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2024 International Conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Sharma, Pooja Miglani, Ishika Sharma, Lucky Panipat Institute Of Engineering And Technology Samalkha India Panipat Institute Of Engineering And Technology Artificial Intelligence And Machine Learning Samalkha India

ISBN: (纸本)9798350306446

All In the realm of communication, individuals employ two primary modes: written and spoken language. Handwriting, in particular, serves as a powerful tool for conveying information and emotions across various contexts, including healthcare. Despite its widespread use, deciphering handwritten text remains a challenge due to variations in individual writing styles and the lack of standardization. This challenge is particularly pronounced in the medical field, where handwritten prescriptions can lead to serious consequences if misinterpreted. Our research addresses this issue by proposing DocAssist, a novel model that utilizes Convolutional neural networks to achieve an impressive 81% accuracy in recognizing doctors' handwriting. By leveraging machine learning and image recognition technologies, DocAssist aims to enhance prescription legibility, mitigate medication errors, and ultimately improve patient safety and healthcare outcomes. Through rigorous experimentation with the EMNIST dataset, our study demonstrates the effectiveness of DocAssist in accurately interpreting handwritten prescriptions, highlighting its potential to revolutionize prescription processing and alleviate the burden on healthcare professionals and pharmacists. © 2024 IEEE.

关键词： image enhancement

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ALE-GAN: A 3D Conditional Generative Adversarial Network with Attention Learning Modules for Lung Nodule Segmentation 2nd

ALE-GAN: A 3D Conditional Generative Adversarial Network wit...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Dabass, Manju Chandalia, Anuj Datta, Suvrankar Mahapatra, Dwarikanath AI Research and Development Department Manentia AI Advisory Private Limited. Gandhinagar India Radiodiagnosis and Interventional Radiology AlIMS New Delhi India R&D Department Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819995301

Early lung cancer detection using lung nodule segmentation can enhance patient survival. In computer vision applications like medical image analysis, convolutional neural networks (CNNs) outperform standard image processing methods. Convolutional neural network-based medical image segmentation methods have exhibited state-of-the-art performance, but they still face limitations. Data scarceness and class inequality induce overfitting and deprived performance. This study proposes a scalar attention learning modules (Sc-ALM) embedded generative adversarial network for lung nodule segmentation that learns nodule-focused data distribution to boost accuracy. The proposed network utilizes Sc-ALM in generator part based on U-Net as well as the discriminator based on a simple classification network that empowered the model to distinguish ground truth from falsified segmentation. Also, patch-based training reduces overfitting. The LUNA16 dataset is used to perform extensive lung nodule segmentation experiments. The proposed model’s boundary F1-score, average Jaccard index, and dice similarity coefficient are 0.9695, 0.9650, and 0.9825 respectively, outperforming various state-of-the-art models. This model has remarkable lung nodule segmentation performance and can help doctors diagnose lung nodules early by assessing their size, shape, and other properties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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A Short Survey on Deep Learning-Based Frameworks for Knowledge Graph applications 2

A Short Survey on Deep Learning-Based Frameworks for Knowled...

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2nd International Conference on Computer Technologies, ICCTech 2023

作者： Kushk, Abdulkarim Kochut, Krzysztof School of Computing University of Georgia AthensGA United States

ISBN: (纸本)9781665455824

Knowledge Graphs (KG) are repositories of structured, machine-readable data stored as relational triples. DBpedia, Freebase, and YAGO are examples of KGs that have been playing a vital role in many applications, including question answering systems, recommendation systems, and expert systems. Deep learning is a subset of machine learning techniques that consists of layers of artificial neural networks. It is a relatively young technology that, in a very short amount of time, has achieved state-of-the-art performance in many fields of science, business, and government. For instance, convolutional networks have achieved major breakthroughs in processing image, video, and audio files. Moreover, recurrent networks have a similar effect in processing sequential data, such as texts and time series [1]. In this survey, we shed light on the development deep learning has brought about in two KG tasks: entity summarization and entity linking. Experiments on standard performance measures and benchmark datasets show that deep learning-based models generally outperform other models by a considerable margin. © 2023 IEEE.

关键词： Semantic Web

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Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system

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Multimedia Tools and applications 2025年第9期84卷 6519-6543页

作者： Ashwin, M. Jagadeesan, D. Raman Kumar, M. Murugavalli, S. Chaitanya Krishna, A. Ammisetty, Veeraswamy Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Andhra Pradesh Madanapalle517325 India Nalla Malla Reddy Engineering College Telangana Hyderabad500088 India Department of Artificial Intelligence K. Ramakrishnan College of Technology Trichy -2 Kariyamanickam Rd Tamil Nadu Tiruchirappalli621112 India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Human Activity Recognition (HAR) has emerged as a crucial area of research in computer vision, signal processing, and machine learning, enabling to automatically identify human activities from sensor data. Although various models are developed for automatic HAR, they face challenges like limited accuracy, large training time, etc. To resolve these issues, we developed an innovative hybrid strategy using the combination of hybrid meta-heuristic optimization algorithms and deep learning. The developed work commences with the collection of HAR database and the collected database was pre-processed to enhance image quality. Further, segmentation was performed using optimized Otsu’s methodology to extract the most significant features. Then, an innovative classification model was developed using the combination of Improved Spotted Hyena algorithm with Seagull Optimization Algorithm (ISHO-SOA) and Adaptive Deep Convolutional neural networks (ADCNN) for identifying and classifying human activities. The novelty of the algorithm lies in the seamless integration of different meta-heuristic optimization algorithms and deep learning for HAR. In the proposed algorithm, the ADCNN was trained using the database to classify human activities, while the ISHO-SOA for refining the hyperparameters of ADCNN to optimize its training and improve classification performances. The proposed algorithm was modeled in MATLAB software, and validated using HAR dataset from the Kaggle database. The experimental results depict that the developed algorithm achieved higher accuracy of 99.84%, and greater precision of 99.90%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Early Detection of Neurodegenerative Diseases using Deep Learning Techniques: Issues & Challenges 15

