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Classification of Kidney Diseases Using Deep Neural Networks

Classification of Kidney Diseases Using Deep Neural Networks

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Kristipati thoyajaksha Kashyap Moturu Bhuvi Parimi Sunayana Dudam Neeraj Dattu Lina Chato Department of Computer Science University Of South Dakota Vermillion South Dakota

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Kidney-related diseases such as tumors, cysts, and stones are common, so prompt and precise diagnosis is essential for better patient outcomes. In this work, we propose developing an automatic Deep Learning (DL) model based on various DL structures and learning techniques to create an efficient prediction model for diagnosis of kidney diseases and disorders. A publicly available dataset of computer Tomography (CT) scan images with three kidney disease categories (tumor, cyst, and stone), as well as the normal category, are used to test our proposed DL multi-classification structures: Convolutional Neural Network (CNN), CNN with transfer learning, Vision Transformer (ViT) DL based model, and ViT transfer learning-based model. Transfer learning from state-of-the-art pre-trained DL models, including VGG16, ResNet50, and ViT, are used to evaluate its effectiveness in improving performance and reducing the dependency on large training datasets. Preprocessing and data augmentation are applied to enhance image quality and ensure reliable model performance. the Vision Transformer with transfer learning achieved a high test accuracy of 99.6% on test data, with accuracy improvements of 1.13% and 1.46% compared to CNN transfer learning based and CNN classical learning based, respectively.

关键词： computer vision Accuracy Computational modeling Transfer learning Predictive models Transformers Convolutional neural networks Kidney Diseases Tumors

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UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Network for Short Text Classification 15

UA-HGAT: Uncertainty-aware Heterogeneous Graph Attention Net...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of things, ithings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Kong, DeYan Ji, ZhenYan Sang, Yanjuan Dong, Wei Yang, Yanyan Beijing Jiaotong University School of Software Engineering Beijing China Blueintelligence Technology Co.Ltd. R&d Department Wuhan China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing100190 China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

During the last decade, deep learning has played a significant role in improving text classification which is widely applied in question answering systems, comment extraction, sentiment analysis, etc. Considering that few labeled training data are available, semi-supervised short text classification needs to be studied urgently. Existing works focus on long texts and cannot achieve satisfactory performance on short texts due to the sparsity, non-standardization, and insufficient labeled data. In this paper, we propose a novel uncertainty-aware heterogeneous graph attention network for semi-supervised short text classification (UA-HGAT), which makes full use of limited labeled data and large amounts of unlabeled data via exploiting an uncertainty-aware mechanism. In the model, entity description from Wikipedia is introduced to empower short texts, and word-level information is acquired by Word2Vec. the entity description and word-level information are combined with topic information to address the issue of short text sparseness. In particular, we introduce uncertainty-aware mechanism and negative sample learning to relieve the erroneous high confidence predictions from poorly calibrated models, so that it can offer enough pseudo-label samples for the model training. Extensive experiments show that our model significantly outperforms the SOTA methods on AGNews and Snippets datasets. © 2022 IEEE.

关键词： Sentiment analysis

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Enhanced Plant Disease Detection Using a Custom CNN with Advanced Feature Extraction Techniques

Enhanced Plant Disease Detection Using a Custom CNN with Adv...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Shubham Sharma Manu Vardhan National Institute of Technology Raipur Chhattisgarh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Prompt and precise identification of plant diseases is essential to guarantee agricultural output and food security. Conventional techniques that depend on visual examinations are inefficient, based on personal judgment, and frequently cannot be expanded to larger scales, emphasizing the necessity for automated alternatives. Recent advancements in convolutional neural networks (CNNs) have shown promise in addressing these challenges, offering high accuracy in image classification tasks. However, existing CNN architectures often require extensive computational resources, limiting their real-time application and scalability. Ensemble methods, while improving accuracy, also increase model complexity and deployment difficulty. To address these issues, we propose a custom CNN architecture specifically designed for plant disease detection. Our model includes advanced features like depthwise separable convolutions, dilated convolutions, and an attention mechanism to improve the efficiency of feature extraction and focus on essential parts of the image. these enhancements ensure high classification performance without imposing excessive computational demands. Our experimental results demonstrate the superiority of the custom CNN, achieving a remarkable validation accuracy of $\mathbf{9 9 . 4 4 \%}$, precision of 0.98, recall of 0.97, and an $F 1$ score of 0.97. By the fifth epoch, the model’s training process shows a consistent decrease in loss, with the training loss dropping to 0.0245 and the validation loss to 0.0196. these results underscore the model’s ability to generalize effectively to unseen data while maintaining computational efficiency suitable for real-time applications. the proposed custom CNN significantly outperforms traditional CNN architectures and ensemble methods in plant disease detection.

关键词： Training Plant diseases Adaptation models Accuracy Computational modeling computer architecture Feature extraction Real-time systems Convolutional neural networks Load modeling

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Model of a management system for deterministic scientific services of digital platform 14

Model of a management system for deterministic scientific se...

