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neural networks and Their applications in Smart Systems 9th

Neural Networks and Their Applications in Smart Systems

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9th International conference on Smart City applications, SCA 2024

作者： Atmani, Aziz Boumhamdi, Mounir Laboratoire de Recherche Business Intelligence Gouvernance des Organisations Finance et Criminalité Financière Université Hassan II Faculté des Sciences Juridiques Économiques et Sociales de Casablanca No8110 Oasis Casablanca Morocco

ISBN: (纸本)9783031886522

This work investigates the potential of neural networks in the processing of artificial intelligence, especially in smart systems, and explores their applications regarding advancing automation, user experience, and resource management. The methodology will include collecting data from public datasets and international organizations, preprocessing or cleaning, normalization and augmentation of data, followed by the development of different neural network models: Convolutional neural networks for image recognition;Recurrent neural networks and Long Short-Term Memory for speech recognition and time series prediction;hybrids for such complicated tasks as energy consumption forecasting andhealth data analysis. Results are very high for a wide variety of datasets, with CNNs attaining state-of-the-art in image recognition, LSTMs doing the same in speech recognition with very low word error rates, and the hybrids quite effective in predictive analytics and health data analysis. The discussion has been about how neural networks are impacting smart systems in a big way while also presenting the challenges regarding the requirements around data and computational resources and model interpretability. Future directions in this regard may include transfer learning, explainable AI, edge computing, and quantum computing to further enhance neural network capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Long short-term memory

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Improved Moth Flame Optimization with Deep Convolutional neural Network for Colorectal Cancer Classification using Biomedical images 2

Improved Moth Flame Optimization with Deep Convolutional Neu...

引用

2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Parimala, S. Kumutha, R. Iram, Faiza Sunena Rose, M.V. Nanmaran, R. Angelpreethi, A. Department of Computer Science and Applications SRM Institute of Science and Technology Vadapalani India S. A. Engineering College Chennai India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India St.Joseph's College Tiruchirappalli India

ISBN: (纸本)9798350383867

Colorectal cancer (CRC) affects the large intestinal colon. CRC treated radiation, chemotherapy, and immunotherapy. If aberrant cells, which are predicted to become cancer cells, are detected early, CRC may be effectively managed. Histopathological analysis of tissue specimens is the most common way to diagnose CRC. Histopathological examination and appropriate therapy are the only ways to improve cancer survival. AI in colon and lung cancer histopathology analysis may help doctors identify instances faster, cheaper, and easier. Medical image processing may use ML devices for pattern recognition and resolution. This paper presents an Improved Moth Flame Optimisation with Deep Convolutional neural Network (IMFO-DCNN) method for biomedical image-based colorectal cancer classification. The IMFO-DCNN approach uses the DCNN model to automatically extract biomedical picture properties. The IMFO method fine-tunes DCNN model hyperparameters to improve detection performance. The last step uses a Random Forest (RF) classification algorithm to effectively recognise and categorise biological pictures. Methodically estimating the IMFO-DCNN methodology on biological pictures improves CRC classification accuracy and resilience over traditional techniques. CRC detection and classification are efficient and reliable using IMFO-DCNN. © 2024 IEEE.

关键词： Convolutional neural networks

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DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of image Recognition Models 39

DeltaNN: Assessing the Impact of Computational Environment P...

引用

39th IEEE International conference on Software Maintenance and Evolution (ICSME)

作者： Louloudakis, Nikolaos Gibson, Perry Cano, Jose Rajan, Ajitha Univ Edinburgh Edinburgh Midlothian Scotland Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9798350327830

image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the imageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.

关键词： artificial intelligence deep neural networks differential testing image recognition performance software engineering software testing

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Continuous Action Learning Automata Game Optimizer Applied to Convolutional neural networks

Continuous Action Learning Automata Game Optimizer Applied t...

引用

International conference on Communications, Signal processing, and their applications (ICCSPA)

作者： James Lindsay Sidney Givigi Department of Computer and Electrical Engineering Royal Military College of Canada Kingston Canada School of Computing Queen's University Kingston Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

This paper explores the application of the Continuous Action Learning Automata (CALA) game optimizer to Convolutional neural networks (CNNs) for image classification tasks. The CALA game optimizer, initially developed for training artificial neural networks (ANNs), offers a non-gradient descent-based optimization approach that can adapt to different network architectures and activation functions. Leveraging the versatility of the CALA game optimizer, we investigate its performance on CNNs, specifically targeting image recognition within the MNIST dataset. The paper discusses the rationale behind using the CALA game optimizer for CNNs, including its ability to accommodate various activation functions and deeper network architectures. Experimental results demonstrate the efficacy of CALA in training CNNs, showcasing its flexibility and effectiveness in optimizing network parameters for image classification tasks.

