimitrios E. Dimitrakopoulos, Georgios N. Exarchos, Themis P. Vlamos, Panagiotis Vrahatis, Aristidis G. Bioinformatics and Human Electrophysiology Laboratory Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9783031632266

Single-cell RNA sequencing (scRNA-seq) data analysis often involves inferring gene regulatory networks (GRNs) to understand the intricate regulatory relationships underlying cellular processes. While existing methods predominantly rely on mathematical models, they frequently lack integration with prior biological knowledge. This limitation is also evident in popular techniques that primarily employ correlation-based approaches for gene selection, disregarding important biological context encoded in Gene Ontology (GO) terms. To address this gap, we propose an innovative strategy that combines dataset-specific information with biological insights, including GO term annotations, to prioritize genes for GRN inference. By integrating the mathematical modeling of SCENIC with biologically relevant criteria, our approach enhances the accuracy and interpretability of inferred regulatory networks. Through extensive validation on a scRNA-seq dataset from type 2 diabetes donors, we demonstrate the efficacy of our method in capturing meaningful regulatory relationships. This integrative framework represents a significant advancement in single-cell GRN inference, offering a holistic approach for unraveling complex cellular regulatory mechanisms. © IFIP international Federation for Information Processing 2024.

关键词： RNA

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Adaptive Joint Attention with Reinforcement Training for Convolutional Image Caption 1st

Adaptive Joint Attention with Reinforcement Training for C...

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1st international workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th international Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Chen, Ruoyu Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789811513978

A convolutional decoder for image caption has proven to be easier to train than the Long Short Term Memory (LstM) decoder [2]. However, previous convolutional image captioning methods are not good at capture the relationship between generated words, which could lead to failure in predicting visually unrelated words. To address this issue, we propose an Adaptive Joint Attention (AJA) module for the convolutional decoder, by incorporating self-attention to explore the correlation between generated words automatically. Specifically, we develop a word gate with multi-head self-attention to directly capture words’ relationship. An adaptive combination strategy is proposed to learn which word is less related to image information. Besides, reinforcement learning is applied for directly optimizing the non-differentiable metrics while avoiding the exposure bias during inference. Extensive evaluations on two public data sets demonstrate that our method outperforms several state-of-the-art approaches in image captioning task. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Optimization of the SVM kernels using an empirical error minimization scheme 1st

Optimization of the SVM kernels using an empirical error min...

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1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： Ayat, Nedjem-Eddine Cheriet, Mohamed Suen, Ching Y. LIVIA ÉTS 1100 rue Notre Dame Ouest MontrealH3C 1K3 Canada CENPARMI Concordia University 1455 de Maisonneuve Blvd West MontrealH3G 1M8 Canada

ISBN: (纸本)354044016X

We address the problem of optimizing kernel parameters in Support Vector machine modelling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. for optimizing SVM kernels using an analytic upper bound of the error. However, our optimization scheme minimizes an empirical error estimate using a Quasi-Newton technique. The method has shown to reduce the number of support vectors along the optimization process. In order to assess our contribution, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIst digit image database. The method has shown satisfactory results with much faster convergence in comparison with the simple gradient descent method. Furthermore, we also experimented two more optimization schemes based respectively on the maximization of the margin and on the minimization of an approximated VC dimension estimate. While both of the objective functions are minimized, the error is not. The corresponding experimental results we carried out show this shortcoming. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Optimization

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Classifying Judging states from fMRI data of Visual recognition Task

Classifying Judging States from fMRI Data of Visual Recognit...

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international workshop on Complexity and data mining (IWCDM)

作者： Huangjing Ni Wei Wang Chaoyi Pang Jiayuan Yu Machine Learning and Cognition Laboratory Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China Australian e-Health Research Centre CSIRO Australia Department of Psychology Educational Science College Nanjing Normal University Nanjing Jiangsu China

Identifying the subject's simple judging states from fMRI data is the basis of studying complex logical relationship and has great theoretical significance. In this paper, we study judging states from fMRI data in terms of logical recognition classifications. We found that the ROI (Regions of Interest) regions played an important role in visual recognition task and identified what ROI regions were crucial for the correct judgments. We indicated that the correct and wrong judging states could be identified. In general, our work can answer the questions: (1) what are the judging states when one is performing a visual recognition task, and (2) whether the differences of brain activity pattern exist between the situations of correct and wrong judgments.

