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Hybrid classifier model for fruit classification

MULTIMEDIA TOOLS AND applications 2021年第18期80卷 27495-27530页

作者： Gill, Harmandeep Singh Khehra, Baljit Singh Guru Arjan Dev Khalsa Coll Comp Sci Tarn Taran Punjab India BBSBEC CSE Fatehgarh Sahib India

With an advancement in artificial intelligence (AI) applications, the use of smart imaging devices has been increased at a rapid rate. Recently, many researchers have utilized deep learning models such as convolutional neural networks (CNN) for image classification models. Compared to the traditional machine learning models, CNN does not require any kind of handcrafted features. It utilizes various filters to extract the potential features of images automatically. Inspired from this, in this paper, we have proposed a novel fruit classification model which utilizes the features of CNN, Long short Term Memory (LSTM) and Recurrent neural Network (RNN) architectures. Type-ii fuzzy enhancement is also used as pre-processing tool to enhance the images. Additionally, to tune the hyper-parameters of the proposed model, TLBO-MCET is also utilized. Extensive experiments are drawn by considering the existing and the proposed fruit classification models. Comparative analysis reveals that the proposed model outperforms the competitive fruit classification models.

关键词： Hybrid-Type ii Fuzzy TLBO-MCET Fruit image Classification CNN RNN LSTM

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Enhanced image Segmentation by a Novel Test Time Augmentation and Super-Resolution 9th

Enhanced Image Segmentation by a Novel Test Time Augmentatio...

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9th International Work-Conference on the Interplay Between Natural and artificial Computation (IWINAC)

作者： Garcia-Aguilar, Ivan Garcia-Gonzalez, Jorge Marcos Luque-Baena, Rafael Lopez-Rubio, Ezequiel Dominguez-Merino, Enrique Univ Malaga Dept Comp Languages & Comp Sci Bulevar Louis Pasteur 35 Malaga 29071 Spain Biomed Res Inst Malaga IBIMA C Doctor Miguel Diaz Recio 28 Malaga 29010 Spain

ISBN: (纸本)9783031065279;9783031065262

image segmentation in computer vision applications plays a critical role in the video processing workflow. In real applications, where interesting elements are moving in the presence of moving objects in the background, complex models are required in the segmentation process to obtain better results. In this paper, a methodology based on super-resolution and test time augmentation is proposed to improve the precision and effectiveness of the segmentation process. Our proposal avoids both modification and retraining of the model. Experiments show that our approach can increase the mean average precision of images segmentation in sequences from well-known benchmark datasets with a significant improvement.

关键词： image segmentation Convolutional neural networks (CNN) Test Time Augmentation (TTA) Super-resolution (SR)

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Source localization in resource-constrained sensor networks based on deep learning

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neural COMPUTING & applications 2021年第9期33卷 4217-4228页

作者： Javadi, S. Hamed Guerrero, Angela Mouazen, Abdul M. Univ Ghent Fac Biosci Engn Dept Environm B-9000 Ghent Belgium

Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.

关键词： artificial neural network (ANN) Decentralized detection Deep learning Error type I Error type ii Internet of things (IoT) Source localization Target tracking Wireless sensor networks (WSN)

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Development of NDVI Prediction Model Using artificial neural networks 4th

Development of NDVI Prediction Model Using Artificial Neura...

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4th International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Gaikwad, Sandeep V. Vibhute, Amol D. Kale, Karbhari V. Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University MH Aurangabad431004 India School of Computer Science MIT-World Peace University MH Pune411038 India

ISBN: (纸本)9783031070044

The use of remotely sensed images in the agriculture sector plays a vital role in knowing crop status on a higher spatial scale. Researchers have developed various indices for this purpose. The Normalized Difference Vegetation Index (NDVI) is an important indices that measure the vegetation vigor of crop. The agricultural community has shown its uses in various applications, viz. crop growth assessment, crop health monitoring, crop yield estimation, etc. The NDVI forecasting helps to make an educated guess, based on its temporal behavior in past years, on likely vegetation conditions ahead of time and thus supports decision-makers to formulate mitigation strategies. In this paper, Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data has been used to calculate the NDVI, and then NDVI forecasting is performed based on 2002–2014 NDVI time-series data. The results are promising and can be used in agricultural applications. © 2022, Springer Nature Switzerland AG.

