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artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Milind S. Patil Pradip B. Mane All India Shri Shivaji Memoril Society’s Institute of Information Technology Pune India E&TC Engineering Department Vishwakarama Institute of Information Technology Pune India

image Quality Assessment (IQA) has got importance in the computer vision applications as it provides tool to evaluate and rate different image processing algorithms. image Fusion is a process in which information from multiple images is combined into a single image. Due to specific nature of fused images present IQA methods have limitations for evaluation of image Fusion algorithms. With the recent development of Deep Convolutional neural networks (Deep CNNs), No- reference image quality assessment is becomes reality. This article has proposed the pre-trained Deep CNNs based image fusion classification using Alexnet, VGG19, Inception V3 and ResNet-50. Four states–of–the-art image fusion algorithms used for image fusion are Laplacian Pyramid (LP), Shift Invariant DWT (SIDWT), Discrete Wavelet Transform (DWT) and Ratio Pyramid (RP). To achieve the effective IQA, sufficiently large dataset of synthetically fused images is created and same is evaluated by using Deep CNNs. The results show that recent deep CNN methods correctly classify the fused images into corresponding categories based on its fusion algorithms. The results are consistent with FR-IQA methods. ResNet-50 provides best classification accuracy with less number of epochs and time to converge due to sparse network connections.

关键词： Training image quality Transforms Feature extraction Discrete wavelet transforms Classification algorithms Quality assessment

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PETNet–Coincident Particle Event Detection using Spiking neural networks

PETNet–Coincident Particle Event Detection using Spiking Ne...

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Neuro Inspired Computational Elements Conference (NICE)

作者： Jan Debus Charlotte Debus Günther Dissertori Markus Götz Institute for Particle Physics and Astrophysics ETH Zürich Switzerland Scientific Computing Center Karlsruhe Institute of Technology Germany Helmholtz AI

ISBN: (数字)9798350390582

ISBN: (纸本)9798350390599

Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural networks. Their time-variant nature makes them particularly suitable for processing time-resolved, sparse binary data. In this paper, we investigate the potential of leveraging SNNs for the detection of photon coincidences in positron emission tomography (PET) data. PET is a medical imaging technique based on injecting a patient with a radioactive tracer and detecting the emitted photons. One central post-processing task for inferring an image of the tracer distribution is the filtering of invalid hits occurring due to e.g. absorption or scattering processes. Our approach, coined PETNet, interprets the detector hits as a binary-valued spike train and learns to identify photon coincidence pairs in a supervised manner. We introduce a dedicated multi-objective loss function and demonstrate the effects of explicitly modeling the detector geometry on simulation data for two use-cases. Our results show that PETNet can outperform the state-of-the-art classical algorithm with a maximal coincidence detection F 1 of 95.2%. At the same time, PETNet is able to predict photon coincidences up to 36 times faster than the classical approach, highlighting the great potential of SNNs in particle physics applications.

关键词： Geometry High energy physics Tensors Computational modeling Detectors Prediction algorithms Data models

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21st International Conference on Computer Information Systems and Industrial Management applications, CISIM 2022

21st International Conference on Computer Information System...

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21st International Conference on Computer Information Systems and Industrial Management applications, CISIM 2022

ISBN: (纸本)9783031105388

The proceedings contain 28 papers. The special focus in this conference is on Computer Information Systems and Industrial Management applications. The topics include: Augmentation of Accelerometer and Gyroscope Signals in Biometric Gait Systems;Evolutionary Optimization of UAVs Deployment for k-Coverage Problem;Embedded Processor Design in FPGA by ASMD-FSMD and FSM-Single Techniques;an Efficient Data Distribution Strategy for Distributed Graph processing System;mobile Phone as a 6DoF Motion Controller;application of Graph Document Model for Classification of Agricultural Scientific Papers in Polish;integrated Models-Driven Framework to Generate Various Online and Print Tests;hybrid Model of Tourism Recommendation Software Development;Application of Continuous Embedding of Viral Genome Sequences and Machine Learning in the Prediction of SARS-CoV-2 Variants;Food Classification from images Using a neural Network Based Approach with NVIDIA Volta and Pascal GPUs;bird Identification from the Thamnophilidae Family at the Andean Region of Colombia;predictive Model of Cardiovascular Diseases Implementing artificial neural networks;user Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness;analysis of Pre-trained Convolutional neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus images;knowledge Management: Effects on Innovation in Micro, Small, and Medium-Sized Export Enterprises;international Purchase Transactions: An Analysis of the Decision Cycles in Colombian Companies’ Operations;drivers of Eco-innovation in Industrial Clusters - A Case Study in the Colombian Metalworking Sector;design of a Wearable Assistive System for Visually Impaired People;blood Pressure Estimation from Photoplethysmography Signals by Applying Deep Learning Techniques;a Recommender System for Digital Newspaper Readers Based on Random Forest;preface;a New Approach for image Thinning.

