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

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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ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection

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MULTIMEDIA TOOLS AND applications 2024年第4期83卷 10989-11015页

作者： Gautam, Vinay Ranjan, Ranjeet Kumar Dahiya, Priyanka Kumar, Anil Chitkara Univ Inst Engn & Technol Rajpura Punjab India Thapar Inst Engn & Technol CSE Dept Patiala India DIT Univ Sch Comp Data Sci Res Grp Dehra Dun India

The mango crop production and quality are affected by several factors. Plant disease is one of the prime factors that impact crop yield. The disease impacts all parts of plants such as leaves, roots, and stems. Farmers waste most of their energy, time, and effort dealing with it manually and lead to heavy loss in the yield of the crops. Mango production is affected by several types of diseases. Prior recognition of disease would ease and reduce the diagnostic process that enhances the production of quality crops. A huge investigation has been carried out in the field of identifying and classifying leaf disease. Hence, an appropriate artificial intelligence (AI) based solution is needed to support farmers. The objective of the work is to provide an effective and efficient AI-based solution to detect and classify leaf disease earlier. Earlier disease detection in crops is the most prominent way to prevent loss of money and time. The plant leaf image is mainly used as a source to detect disease in the plant. This paper employs an ensemble stacked deep learning model to resolve the problem of automatic identification of mango-leaf diseases. In the proposed approach, initially, the images are segmented for the region of interest and input to a stack of various deep neural networks. The outcome of the deep neural network is aggregated with a machine learning model to identify leaf disease. This model is used to identify various mango leaf diseases such as Powdery mildew, Anthracnose, etc. In the experiment, the deep learning models are stacked and aggregated with machine learning to identify mango leaf diseases. The proposed model is compared with state-of-the-art models and outperforms with 98.57% accuracy.

关键词： Classification Convolutional neural Network Ensemble Learning image processing Leaf disease detection

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ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL imageRY: A HIGH-RESOLUTION neural NETWORK APPROACH WITH AMPLIFIED FEATURE PYRAMID AND SIGMOID RE-WEIGHTING 31

ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL IMAGERY: A HIG...

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2024 International Conference on image processing

作者： Park, Chanyeong Jang, Junbo Kim, Heegwang Paik, Joonki Chung Ang Univ Dept Image Seoul South Korea Chung Ang Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350349405;9798350349399

Detecting small objects in drone-captured images or aerial videos is challenging due to their minimal representation. As data traverses deep learning networks, the information about small objects can diminish, making high-resolution images essential for enhanced detection performance. However, high-resolution images increase computational load undesirably. Leveraging this fact, we propose a streamlined neural network designed specifically for small object detection in high-resolution images. The proposed network encompasses three main components: i) Enhanced High-Resolution processing Module (EHRPM), ii) the Small Object Feature Amplified Feature Pyramid Network (SOFA-FPN) with its Edge Enhancement Module (EEM), Cross Lateral Connection Module (CLCM), and Dual Bottom-up Convolution Module (DBCM), and iii) the Sigmoid Re-weighting Module (SRM). Compared to several state-of-the-art networks, our method delivers superior performance with fewer parameters and a lower computational demand. The source code is available at https://***/datu0615/EHRPM.

关键词： Small Object Detection Aerial images Multi-scale Feature Fusion High-resolution processing

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Deep Residual Learning for Fruits of Ceremonial Plants Recognition 2

Deep Residual Learning for Fruits of Ceremonial Plants Recog...

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2nd International Conference on Data, Information and Computing Science, CDICS 2024

作者： Negara, Ida Bagus Kade Dwi Suta Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Udayana University Denpasar Indonesia

ISBN: (纸本)9781643685762

Fruit detection refers to a method within image processing and computer vision that focuses on automatically recognizing and distinguishing different types of fruits using advanced algorithms and techniques. Fruits are essential and widely used in ceremonial offerings (Banten). They are pivotal in ensuring the completeness of ceremonial offerings. The main goal of fruit detection is to recognize fruits in image form, making it applicable in various applications, including inventory management, automatic classification in the agricultural industry, and medical applications for monitoring dietary patterns. Commonly used methods include artificial neural networks (deep learning), digital image processing, and feature extraction techniques to distinguish between shapes, colours, textures, and other visual features of different types of fruits. In this research, the deep learning algorithm CNN (Convolutional neural Network) is employed. This technique, a deep learning algorithm, excels in accurate image recognition and classification. A total of 500 fruit images are used for five types of ceremonial fruits commonly used in religious activities, namely coconut, banana, candlenut, nutmeg, and areca nut. The residual learning method RESNET152 is used as the CNN algorithm architecture. According to the test findings, the highest accuracy rate achieved was 93% in correctly identifying ceremonial fruit images. © 2025 The Authors.