关键词： Training image recognition Learning automata Games Network architecture Signal processing Convolutional neural networks Optimization image classification

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Playing Lottery Tickets with Vision and Language 36

Playing Lottery Tickets with Vision and Language

引用

36th AAAI conference on artificial Intelligence / 34th conference on Innovative applications of artificial Intelligence / 12th Symposium on Educational Advances in artificial Intelligence

作者： Gan, Zhe Chen, Yen-Chun Li, Linjie Chen, Tianlong Cheng, Yu Wang, Shuohang Liu, Jingjing Wang, Lijuan Liu, Zicheng Microsoft Corp Redmond WA 98052 USA Univ Texas Austin Austin TX 78712 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781577358763

Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide range of VL tasks. However, the large number of parameters in such models hinders their application in practice. In parallel, work on the lottery ticket hypothesis (LTH) has shown that deep neural networks contain small matching subnetworks that can achieve on par or even better performance than the dense networks when trained in isolation. In this work, we perform the first empirical study to assess whether such trainable subnetworks also exist in pre-trained VL models. We use UNITER as the main testbed (also test on LXMERT and ViLT), and consolidate 7 representative VL tasks for experiments, including visual question answering, visual commonsense reasoning, visual entailment, referring expression comprehension, image-text retrieval, GQA, and NLVR2. Through comprehensive analysis, we summarize our main findings as follows. (i) It is difficult to find subnetworks that strictly match the performance of the full model. However, we can find "relaxed" winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy. (ii) Subnetworks found by task-specific pruning transfer reasonably well to the other tasks, while those found on the pre-training tasks at 60%/70% sparsity transfer universally, matching 98%/96% of the full accuracy on average over all the tasks. (iii) Besides UNITER, other models such as LXMERT and ViLT can also play lottery tickets. However, the highest sparsity we can achieve for ViLT is far lower than LXMERT and UNITER (30% vs. 70%). (iv) LTH also remains relevant when using other training methods (e.g., adversarial training).

关键词： Deep neural networks

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Predicting Brain Age Using Transferable Covariance neural networks 48

Predicting Brain Age Using Transferable Covariance Neural Ne...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Sihag, Saurabh Mateos, Gonzalo McMillan, Corey Ribeiro, Alejandro University of Rochester RochesterNY United States University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9781728163277

The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven changes to brain structure are captured by various neuroimaging modalities. These datasets are characterized by high dimensionality as well as collinearity, hence applications of graph neural networks in neuroimaging research routinely use sample covariance matrices as graphs. We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices using the architecture derived from graph convolutional networks, and we showed VNNs enjoy significant advantages over traditional data analysis approaches. In this paper, we demonstrate the utility of VNNs in inferring brain age using cortical thickness data. Furthermore, our results show that VNNs exhibit multi-scale and multi-site transferability for inferring brain age. In the context of brain age in Alzheimer's disease (AD), our experiments show that i) VNN outputs are interpretable as brain age predicted using VNNs is significantly elevated as compared to the chronological age for AD with respect to healthy subjects for different datasets;and ii) VNNs can be transferable, i.e., VNNs trained on one dataset can be transferred to another dataset with different dimensionality without retraining for brain age prediction. © 2023 IEEE.

关键词： Alzheimer's disease biomarkers brain age covariance matrix Graph convolutional network

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New Optimization: Design of a neural Network Cat and Dog Recognition Prediction Model 5

New Optimization: Design of a Neural Network Cat and Dog Rec...