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An Intelligent Classification Logic to Differentiate Crops and Weeds based on Learning assisted image processing Technique

An Intelligent Classification Logic to Differentiate Crops a...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： K. Suresh Shaik Heena P. Elayaraja Ala'a Al-Shaikh Neeraj Sunheriya Raziff Hamsan Department of EEE QIS College of Engineering and Technology Ongole Dept of CSE QIS College of Engineering and Technology Ongole AP Department of ECE KongunaduCollege of Engineering and Technology Trichy Dept of Cybersecurity Faculty of IT Zarqa University Zarqa Jordan UBT - University of Business and Technology Jeddah Saudi Arabia Dept of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur IN Faculty of Sceicen and Technology Spectrum International University College Selangor Malaysia

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Precision agriculture has become increasingly critical as the global demand for food production rises alongside the need for sustainable farming practices. This study presents an intelligent classification system that differentiates crops from weeds using a hybrid approach combining artificial neural networks (ANN) with XGBoost. The proposed method leverages learning-assisted image processing to achieve robust feature extraction and precise classification. High-resolution images were acquired from multiple sources, including field cameras and drones, under diverse environmental conditions. Extensive preprocessing and augmentation techniques, such as noise reduction, normalization, rotation, scaling, and cropping, were applied to enhance image quality and dataset variability. Subsequent segmentation using region-based methods and morphological operations ensured the isolation of relevant plant features from the background. Both manual and ANN-based feature extraction techniques were employed to capture intrinsic characteristics of crops and weeds. The ANN module automatically extracted high-level features that were then passed to the XGBoost classifier, which fine-tuned the decision boundaries through gradient boosting. Experimental results indicate that the hybrid model achieved an overall classification accuracy ranging from 92% to 95%, with the best configuration reaching an accuracy of 93.5%, a precision of 94%, and a recall of 93%. These performance metrics demonstrate the system's potential for real-time field deployment in precision agriculture. The integration of deep learning with gradient boosting not only improved the model's discriminative ability but also ensured computational efficiency, making it viable for practical applications. This work contributes to sustainable agricultural practices by enabling precise weed management and targeted interventions, thereby reducing the need for extensive chemical treatments and enhancing crop yield.

关键词： Precision agriculture Deep learning Accuracy Noise reduction artificial neural networks Manuals Feature extraction Boosting Real-time systems Morphological operations

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A Verilog based Approach for Object Detection using CNN

A Verilog based Approach for Object Detection using CNN

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Global Conference for Advancement in Technology (GCAT)

作者： Samson Swaraj Quadros S. Adityakrishna Kirti S. Pande Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

A Convolutional neural Network (CNN) are a class of artificial neural networks specifically designed to process data with a grid-like topology, such as images, making them well-suited for tasks like image recognition and classification, object detection, and speech recognition. However, their current software implementations leave much to be desired regarding energy efficiency, speed, performance and scalability. Hardware-based implementation of CNNs has several significant advantages over software, including faster data processing due to the parallel execution of hardware-based FPGAs and ASICs, which are necessary for real-time applications. They are more energy-efficient and have consistent, predictable performance. In this paper, we present the implementation of CNN on Verilog. We implemented a highly optimized CNN regression model architecture for object detection having an accuracy of 97%. The entire design was made on Verilog, allowing easy transferability to both FPGA and ASIC platforms. The proposed work is compared to the current standard for software implementations – Google Colab. The results obtained show considerable speed up and improved performance.