关键词： Visualization machine learning Accuracy Educational institutions Humans Support vector machines Brain

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Biomedical Engineering Systems and Technologies 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Claudine Gehin Bruno Wacogne Alexandre Douplik Ronny Lorenz Bethany Bracken Cátia Pesquita Ana Fred Hugo Gamboa

ISBN: (数字)9783031206641

ISBN: (纸本)9783031206634

This book constitutes the refereed proceedings of the 14th international Joint Conference on Biomedical Engineering Systems and Technologies, BIOstEC 2021, held virtually due to the COVID-19 crisis, in February 2021. The conference was held virtually due to the COVID-19 crisis.;The 13 full papers included in this book were carefully reviewed and selected from 265 submissions. The papers selected to be included in this book contribute to the understanding of relevant trends of current research on Biomedical Engineering Systems and Technologies, including: pattern recognition and machine learning, Application of Health Informatics in Clinical Cases, Evaluation and Use of Healthcare IT, Medical Signal Acquisition, Analysis and Processing, data mining and data Analysis, Decision Support Systems, e-Health, e-Health Applications, Mobile Technologies for Healthcare Applications and Medical Devices design.

关键词： Health Informatics Computer Communication Networks Computer System Implementation Computer Appl. in Social and Behavioral Sciences Artificial Intelligence Computer Imaging, Vision, pattern recognition and Graphics

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Building statistical Language Models of code

Building Statistical Language Models of code

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international workshop on data Analysis patterns in Software Engineering (DAPSE)

作者： Peter Schulam Roni Rosenfeld Premkumar Devanbu Language Technologies Institute Carnegie Mellon University USA Department of Computer Science University of California Davis USA

ISBN: (纸本)9781467362955

We present the Source Code statistical Language Model data analysis pattern. statistical language models have been an enabling tool for a wide array of important language technologies. Speech recognition, machine translation, and document summarization (to name a few) all rely on statistical language models to assign probability estimates to natural language utterances or sentences. In this data analysis pattern, we describe the process of building n-gram language models over software source files. We hope that by introducing the empirical software engineering community to best practices that have been established over the years in research for natural languages, statistical language models can become a tool that SE researchers are able to use to explore new research directions.

关键词： data models Vocabulary Smoothing methods Speech recognition Buildings Software engineering Natural languages

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Parallel and Sequential Optical Computing

Parallel and Sequential Optical Computing

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1st international workshop on Optical SuperComputing

作者： Woods, Darnien Naughton, Thomas J. Univ Seville Dept Comp Sci & Artificial Intelligence Seville Spain Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland Univ Oulu Oulu Southern Inst RFMedia Lab Ylivieska 84100 Finland

ISBN: (纸本)9783540856726

We present a number of computational complexity results for an Optical model of computation called the continuous space machine. We also describe an implementation for an optical computing algorithm that, can be easily defined within the model. Our optical model is designed to model a wide class of optical computers, such as matrix vector multipliers and pattern recognition architectures. It is known that the model solves intractable PSPACE problems in polynomial time, and NC problems in polylogarithmic time. Both of these results use large spatial resolution (number of pixels). Here we look at what happens when we have constant spatial resolution. It turns out that we obtain similar results by exploiting other resources, such as dynamic range and amplitude resolution. However, with certain other restrictions we essentially have a sequential device. Thus we are exploring the border between parallel and sequential computation in optical computing. We describe an optical architecture for the unordered search problem of finding a one in a list of zeros. We argue that our algorithm scales well, and is relatively straightforward to implement. This problem is easily parallelisable and is from the class NC. We go on to argue that the optical computing community should focus their attention on problems within P (and especially NC), rather than developing systems for tackling intractable problems.

关键词： Optical data processing

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