Early Detection of Neurodegenerative Diseases using Deep Lea...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tyagi, Shalini Mehrotra, Monica Aggarwal, Arpita Department of Computer Science Jamia Millia Islamia New Delhi India IEEE Department of Computer Science Jamia Millia Islamia New Delhi India IEEE Department of Computer Science PGDAV College University of Delhi Delhi India

ISBN: (纸本)9798331300579

Our body's control center is the brain. Newer and newer brain disorders are being identified as time goes on. The cumulative loss of immunological and neuronal function is what leads to neurodegenerative diseases including Alzheimer's, Parkinson's, Hunting ton's, and many others. According to recent trends, there will be 152 million peoples with various neurodegenerative diseases in 2050. In recent years, the use of artificial intelligence (AI), specifically deep learning (DL) has transformed the area of neurology. This paper introduces and discusses various deep learning algorithms, including Deep belief networks (DBN), Convolutional neural networks (CNN), Recurrent neural networks (RNN) and Autoencoders along with their theory and applications in neurological diseases prediction. This paper summarizes and highlights the most promising deep learning models for Alzheimer's disease (AD) diagnosis and risk factors by training on neuroimaging datasets combining PET and MRI images gathered using diverse modalities. This study also reviews the entire MRI processing chain-from acquisition to image retrieval, from segmentation to disease prediction which has been optimized for deep learning. Deep learning models are not meant to replace healthcare professionals, but rather to aid in recognizing illnesses and better decision-making. The ultimate goal is to help healthcare providers make more accurate diagnoses and intervene more quickly. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Efficient Implementation of Matrix-Based image processing Algorithms for IoT applications

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APPLIED SCIENCES-BASEL 2025年第9期15卷 4947-4947页

作者： Zoican, Sorin Zoican, Roxana POLITEHN Bucharest Natl Univ Sci & Technol Fac Elect Telecommun & Informat Technol Telecommun Dept Bucharest 060042 Romania

This paper analyzes implementation approaches of matrix-based image processing algorithms. As an example, an image processing algorithm that provides both image compression and image denoising using random sample consensus and discrete cosine transform is analyzed. Two implementations are illustrated: one using the Blackfin processor with 32-bit fixed-point representation and the second using the convolutional neural network (CNN) accelerator in the MAX78000 microcontroller. Implementation with Blackfin can be considered a classic approach, in C language, possible on all existing microcontrollers. This implementation is improved by using two cores. The proposed implementation with the CNN accelerator is a new approach that effectively uses the dedicated accelerator for convolutional neural networks, with better results than a classical implementation. The execution time of matrix-based image processing algorithms can be reduced by using CNN accelerators already integrated in some modern microcontrollers to implement artificial intelligence algorithms. The proposed method uses CNN in a different way, not for artificial intelligence algorithms, but for matrix calculations using CNN resources effectively while maintaining the accuracy of the calculations. A comparison of these two implementations and the validation using MATLAB with 64 bits floating point representation are conducted. The obtained performance is good both in terms of quality of reconstructed image and execution time, and the performance differences between the infinite precision implementation and the finite precision implementation are small. The CNN accelerator implementation, based on matrix multiplication implemented using CNN, has a better performance suitable for Internet of Things applications.

关键词： discrete cosine transform random sample consensus visual DSP plus plus blackfin processor CNN accelerator internet of things

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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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International Journal of Information Technology (Singapore) 2025年 1-20页

作者： Medikonda, Venkata Reddy Reddy, Tatireddy Subba Yallamandaiah, S. Karthik, Sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an Improved ResNet50 model, while optimal feature selection is achieved through the Improved artificial Rabbits Optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a Multi-Scale Residual Depthwise Separable Convolutional Network (MRDSCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © Bharati Vidyapeeth's Institute of Computer applications and Management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification Improved artificial rabbits optimization algorithm Multi-scale residual depthwise separable convolutional neural network

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Chicken Quality Evaluation Using Deep Learning 2nd

Chicken Quality Evaluation Using Deep Learning

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2nd International Conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2023

作者： Madan, Rishi Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Toe, Teoh Teik Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Singapore Singapore

ISBN: (纸本)9789819919451

In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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New Generation Deep Learning for Video Object Detection: A Survey

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2022年第8期33卷 3195-3215页

作者： Jiao, Licheng Zhang, Ruohan Liu, Fang Yang, Shuyuan Hou, Biao Li, Lingling Tang, Xu Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.

关键词： Object detection Feature extraction Deep learning Task analysis Detection algorithms Learning systems Convolution Learning neural networks object detection pipeline processing video signal processing

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Oscillatory neural Network as Hetero-Associative Memory for image Edge Detection 2022

Oscillatory Neural Network as Hetero-Associative Memory for ...

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Annual Neuro-Inspired Computational Elements Conference (NICE)

作者： Abernot, Madeleine Gil, Thierry Todri-Sanial, Aida Univ Montpellier CNRS LIRMM Montpellier France

ISBN: (纸本)9781450395595

The increasing amount of data to be processed on edge devices, such as cameras, has motivated artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory neural networks (ONNs) present a novel brain-inspired computing approach by emulating brain oscillations to perform auto-associative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a Heterogeneous Associative Memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.

关键词： Edge detection Hetero-Associative Memory Neuromorphic Computing Oscillatory neural networks

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