关键词： neural networks

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artificial intelligence-powered precision: Unveiling the landscape of liver disease diagnosis-A comprehensive review

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ENGINEERING applications OF artificial INTELLIGENCE 2024年 138卷

作者： Vadlamudi, Sireesha Kumar, Vimal Ghosh, Debjani Abraham, Ajith Bennett Univ Sch Comp Sci Engn & Technol TechZone II Greater Noida Uttar Pradesh India Bennett Univ Sch Artificial Intelligence TechZone II Greater Noida Uttar Pradesh India

The global significance of diagnosing liver diseases is heightened, particularly in under-resourced regions with limited healthcare facilities. Traditional diagnostic methods, characterized by time and labor-intensive processes, have led to a growing demand for telemedicine-based solutions. The incorporation of artificial Intelligence is deemed essential to enhance the efficiency and accuracy of diagnostic models. This review explores the seamless integration of diverse data modalities, including clinical records, demographics, laboratory values, biopsy specimens, and imaging data, emphasizing the importance of combining both types for comprehensive liver disease diagnosis. The study rigorously examines various approaches, focusing on pre-processing and feature engineering in non-image data diagnostic model development. Additionally, it analyzes studies employing Convolutional neural networks for cutting-edge solutions in image data interpretation. The paper provides insights into existing liver disease datasets, encompassing both image and non-image data, offering a comprehensive understanding of crucial research data sources. Emphasis is placed on performance evaluation metrics and their correlation in assessing diagnostic model efficiency. The review also explores open-source software tools dedicated to computer-aided liver analysis, enhancing exploration in liver disease diagnostics. Serving as a concise handbook, it caters to novice and experienced researchers alike, offering essential insights, summarizing the latest research, and providing a glimpse into emerging trends, challenges, and future trajectories in liver disease diagnosis.

关键词： Liver diagnosis Telemedicine image & non-image data Pathology Deep learning artificial intelligence

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1st International Conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st International Conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning Algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation Techniques and Optimization Algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-ii: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in Distributed Cyber Physical System;Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) neural networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP applications;air Quality Monitoring System Based on artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG Signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control Methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor networks Against Malicious Attacks Using Butterfly Method.

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A Moroccan Sign Language Recognition Algorithm Using a Convolution neural Network

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第3期10卷 411-425页

作者： Nourdine Herbaz Hassan El Idrissi Abdelmajid Badri Laboratory of Electronics Energy Automation & Information Processing (LEEA&TI) Faculty of Sciences and Techniques Mohammedia Hassan II University of Casablanca Morocco

Gesture recognition is an open phenomenon in computer vision, and one of the topics of current interest. Gesture recognition has many applications in the interpretation of sign language, one is in human-computer interaction, and the other is in immersive game technology. For this reason, we have developed a model of image processing recognition of gestures, based on artificial neural networks, starting from data collection, identification, tracking and classification of gestures, to the display of the obtained results. We propose an approach to contribute to the translation of sign language into voice/text format. In this paper, we present a Moroccan sign language recognition system using a convolutional neural network (CNN). This system includes an important data set of more than 20 files. Each file contains 1000 static images of each signal from several different angles that we collected with a camera. Different sign language models were evaluated and compared with the proposed CNN model. The proposed system achieved an accuracy of 99.33% and achieved best performance with an accuracy rate of 98.7%.

关键词： Human computer interaction image recognition Gesture recognition Standardization Games Assistive technologies Cameras

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Adaptive Methods for Kernel Initialization of Convolutional neural Network Model Applied to Plant Disease Classification

Adaptive Methods for Kernel Initialization of Convolutional ...

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Electrical, Electronic and Information Sciences (eStream), Open Conference of

作者： Saloua Lagnaoui Khaoula Boumais Saad El Fallah Zakariae En-Naimani Khalid Haddouch Dalius Matuzevičius Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory AI Data Science and Emerging Systems Sidi Mohammed ben Abdallah University ENSA Fez Morocco Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Private University of Fez Fez Morocco Laboratory Computer Science AI and Cyber Security ENSET University of Hassan II Casablanca Mohammedia Morocco Department of Electronic Systems Faculty of Electronics Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

Convolutional neural networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional neural networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional neural networks effectiveness, especially in critical applications like Plant Disease classification.

关键词： Training Plant diseases Adaptation models Sensitivity neural networks Convolutional neural networks Kernel

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A Review of Deep Learning on Medical image Analysis

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MOBILE networks & applications 2021年第1期26卷 351-380页

作者： Wang, Jian Zhu, Hengde Wang, Shui-Hua Zhang, Yu-Dong Univ Leicester Sch Informat Leicester LE1 7RH Leics England Loughborough Univ Sch Architecture Bldg & Civil Engn Loughborough LE11 3TU Leics England Univ Leicester Sch Math & Actuarial Sci Leicester LE1 7RH Leics England King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia

Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients-compared with image datasets in other computer vision fields such like faces-medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis.

关键词： Transfer learning Medical image analysis CT Deep learning MRI Convolutional neural networks Fine-tuning Feature extractor artificial intelligence

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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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