关键词： Convolutional neural networks

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AI Innovator: Text to image Generation using GAN

AI Innovator: Text to Image Generation using GAN

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Nimbarte, Mrudula Hussain, Alfiya Budhlani, Kanak Bansod, Chirag Ramdham, Mayur Kanhe, Om Khullar, Amit S.B. Jain Institute of Technology Management and Research Department of Computer Science and Engineering MS Nagpur India

ISBN: (纸本)9798350348460

This review provides a comprehensive review of the latest approaches and advances in text-to-image processing using artificial neural networks (GANs). The work under review uses GAN architecture, where generators and classifiers work together to extract corresponding images from descriptions. This research includes a comprehensive review of the GAN framework, delving into the complexities of the generator and discrimination during training. The project solves problems related to converting text input into visual images using a combination of natural language processing and computer vision. This article reviews the main concepts of the architecture as well as the use of advanced neural networks for text understanding and image synthesis. Particular attention is paid to the optimization process the generator uses to improve the quality and integrity of the resulting images. Additionally, the review highlights the evolution of different labels and discusses their implications for real-world applications. © 2024 IEEE.

关键词： Adversarial Training Computer Vision Generative Adversarial networks (GANs) image Synthesis Multimodal Learning Natural Language processing (NLP) neural Network Architecture Text to image Generation

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Remote sensing image classification method based on improved ShuffleNet convolutional neural network

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INTELLIGENT DATA ANALYSIS 2024年第2期28卷 397-414页

作者： Li, Ziqi Su, Yuxuan Zhang, Yonghong Yin, Hefeng Sun, Jun Wu, Xiaojun Wuxi Univ Sch Automat Wuxi Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China

As a list of remotely sensed data sources is available, the effective processing of remote sensing images is of great significance in practical applications in various fields. This paper proposes a new lightweight network to solve the problem of remote sensing image processing by using the method of deep learning. Specifically, the proposed model employs ShuffleNet V2 as the backbone network, appropriately increases part of the convolution kernels to improve the classification accuracy of the network, and uses the maximum overlapping pooling layer to enhance the detailed features of the input images. Finally, Squeeze and Excitation (SE) blocks are introduced as the attention mechanism to improve the architecture of the network. Experimental results based on several multisource data show that our proposed network model has a good classification effect on the test samples and can achieve more excellent classification performance than some existing methods, with an accuracy of 91%, and can be used for the classification of remote sensing images. Our model not only has high accuracy but also has faster training speed compared with large networks and can greatly reduce computation costs. The demo code of our proposed method will be available at https://***/li-zi-qi.

关键词： Remote sensing image classification convolutional neural network

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Large Language Models (such as ChatGPT) as Tools for Machine Learning-Based Data Insights in Analytical Chemistry

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ANALYTICAL CHEMISTRY 2025年第13期97卷 6956-6961页

作者： Duponchel, Ludovic de Oliveira, Rodrigo Rocha Motto-Ros, Vincent Univ Lille CNRS UMR 8516 LASIRE Lab Spect Interact Re?activite? & Environm F-59000 Lille France Univ Barcelona Fac Chem Chemometr Grp Barcelona 08028 Spain Univ Claude Bernard Lyon 1 Inst Lumiere Matiere CNRS UMR 5306 F-69622 Villeurbanne France