关键词： Scalability Object detection Speech recognition Software Energy efficiency Topology Convolutional neural networks Hardware design languages Field programmable gate arrays Standards

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Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

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JOURNAL OF CLINICAL NEUROSCIENCE 2021年 89卷 177-198页

作者： Buchlak, Quinlan D. Esmaili, Nazanin Leveque, Jean-Christophe Bennett, Christine Farrokhi, Farrokh Piccardi, Massimo Univ Notre Dame Australia Sch Med Sydney NSW Australia Univ Technol Sydney Fac Engn & IT Ultimo NSW Australia Virginia Mason Med Ctr Neurosci Inst Seattle WA 98101 USA

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 +/- 0.09;sensitivity = 0.87 +/- 0.10;specificity = 0.0.86 +/- 0.10;precision = 0.88 +/- 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Machine learning Multimodal neuroimaging Neurosurgery image processing Radiomics Glioblastoma Brain tumor classification Glioma Glioma grading Deep learning Convolutional neural networks T1-MR image T2-MR image FLAIR

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An Unsupervised Model Based on Convolutional neural Network for Fusing Landsat-8 and Sentinel-2 Data

An Unsupervised Model Based on Convolutional Neural Network ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shengnan Yu Yong Deng Yunfei Li Jun Li Jia Chen Shaoquan Zhang School of Computer Science China University of Geosciences Wuhan P. R. China Changjiang Institute of Survey Planning Design and Research Wuhan China School of Geography and Planning Sun Yat-sen University Guangzhou China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Medium spatial resolution satellite image series is important for some applications. Currently, the most widely used medium resolution data is from Sentinel-2 and Landsat-8 satellites. For the purpose of acquiring dense image series, the fusion of them has drawn much attention. In this field, the deep learning-based fusion models have appeared excellent performance, which demonstrated the applicability for deep learning for the fusion. However, existing deep learning based fusion models all need high quality and abundant training data, which may limit their applications. To address this problem, we develop an unsupervised fusion model based on convolutional neural networks (CNNs), which can fuse the Sentinel-2 and Landsat images without other training data. This fusion model takes full advantage of CNNs in the image fusion task, but get raid of the limitation of the requirement of training data. In the experiment, we test the new model using the Sentinel-Landsat dataset, and compare it with other two fusion approaches. The experimental results indicate that the proposed fusion model has remarkable fusion performance especially in terms of accuracy. However, it is can not capture the surface changes well, which is the main direction for us to improve it in the future.

关键词： Earth Deep learning artificial satellites Training data Data models Convolutional neural networks Task analysis

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Element Base of Pulse neural Network Units 2

Element Base of Pulse Neural Network Units

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2nd International Conference on neural networks and Neurotechnologies, NeuroNT 2021

作者： Bureneva, Olga I. Saint-Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering Saint Petersburg Russia

ISBN: (纸本)9781665445344

An important issue in modeling of information processing in biological systems is the coding of information by nerve impulses. Pulse streams are the form of information representation closest to the biological one. To build elements and units of pulse neural network it is necessary to implement a basic set of elements, which are able to perform various arithmetic and logical transformations in pulse format. The report presents a set of digital elements for pulse stream processing. The elements can be used in the design of neurocomputers, and technical systems based on neural networks, used for pattern recognition, image analysis and processing and other applications in the field of artificial intelligence. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Learning biophysical determinants of cell fate with deep neural networks

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NATURE MACHINE INTELLIGENCE 2022年第7期4卷 636-+页

作者： Soelistyo, Christopher J. Vallardi, Giulia Charras, Guillaume Lowe, Alan R. UCL Inst Phys Living Syst London England UCL Dept Struct & Mol Biol London England UCL Dept Cell & Dev Biol London England UCL London Ctr Nanotechnol London England Alan Turing Inst London England

Deep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning approach capable of learning an explainable model of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue, during which cell fate is thought to be determined by the local cellular neighbourhood over time. To investigate this, we developed a new approach (tau-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the tau-VAE's latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate-a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network, which using the predictions of the tau-VAE can identify conditions that deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening. An end-to-end machine learning approach that can learn which mechanisms determine cell fate and competition from a large time-lapse microscopy dataset is developed. The approach makes use of a probabilistic autoencoder to learn an interpretable representation of the

关键词： Cells

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Imagined Speech and Visual imagery as Intuitive Paradigms for Brain-Computer Interfaces

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Seo-Hyun Lee Ji-Ha Park Deok-Seon Kim Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Dept. of Artificial Intelligence Korea University Seoul Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance.

关键词： Visualization Communication systems neural activity Signal processing Motors Brain-computer interfaces Decoding Calibration Synchronization Speech processing

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