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14th International symposium on Intelligent Systems

作者： Denisov, S. A. Sorokin, A. A. Russian Acad Sci Fed Res Ctr Comp Sciense & Control 44 Vavilova StKorpus 2 Moscow 119333 Russia Russian Acad Sci Comp Ctr Far Eastern Branch 65 Kim Yu Chen St Khabarovsk 680000 Russia

the article presents the results of experimental studies of the user interaction algorithm with a digital platform in terms of data exchange and high-performance computing on the model of a management system for deterministic scientific services of digital platform developed at FRC CSC RAS. the results allow us to evaluate the practical aspects of the functioning of the digital platform for scientific research, designed to effectively organize scientific research and manage the scientific instrument base in the interests of a wide range of research teams and industrial consumers. (c) 2021 the Authors. Published by Elsevier B.V.

关键词： Scientific service consolidated scientific service digital platform cloud computing service-oriented architecture microservices flexible integration

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computing and Listing Avoidable Vertices and Paths 15th

Computing and Listing Avoidable Vertices and Paths

引用

15th Latin American symposium on theoretical Informatics

作者： Papadopoulos, Charis Zisis, Athanasios E. Univ Ioannina Dept Math Ioannina Greece

ISBN: (纸本)9783031206238;9783031206245

A simplicial vertex of a graph is a vertex whose neighborhood is a clique. It is known that listing all simplicial vertices can be done in O(nm) time or O(n(omega)) time, where O(n(omega)) is the time needed to perform a fast matrix multiplication. the notion of avoidable vertices generalizes the concept of simplicial vertices in the following way: a vertex u is avoidable if every induced path on three vertices with middle vertex u is contained in an induced cycle. We present algorithms for listing all avoidable vertices of a graph through the notion of minimal triangulations and common neighborhood detection. In particular we give algorithms with running times O(n(2) m) and O(n(1+omega)), respectively. Additionally, based on a simplified graph traversal we propose a fast algorithm that runs in time O(n(2) + m(2)) and matches the corresponding running time of listing all simplicial vertices on sparse graphs with m = O(n). Moreover, we show that our algorithms cannot be improved significantly, as we prove that under plausible complexity assumptions there is no truly subquadratic algorithm for recognizing an avoidable vertex. To complement our results, we consider their natural generalizations of avoidable edges and avoidable paths. We propose an O(nm)-time algorithm that recognizes whether a given induced path is avoidable.

关键词： Graph theory

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Incentivizing Crowdsensing in IoT through Micropayments: A Simulation Study 25

Incentivizing Crowdsensing in IoT through Micropayments: A S...

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40th Annual ACM symposium on Applied computing, SAC 2025

作者： Serena, Luca Manzoni, Pietro Marzolla, Moreno D'angelo, Gabriele Ferretti, Stefano Department of Computer Science and Engineering University of Bologna Bologna Italy Computer Engineering Department Universitat Politècnica de València Valencia Spain Department of Computer Science and Engineering Center for Inter-Department Industrial Research ICT University of Bologna Cesena Italy

ISBN: (纸本)9798400706295

Many IoT applications rely on the timely collection and processing of data. Although the technology that allows data collection is readily available, its deployment on a large scale raises many practical issues, the main one probably being: who pays for the infrastructure? IoT deployment could significantly benefit from crowdsourcing: to ensure the successful deployment of a data collection infrastructure, it is crucial to motivate end users, as their active participation and engagement are key to the system's effectiveness and reach. this paper addresses these issues in a practical use case that deals with collecting environmental data. We propose a decentralized crowdsensing architecture where vehicles act as data collectors and transfer data from sources (e.g., sensing devices) to networked access points. the system provides economic incentives to individuals willing to act as data collectors or to operate gateways in the form of micropayments enabled by a blockchain. the proposed architecture is evaluated using a multilevel simulation model that combines existing communication, mobility, and behavioral sub-models. this reduces the time required to build a full simulator and potentially increases the accuracy of the results. Copyright © 2025 held by the owner/author(s).

关键词： Data collection

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Pacific Rim International symposium on Dependable computing

作者： Md Mashrur Arifin Jyh-Haw Yeh Computer Science Department Boise State University Idaho USA

ISBN: (数字)9798331540746

ISBN: (纸本)9798331540753

Generative Adversarial Networks (GANs) have revolutionized the generation of synthetic malware images, providing significant applications in cybersecurity. Traditional image-based metrics such as Inception Score (IS) and Fréchet Inception Distance (FID) evaluate the visual quality of these images but fail to capture their malicious nature fully. We propose a novel evaluation framework, cTIMS (Correlated Textual-Based and Image-Based Metric Suites), which integrates both image and text-based metrics to provide a comprehensive assessment. Utilizing the multimodal image captioning model BLIP, we extract textual descriptions of GAN-synthesized malware images and analyze the correlation between image metrics (IS, FID) and text metrics (BLEU, METEOR, ROUGE). Additionally, we introduce Kolmogrov-Arnold Network (KAN)-based CNN architectures (VGG16-KAN, VGG19-KAN), demonstrating significant performance improvements in malware classification. the effectiveness of the Train Real Test Synthetic (TRTS) approach in validating these correlations is also evaluated, confirming the method’s reliability in selecting high-quality GAN-generated images.

