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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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COVID-19 Image Retrieval Using Siamese Deep Neural Network and Hashing Technique 14

COVID-19 Image Retrieval Using Siamese Deep Neural Network a...

引用

14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Boroujeni, Farsad Zamani Afshari, Doryaneh Hossien Mahmoodi, Fatemeh Science and Research Branch Dept. of Computer Engineering Tehran Iran Branch Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

Medical images are crucial for early diagnosis and are increasingly important in modern medicine. This study presents a medical image retrieval system using a Siamese neural network with 13 layers. The system uses the Minimum Redundancy Maximum Relevance (mRMR) technique to extract deep features from the Siamese, and then uses binary hashing to retrieve similar images using Hamming distance. The experimental results on Covid-19 dataset show that the proposed method improves lung image retrieval by over 6% compared to previous methods, achieving an average accuracy of 93.83% and 92.73% in 5 and 10 retrieved images, respectively. © 2023 IEEE.

关键词： Image enhancement

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WiFi-based Indoor Positioning using Low-cost Microcontrollers and Signal Fingerprinting 16

WiFi-based Indoor Positioning using Low-cost Microcontroller...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Hui, Kevin Y.H. Datta, Suprakash Electrical Engineering and Computer Science Dept York University Toronto Canada

The problem of positioning or localization is important in many applications, including cellular devices, sensor networks, Internet-of-Things, vehicles and human beings. For some applications, Global Positioning System (GPS)-based algorithms provide adequate accuracy. In many cases, cellular networks are used to augment GPS and other signals to improve the accuracy. However, in indoor environments, GPS does not work or does not provide satisfactory accuracy. In this paper, we propose a low-cost, high-accuracy solution to the problem of indoor localization. In particular, we focus on buildings with existing Wifi infrastructure. We use low-cost, low-power, WiFi-enabled microcontroller units to build a system that models wireless signal fingerprint data using simple machine learning methods. We demonstrate through experiments carried out in a university building that our system provides high accuracy. We also study the change in accuracy with the increase in number of microcontroller units used. © 2025 Elsevier B.V.. All rights reserved.

关键词： College buildings

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A computer science Perspective on Digital Sustainability: A Thematic Literature Review 22

A Computer Science Perspective on Digital Sustainability: A ...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： Green development

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Enabling Precise Diabetic Retinopathy Severity Prediction through Convolutional Neural Networks 10

Enabling Precise Diabetic Retinopathy Severity Prediction th...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shruti, P. Reddy, Tanuja Konda Abhiram, S. Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Amrita Vishwa Vidyapeetam Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Diabetes retinopathy (DR) continues to be a major public health concern, needing efficient and reliable approaches for early identification and severity categorization. In this work, we used deep learning to improve the diagnosis procedure of diabetic retinopathy, which is blindness caused by diabetes. Deep learning algorithms, notably neural networks, have shown extraordinary performance in image analysis tasks, demonstrating their potential to transform medical picture classification. The dataset used was the APTOS-2019 dataset with 3930 retinal images and corresponding classes of severity of the disease. Using a custom-designed CNN architecture, we focused on diabetic retinopathy severity classification, a multiclass classification consisting of 5 classes. The suggested custom model outperformed existing pretrained models in terms of accuracy with training accuracy at a high 98% and testing accuracy of 93%, showing its efficacy in diabetic retinopathy severity categorization. Our discoveries revealed the potential of customized deep learning models to improve diagnostic speed and accuracy, providing a major advancement toward individualized and successful patient treatment in the field of managing diabetic retinopathy. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models 3

Identification of Genomic Associations Between Parkinson's a...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Rana, Md. Shohel Podder, Nitun Kumar Rana, Humayan Kabir Hasan, Md. Imam Azam, Md. Shafiul Rahim, Md Abdur Hasan Sazzad Iqbal, S.M. Saha, Subir Pabna University of Science and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