artificial intelligence (AI), especially through the development of deep learning techniques like convolutional neural networks (CNNs), has revolutionized numerous fields. CNNs, introduced by Yann LeCun in the 1990s (Hubbard, W.;Jackel, L. D. Backpropagation Applied to Handwritten Zip Code Recognition. neural Comput. 1989, 1 (4), 541- 551. https://***/10.1162/neco.1989.1.4.541), have found applications in healthcare for medical diagnostics, autonomous vehicles in transportation, stock market prediction in finance, and image recognition in computer vision to name just a few. Similarly, in analytical chemistry, deep learning has enhanced data analysis from techniques like MS spectrometry, NMR, fluorescence spectroscopy, and chromatography. Another AI branch, Natural Language processing (NLP), has surged recently with the advent of Large Language Models (LLMs), such as OpenAI's ChatGPT. This paper demonstrates the application of an LLM via a smartphone to conduct multivariate data analyses, in an interactive conversational manner, of a hyper-spectral imaging data set from laser-induced breakdown spectroscopy (LIBS). We demonstrate the potential of LLMs to process and analyze data sets, which automatically generate and execute code in response to user queries, and anticipate their growing role in the future of analytical chemistry.

关键词： Convolutional neural networks

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Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2023年第3期31卷 109-125页

作者： Walsh, Jeremy Neupane, Arjun Koirala, Anand Li, Michael Anderson, Nicholas Cent Queensland Univ Bldg 361 CQU Rockhampton Qld 4701 Australia

The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral preprocessing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.

关键词： Fruit review point spectroscopy chemometrics deep learning artificial neural network dry matter content

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Event-Based Hand Detection on Neuromorphic Hardware Using a Sigma Delta neural Network 33rd

Event-Based Hand Detection on Neuromorphic Hardware Using a ...

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33rd International Conference on artificial neural networks and Machine Learning (ICANN)

作者： Azzalini, Loic Gluege, Stefan Struckmeier, Jens Sandamirskaya, Yulia ZHAW Zurich Univ Appl Sci Winterthur Switzerland WAIYS GmbH Langen Germany

ISBN: (纸本)9783031723582;9783031723599

The development of deep learning (DL) models has dramatically improved marker-free human pose estimation, including an important task of hand tracking. However, for applications in real-time critical and embedded systems, e.g. in robotics or augmented reality, hand tracking based on standard frame-based cameras is too slow and/or power hungry. The latency is limited by the frame rate of the image sensor already, and any subsequent DL processing further increases the latency gap, while requiring substantial power for processing. Dynamic vision sensors, on the other hand, enable sub-millisecond time resolution and output sparse signals that can be processed with an efficient Sigma Delta neural Network (SDNN) model that preserves the sparsity advantage in the neural network. This paper presents the training and evaluation of a small SDNN for hand detection, based on event data from the DHP19 dataset deployed on Intel's Loihi 2 neuromorphic development board. We found it possible to deploy a hand detection model in neuromorphic hardware backend without a notable performance difference to the original GPU implementation, at an estimated mean dynamic power consumption for the network running on the chip of approximate to 7 mW.

关键词： Event-based Vision Neuromorphic Hardware Hand Tracking Sigma Delta neural networks

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Global-Local Multigranularity Transformer for Hyperspectral image Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 112-131页

作者： Meng, Zhe Yan, Qian Zhao, Feng Chen, Gaige Hua, Wenqiang Liang, Miaomiao Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Sch Artificial Intelligence Xian 710121 Peoples R China Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Jiangxi Prov Key Lab Multidimens Intelligent Perce Ganzhou 341000 Peoples R China

Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. Recently, transformer has become proficient in attending to global information due to their self-attention mechanisms, yet they may fall short in capturing multiscale features of HSI. To address these limitations, a global-local multigranularity transformer (GLMGT) network is proposed for HSI classification. The GLMGT combines CNN with the transformer to comprehensively capture multigranularity spectral and spatial features across global and local scales. Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. In addition, we introduce a multigranularity spectral feature extraction block to fully leverage spectral information across different granularities. The validity of the proposed method is demonstrated through experimental validation using seven publicly available datasets, which include two Chinese satellite hyperspectral datasets (ZY1-02D Huanghekou and GF-5 Yancheng) and one UAV-based hyperspectral dataset.

关键词： Feature extraction Transformers Convolution Convolutional neural networks Data mining Three-dimensional displays Hyperspectral imaging Kernel Spectral analysis Computer vision CNN multigranularity transformer hyperspectral image (HSI) classification

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