关键词： Measurement Visualization Analytical models Correlation computer architecture Generative adversarial networks Malware Reliability Meteors computer crime

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Optimizing CRM Platforms with Distributed Cloud architectures for Scalable performance and Security

Optimizing CRM Platforms with Distributed Cloud Architecture...

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Innovative Approaches in Smart Technologies (ISAS), International symposium on

作者： Marian Ileana Pavel Petrov Vassil Milev Interdisciplinary Doctoral School National University of Science and Technology POLITEHNICA Bucharest Pitesti University Center Pitesti Romania Department of Computer Systems and Technologies St. Cyril and St. Methodius University of Veliko Tarnovo Veliko Tarnovo Bulgaria

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

In today’s increasingly dynamic business environment, optimizing Customer Relationship Management (CRM) platforms is essential to increase operational efficiency and provide the best possible user experience. the paper presents an approach that aims to improve the performance and security of CRM platforms by integrating Distributed Cloud architectures. the proposed solution leverages the scalability offered by cloud solutions to ensure high availability and low response time while addressing data security challenges through decentralized frameworks. Key metrics are evaluated to improve user performance. By decentralizing data and using robust encryption mechanisms, a high level of security and compliance with data protection regulations is ensured. the analysis suggests that this type of architecture provides a modern solution and a scalable and secure foundation for CRM platforms, thus supporting digitization and business growth in an increasingly digitally interconnected world.

关键词： Cloud computing Costs Scalability Systems operation Customer relationship management Organizations User experience Regulation Reliability Protection

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Innovations in Real-Time Speech Translation: Leveraging Griffin-Lim Algorithm

Innovations in Real-Time Speech Translation: Leveraging Grif...

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International Conference on computing and Networking Technology (ICCNT)

作者： Himanshu Maithani Aditya Giri Goswami R Anita J D Dorathi Jayaseeli Sourabh Tiwari Rashmi T Shankarappa Computing Technologies SRM Institute of Science and Technology Chennai India Voice Intelligence Group Samsung R&D Institute Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

At present, speech-to-speech translator systems often involve multiple intermediary steps, such as automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech generation (TTS), leading to latency in response time and error propagation to later stages. Furthermore, they face challenges when dealing with languages lacking text representation. this paper examines the feasibility of a direct speech-to-speech translator model trained without the use of intermediate text representations during both training and inference. Employing the Librosa library for feature extraction, our approach uses Mel-frequency cepstral coefficients (MFCCs) in conjunction with a sequence-to-sequence Recurrent Neural Network (RNN) model for training on a dataset of German-to-English audio. Our model achieved a BLEU score of 23.5, demonstrating competitive performance. the Perceptual Evaluation of Speech Quality (PESQ) score of 2.9 reveals high-quality generated speech. the average time delay for translation is ${1. 5}$ seconds, significantly lower than the 2.5 to 3 seconds observed in traditional cascade models. Our experiments, including noise-free speech generation using the Griffin Lim algorithm, underline the potential of our approach for real-time applications and lay a promising foundation for future research.

关键词： Training Accuracy Computational modeling computer architecture Real-time systems Libraries Text to speech Mel frequency cepstral coefficient Speech processing Context modeling

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Deep Neural Network Inference Partitioning in Embedded Hybrid Analog-Digital Systems

Deep Neural Network Inference Partitioning in Embedded Hybri...

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IEEE International symposium on Quality Electronic Design

作者： Fabian Kreß Julian Hoefer Qiushi Lin Patrick Schmidt Zhenhua Zhu Yu Zhu Tanja Harbaum Yu Wang Jürgen Becker Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Tsinghua University Beijing China

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Deep Neural Networks (DNNs) deployed in resource-constrained embedded systems, like automotive platforms, often face severe “memory wall” problems and energy-consuming data movements. To address this challenge, existing work has proposed Processing-In-Memory (PIM) architectures based on emerging non-volatile memory, which perform analog-domain computations inside memory, offering great potentials to solve the “memory wall” problem. However, PIM-based accelerators suffer from circuit noise, impacting DNN accuracy. therefore, building a heterogeneous multi-chiplet architecture based on energy-efficient analog PIM and high-precision digital computing units has become a promising DNN acceleration solution that achieves both high energy efficiency and high accuracy. In this paper, we present an automated framework to explore layer-wise DNN inference partitioning in hybrid analog-digital multi-chiplet systems. After performing a topological ordering, it analyzes the robustness of each layer to constrain mapping decisions across available analog PIM chiplets and digital chiplets. By considering several functional and performance metrics, the framework identifies Pareto-optimal partitioning schemes. Exten-sive experimental results on a variety of DNN models show a reduction in latency of up to 52% with a loss in accuracy of less than 1%.

关键词： Accuracy Quantization (signal) Chiplets architecture Scalability Artificial neural networks computer architecture Robustness Energy efficiency Analog-digital conversion

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