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Trends in digital image processing of isolated microalgae by incorporating classification algorithm

BIOTECHNOLOGY ADVANCES 2023年第1期63卷 108095页

作者： Chong, Jun Wei Roy Khoo, Kuan Shiong Chew, Kit Wayne Ting, Huong-Yong Show, Pau Loke Wenzhou Univ Zhejiang Prov Key Lab Subtrop Water Environm & Mar Wenzhou 325035 Peoples R China Univ Nottingham Malaysia Fac Sci & Engn Dept Chem & Environm Engn Jalan Broga Semenyih 43500 Selangor Darul Malaysia Yuan Ze Univ Dept Chem Engn & Mat Sci Taoyuan Taiwan Nanyang Technol Univ Sch Chem Chem Engn & Biotechnol 62 Nanyang Dr Singapore City 637459 Singapore Univ Technol Sarawak Drone Res & Applicat Ctr 1 Jalan Univ Sibu 96000 Sarawak Malaysia SIMATS Saveetha Sch Engn Dept Sustainable Engn Chennai 602105 India

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

关键词： Identification Machine learning image processing Microscopy Microalgae

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Research on Financial Early Warning Mechanism of Colleges and Universities Based on neural Network

Research on Financial Early Warning Mechanism of Colleges an...

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image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Yue Hu Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In this paper, the potential risks and early warning problems in the financial management of Chinese colleges and universities are deeply discussed. With the continuous expansion of the scale of higher education and the increasing complexity of the financial environment, the construction of a scientific, accurate and efficient financial early warning system has become an important issue in the financial management of colleges and universities. This paper first analyzes the main types and formation mechanism of financial risk in colleges and universities, and then introduces the theoretical basis and advantages of neural network model in detail, especially its powerful ability in dealing with nonlinear relations, pattern recognition and prediction, which provides new solutions and technical means for financial early warning in colleges and universities. On this basis, this paper designs and implements a college financial early warning model based on deep neural network. Through training and verification of a large number of college financial data, it realizes real-time monitoring and early warning of college financial risks. The empirical study shows that the model can effectively capture the internal correlation among various financial indicators and accurately predict the possible financial crisis, so as to provide timely and accurate risk decision-making basis for university management.

关键词： Training Accuracy Computational modeling Decision making artificial neural networks Predictive models Financial management Alarm systems Real-time systems Pattern recognition

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Toward memristive in-memory computing:principles and applications

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Frontiers of Optoelectronics 2022年第2期15卷 101-125页

作者： Han Bao Houji Zhou Jiancong Li Huaizhi Pei Jing Tian Ling Yang Shengguang Ren Shaoqin Tong Yi Li Yuhui He Jia Chen Yimao Cai Huaqiang Wu Qi Liu Qing Wan Xiangshui Miao School of Integrated Circuits School of Optical and Electronic InformationWuhan National Laboratory for OptoelectronicsOptics Valley LaboratoryHuazhong University of Science and TechnologyWuhan 430074China Hubei Yangtze Memory Laboratories Wuhan430205China AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong KongChina School of Integrated Circuits Peking UniversityBeijing 100871China School of Integrated Circuits Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China Frontier Institute of Chip and System Fudan UniversityShanghai 200433China School of Electronic Science and Engineering and Collaborative Innovation Centre of Advanced MicrostructuresNanjing UniversityNanjing 210093China

With the rapid growth of computer science and big data,the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and *** in-memory computing paradigm is considered as a prominent candidate to address these issues,and plentiful applications have been demonstrated and *** applications can be broadly categorized into two major types:soft computing that can tolerant uncertain and imprecise results,and hard computing that emphasizes explicit and precise numerical results for each task,leading to different requirements on the computational accuracies and the corresponding hardware *** this review,we conduct a thorough survey of the recent advances of memristive in-memory computing applications,both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms,and the hard computing type that includes scientific computing and digital image *** the end of the review,we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming artificial Intelligence of Things era.

关键词： Memristor In-memory computing Matrix-vector multiplication Machine learning Scientific computing Digital image processing

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Analysis of Impact of image Restoration and Segmentation on Classification Model

Analysis of Impact of Image Restoration and Segmentation on ...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sushma Rahul Vispute K. Rajeswari Aryan Nema Arya Jagtap Mrugendra Kulkarni Pranav Mohite Department of Computer Engineering PCCOE Pune India

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods.