引用

2023 5th International conference on artificial Intelligence and Computer applications, ICAICA 2023

作者： Zhang, Xinwei Zhang, Longqing Song, Zi'ang Guangdong University of Science and Technology Dongguan China Hebei University of Environmental Engineering Qinhuangdao China

ISBN: (纸本)9798350323313

With the advent of the big data era, there has been a surge in research focused on the application of convolutional neural networks (CNNs) and image processing. Similar to how humans effortlessly identify cats and dogs in our everyday lives, modern machines are also capable of performing this task. One effective approach to achieving this is through leveraging Kaggle's automatic cat and dog identification technology. This study aims to tackle the challenge of recognizing cats and dogs in images with complex appearances by developing a Sequential object-based CNN model. To accomplish this, the cutting-edge deep learning framework PyTorch is utilized, along with high-performance GPUs for computational power. The network is trained and tested separately on images of dogs and cats. Experimental results demonstrate that the CNN model achieves remarkably high recognition accuracy and exhibits exceptional performance in distinguishing between different breeds of dogs and cats. © 2023 IEEE.

关键词： Convolutional neural networks

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A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN Training 41

A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN...

引用

41st IEEE International conference on Computer Design (ICCD)

作者： Chen, Menglei Hua, Yu Bai, Rong Huang, Jianming Huazhong Univ Sci & Technol WNLO Wuhan Hubei Peoples R China

ISBN: (纸本)9798350342918

Distributed deep neural network (DNN) training is important to support artificial intelligence (AI) applications, such as image classification, natural language processing, and autonomous driving. Unfortunately, the distributed property makes the DNN training vulnerable to system failures. Check-pointing is generally used to support failure tolerance, which however suffers from high runtime overheads. In order to enable high-performance and low-latency checkpointing, we propose a lightweight checkpointing system for distributed DNN training, called LightCheck. To reduce the checkpointing overheads, we leverage fine-grained asynchronous checkpointing by pipelining checkpointing in a layer-wise way. To further decrease the checkpointing latency, we leverage the software-hardware codesign methodology by coalescing new hardware devices into our checkpointing system via a persistent memory (PM) manager. Experimental results on six representative real-world DNN models demonstrate that LightCheck offers more than 10x higher checkpointing frequency with lower runtime overheads than stateof-the-art checkpointing schemes. We have released the opensource codes for public use in https://***/LighT-chenml/ ***.

关键词： Computer systems Checkpointing systems Failure tolerance Deep neural networks

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BIBLIOMETRIC RESEARCH: TRACKING HOW THE artificial INTELLIGENCE INNTEGRATION CHANGED THE MEDICAL SYSTEMS

BIBLIOMETRIC RESEARCH: TRACKING HOW THE ARTIFICIAL INTELLIGE...

引用

2024 International conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kaur, Jasneet Kuamr, S. Barath Mehta, Pradnya Ninoria, Shalini Karthikeyan, C. Symbiosis College of Nursing Symbiosis International Deemed University Pune India Department of Biotechnology Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India CCSIT TMU India School of Media Film&Television Studies IIMT University Meerut India Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore641105 India

ISBN: (纸本)9798350355338

In latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chronological overview of AI study in the areas of medical care and health. The online science tool was used to obtain a total number of articles that were released between. The detailed study looked at the number of publications, as well as the cooperation between writers and nations. Generally, indicate that a factor, along with robotics, algorithms, neural networks, artificial cognitive computing, and natural language processing, were identified through a vast network of researchers' key words and phrases and message review of pertinent academic papers. These methods are regularly used in clinical forecasting and rehabilitation. The most articles were on cancer, followed by those on cardiovascular disease, stroke, blindness, Early onset dementia, and sadness. Additionally, the lack of study on applying AI to some illnesses with a high disease prevalence indicates potential paths for Ai development. The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area. © 2024 IEEE.

关键词： Medical robotics

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An Instrument Tone Recognition System Based on artificial Intelligence and Feature Recognition

An Instrument Tone Recognition System Based on Artificial In...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Zheng Hu School of Music Zhaoqing University Zhaoqing City Guangdong China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Instrument tone recognition systems have over time had the highest application value and significance in information retrieval. Notably, the traditional systems and methods often rely on convolutional neural networks (CNN) that serve as fundamental through reportedly, have a poor performance. To this effect, this work seeks to examine a resent artificial intelligence (AI) and a feature recognition system in instrument tone recognition. A proposed model in this case is integrated in an interactive system of humanoid robot. The findings of this study reveal that the proposed resent artificial intelligence (AI) and a feature recognition system in the design of an instrument tone recognition system outperforms the traditional model in terms of recognition accuracy and tonal loss rate. The findings of the research therefore providing methods and ideas that may be used in instrument tonal recognition.

关键词： Emotion recognition image recognition Accuracy Instruments Interactive systems Information retrieval Rhythm Convolutional neural networks Timbre artificial intelligence

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