Neurodegenerative diseases (NDGs) can slowly damage the brain as well as the central and peripheral nervous systems. Parkinson's disease (PD) is a sensitive sensory disease that causes a malfunction of dynamic equilibrium. PD causes certain brain neuronal cells to gradually split or die. There are certain significant characteristics of PD that may increase the risk of acquiring deferent NDGs including Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Multiple sclerosis disease (MSL). We conducted a transcriptomic analysis to find connections between PD and NDGs. To explore how PD may affect the advancement of NDGs, we employed a quantitative framework to analyze the gene expression microarray dataset. To obtain genomic associations between PD and NDGs, we performed gene expression profiling by identifying dysregulated pathways, gene ontologies, PPIs, and phylogenetic analysis. We found 15, 11, 28, and 24 significant genes are shared between PD and AD, ALS, HD, and MSL respectively. We also identified a significant number of functional and ontological pathways, hub proteins and phylogenetic associations that indicate PD may have a link to develop NDGs (AD, ALS, HD, and MSL). We validate our findings through gold benchmark databases. This systematic approach is highly beneficial in understanding the causes and progression of NDGs owing to the effect of PD. © 2023 IEEE.

关键词： Neurodegenerative diseases

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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Few-Shot Learning with Feature Pairing and Mean Discrepancy

Few-Shot Learning with Feature Pairing and Mean Discrepancy

引用

2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Singh, K.K. Hima Bindu, K. Nit Andhra Pradesh and Rgukt Nuzvid Dept. of Computer Science and Engineering India Nit Dept. of Computer Science and Engineering AP India

ISBN: (纸本)9798350333602

Few-Shot Learning (FSL) is a sub-area of machine learning which mainly deals with data where there is a scarcity of training supervised samples. Few shot learning (FSL) more closely resembles the human brain in comparing new concepts to others based on prior experience rather than identifying it exactly. FSL aims to generalize the model across the tasks (in meta learning) opposed to the classical supervised learning which generalizes across the data points. In general the FSL models may suffer from underfitting because of scarcity of supervised samples and at the same time it causes overfitting as it is likely to memorize task specific features of the training set. This work aims to reduce such problems and is presented as a metric based model 'Few Shot Learning with Feature Pairing and Mean Discrepancy' (FL-FPMD). As the title suggests, feature pairing is one among various data augmentations. It is observed that flip augmentation is more suitable in the context of pairing the features within the given task. Memorizing task specific features is reduced by incorporating the discrepancy of mean distributions of the query and the support embedding in the loss function. The training and the evaluation is performed at the miniImageNet dataset and the results indicate that the proposed model outperforms the state-of-the-art models of similar complexity. © 2023 IEEE.

关键词： Embeddings

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SI-LERT (Assistive Device for Hearing Impaired Drivers) 7

SI-LERT (Assistive Device for Hearing Impaired Drivers)

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abhiram, S.P. Umamaheswari, S. Muhammed Shybin, M.S. Saju Campbell, Aromal Sreena, V.G. Raji, N.R. Marian Engineering College Dept of Electronics and Communication Engineering Kerala India Dept of Computer Science Kerala India

ISBN: (纸本)9798350372816

The"SI-LERT"(Assistive Device for Hearing Impaired Drivers), represents an innovative solution aimed at enhancing the safety and accessibility of individuals with hearing impairments while driving, particularly in detecting emergency vehicles. Combining a Raspberry Pi 4 and USB microphone for siren recognition, this device transmits signals to an ESP32 microcontroller integrated into driving glove via WiFi. Upon detection, the microcontroller activates micro-vibrators embedded in the glove, providing tactile feedback, and simultaneously displays an image of the approaching emergency vehicle on an OLED display. This innovative approach not only empowers drivers with hearing impairments to respond effectively to emergencies but also marks a significant advancement in assistive technology, offering a comprehensive solution that combines tactile and visual feedback to enhance road safety and driver independence. While the device shows potential, it needs more work and testing to fix problems like limited number of datasets. © 2024 IEEE.

关键词： Microcontrollers

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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Ashraful Sama Raisa, Syeda Rafiatus Khan, Nahiyan Habib Rifat, Ariful Islam Sylhet Engineering College Dept. of Computer Science and Engineering Sylhet Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, 'LeafyVclassify7BD' with 3306 images and 'LeafyVdisease7BD' with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings. © 2023 IEEE.

关键词： Classification (of information)

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