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Design and Optimisations of Metal-Oxide artificial Synaptic Device Based Machine Learning Model

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年

作者： Yilmaz, Yildiran Gul, Fatih Recep Tayyip Erdogan Univ Dept Comp Engn TR-53100 Rize Turkiye Recep Tayyip Erdogan Univ Dept Elect & Elect Engn TR-53100 Rize Turkiye

Synaptic device-based neural network models are increasingly favored for their energy-efficient computing capabilities. However, as the demand for scalable and resource-efficient computing solutions continues to grow, there is a pressing need to explore novel computational paradigms inspired by the human brain. Motivated by the ongoing imperative to enhance the accuracy performance of hardware-based neural network models to compete with software-based counterparts, this paper investigates the potential of memristor-based nanodevice, particularly TiO2 synaptic device, as a promising candidate for hardware-based neural network models and seeks to improve accuracy performance. The innovation of this work lies in the comparative analysis of optimization methods to improve the classification accuracy of hardware-based neural network models using TiO2 synaptic device. By investigating various optimization functions, including SGD, Momentum, RMSProp, Adam, and Adagrad learning methods, this study aims to provide insights into the effectiveness of these methods in enhancing the accuracy of TiO2 synaptic device-based neural network models. Experimental results demonstrate that the choice of optimization method significantly impacts the accuracy of the models, with the Adam algorithm achieving the highest classification accuracy of 92.39% on the MNIST dataset, showcasing the potential of optimized hardware-based models to advance machine learning applications, particularly in image processing and character recognition.

关键词： Computational modeling Accuracy Biology Neurons Biological system modeling Optimization methods Biological neural networks Adam adagrad momentum RMSprop optimization learning methods SGD TiO2 memristor machine learning

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A survey of quaternion neural networks

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artificial INTELLIGENCE REVIEW 2020年第4期53卷 2957-2982页

作者： Parcollet, Titouan Morchid, Mohamed Linares, Georges Univ Avignon LIA Avignon France ORKIS Aix En Provence France

Quaternion neural networks have recently received an increasing interest due to noticeable improvements over real-valued neural networks on real world tasks such as image, speech and signal processing. The extension of quaternion numbers to neural architectures reached state-of-the-art performances with a reduction of the number of neural parameters. This survey provides a review of past and recent research on quaternion neural networks and their applications in different domains. The paper details methods, algorithms and applications for each quaternion-valued neural networks proposed.

关键词： Hypercomplex numbers Quaternion neural networks Deep Learning

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NEAR: A TRAINING-FREE PRE-ESTIMATOR OF MACHINE LEARNING MODEL PERFORMANCE

arXiv

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arXiv 2024年

作者： Husistein, Raphael T. Reiher, Markus Eckhoff, Marco Department of Chemistry and Applied Biosciences ETH Zurich Vladimir-Prelog-Weg 2 Zurich8093 Switzerland

artificial neural networks have been shown to be state-of-the-art machine learning models in a wide variety of applications, including natural language processing and image recognition. However, building a performant neural network is a laborious task and requires substantial computing power. neural Architecture Search (NAS) addresses this issue by an automatic selection of the optimal network from a set of potential candidates. While many NAS methods still require training of (some) neural networks, zero-cost proxies promise to identify the optimal network without training. In this work, we propose the zero-cost proxy Network Expressivity by Activation Rank (NEAR). It is based on the effective rank of the pre- and post-activation matrix, i.e., the values of a neural network layer before and after applying its activation function. We demonstrate the cutting-edge correlation between this network score and the model accuracy on NAS-Bench-101 and NATS-Bench-SSS/TSS. In addition, we present a simple approach to estimate the optimal layer sizes in multi-layer perceptrons. Furthermore, we show that this score can be utilized to select hyperparameters such as the activation function and the neural network weight initialization scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Aria App | LittleOne Edition, an All-in-One Application for processing Medical images and Developing U-Net Models

Aria App | LittleOne Edition, an All-in-One Application for ...

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IEEE International Symposium on Biomedical Imaging

作者： Ntaniel Rez Pantelis Asvestas Department of Biomedical Engineering University of West Attica Athens Greece

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The efficient segmentation of regions of interest is a crucial step for various medical applications, as it is widely used for monitoring disease progression and evaluating various conditions. However, the traditional manual segmentation by trained experts is a time consuming and repetitive process. Various approaches have been proposed in recent years such as and not limited to the use of neural networks and deep learning. Inspired by the achievements of artificial intelligence in demanding computer vision tasks we present the Aria App | LittleOne edition, featuring a GUI where image processing, network development and evaluation can be conducted. In this paper, we attempt to address the challenge of identifying retinal vessels in fundus images which is accomplished using the U-Net network. Experimental results conducted within the app on the DRIVE dataset demonstrate comparable performance to recent research with a 94.9% accuracy, 93.2% sensitivity, 95.1% specificity and 94.20% AUC in the total.

关键词： Deep learning Training Sensitivity neural networks Streaming media Retinal vessels Task analysis

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DNN acceleration in vehicle edge computing with mobility-awareness: A synergistic vehicle-edge and edge-edge framework

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COMPUTER networks 2024年 251卷

作者： Zheng, Yuxin Cui, Lin Tso, Fung Po Li, Zhetao Jia, Weijia Jinan Univ Dept Comp Sci Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Loughborough Univ Dept Comp Sci Loughborough England Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai Peoples R China BNU HKBU United Int Coll Zhuhai Peoples R China

In recent years, vehicular networks have seen a proliferation of applications and services such as image tagging, lane detection, and speech recognition. Many of these applications rely on Deep neural networks (DNNs) and demand low-latency computation. To meet these requirements, Vehicular Edge Computing (VEC) has been introduced to augment the abundant computation capacity of vehicular networks to complement limited computation resources on vehicles. Nevertheless, offloading DNN tasks to MEC (Multi-access Edge Computing) servers effectively and efficiently remains a challenging topic due to the dynamic nature of vehicular mobility and varying loads on the servers. In this paper, we propose a novel and efficient distributed DNN Partitioning And Offloading (DPAO), leveraging the mobility of vehicles and the synergy between vehicle-edge and edge- edge computing. We exploit the variations in both computation time and output data size across different layers of DNN to make optimized decisions for accelerating DNN computations while reducing the transmission time of intermediate data. In the meantime, we dynamically partition and offload tasks between MEC servers based on their load differences. We have conducted extensive simulations and testbed experiments to demonstrate the effectiveness of DPAO. The evaluation results show that, compared to offloaded all tasks to MEC server, DPAO reduces the latency of DNN tasks by 2.4x. DPAO with queue reservation can further reduce the task average completion time by 10%.

关键词： Vehicular edge computing Deep neural networks Task partitioning

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On Some Associations Between Mathematical Morphology and artificial Intelligence 1

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1st IAPR International Joint Conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Bloch, Isabelle Blusseau, Samy Perez, Ramon Pino Puybareau, Elodie Tochon, Guillaume Sorbonne Univ LIP6 CNRS F-75005 Paris France PSL Res Univ Ctr Math Morphol Mines ParisTech Fontainebleau France Univ Artois CRIL CNRS Lens France EPITA Res & Dev Lab LRDE Le Kremlin Bicetre France

ISBN: (数字)9783030766573

ISBN: (纸本)9783030766566;9783030766573

This paper aims at providing an overview of the use of mathematical morphology, in its algebraic setting, in several fields of artificial intelligence (AI). Three domains of AI will be covered. In the first domain, mathematical morphology operators will be expressed in some logics (propositional, modal, description logics) to answer typical questions in knowledge representation and reasoning, such as revision, fusion, explanatory relations, satisfying usual postulates. In the second domain, spatial reasoning will benefit from spatial relations modeled using fuzzy sets and morphological operators, with applications in model-based image understanding. In the third domain, interactions between mathematical morphology and deep learning will be detailed. Morphological neural networks were introduced as an alternative to classical architectures, yielding a new geometry in decision surfaces. Deep networks were also trained to learn morphological operators and pipelines, and morphological algorithms were used as companion tools to machine learning, for pre/post processing or even regularization purposes. These ideas have known a large resurgence in the last few years and new ones are emerging.

关键词： Mathematical morphology artificial intelligence Lattice Logics Spatial reasoning Fuzzy sets neural networks Deep learning

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