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20th International Conference on artificial Intelligence applications and Innovations (AIAI)

作者： Tsiolakis, Theodoros Pavlidis, Nikolaos Perifanis, Vasileios Efraimidis, Pavlos S. Democritus Univ Thrace Dept Elect & Comp Engn Xanthi Greece

ISBN: (纸本)9783031632211;9783031632198;9783031632181

Cellular traffic forecasting is an essential task that enables network operators to perform resource allocation and anomaly mitigation in fast-paced modern environments. However, the increasing amount of data collected by respective base stations makes their processing and analysis challenging. Machine learning (ML) algorithms have emerged as a powerful tool that can handle the large volumes of data and provide operators with accurate predictions. The environmental impact of such algorithms is, however, often overlooked in favor of predictive performance. Thus, a sustainable solution that takes into consideration the power efficiency of the considered ML models is crucial to mitigate the risks of the ongoing climate crisis shaping our future world. In this work, we utilize bioinspired spiking neural networks (SNNs) for the task of cellular traffic forecasting, as an answer to the power consumption challenge. We compare the spiking-based models against baseline architectures, i.e., Convolutional neural Network (CNN) and Multi-Layer Perceptron (MLP), in terms of both predictive accuracy and associated energy demands using data collected from three different locations in Barcelona, Spain. Our results show that SNNs can lead to a decrease in energy costs while maintaining the quality of predictions.

关键词： Machine learning Spiking neural networks Cellular traffic prediction Sustainable AI

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Deep Learning-Based Eye Gaze Estimation for Automotive applications Using Knowledge Distillation

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IEEE ACCESS 2023年 11卷 120741-120753页

作者： Orasan, Ioan Lucan Bublea, Adrian-Ioan Caleanu, Catalin Daniel Politehn Univ Timisoara Fac Elect Telecommun & Informat Technol Dept Appl Elect Timisoara 300006 Romania

Deep neural networks are currently applied in multiple domains, especially in the automotive industry. The main reason for this is related to the more complex challenges found in the field of signal processing, especially when the tasks involve image and video data types. Using conventional/statistical algorithms to deal with these high-complexity challenges is no longer a viable approach. Therefore, the involvement of artificial intelligence solutions like deep neural networks has significantly increased. In recent years, numerous architectures have been developed with the aim of maximizing performance. However, their size and computation requirements have increased at the same time. For this reason, special attention is currently being paid to the optimization of deep neural networks while trying to maintain (almost) the same performance. In this work, we aim to tackle the problem of eye gaze estimation considered within the automotive framework. Our proposal uses a knowledge distillation concept applied to a custom CNN architecture, called the teacher model. Based on this, several CNN student models are derived using layerwise and widthwise compression techniques. Furthermore, they are evaluated with respect to certain performance metrics, e.g. neural network size and inference time. In the experimental results, we propose certain compression methods which can address specific user requirements like model size, accuracy, and inference time. Finally, the student models are evaluated using an EdgeAI embedded device (STM32H747I-DISCO) in terms of accuracy, memory utilization, MACC complexity, and inference time. The combination of layerwise and widthwise compression results as the optimal method to derive student models with a good trade-off between the above-mentioned metrics. Using knowledge distillation, the accuracy can be improved by up to 9.5% over the conventional training procedure.

关键词： ARM Cortex-M convolutional neural networks eye gaze estimation knowledge distillation microcontrollers

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Sparse Auto Encoder Based Deep neural Network for image processing Technology 2

Sparse Auto Encoder Based Deep Neural Network for Image Proc...

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2nd IEEE International Conference on networks, Multimedia and Information Technology, NMITCON 2024

作者： Yang, Zufang Zeng, Heqiong Wuhan Technology and Business University College of Artificial Intelligence Wuhan China Wuhan Technology and Business University Institute of Information and Intelligent Engineering Applications Wuhan China

ISBN: (纸本)9798350372892

The image processing technology has been widely used in various field including video and image surveillance automatic capture the various application in image way representing. However, image process in different way and maximum amount of linear layer then easily fall into local optimum and occur overfitting in learn the data. This paper proposed Sparse Auto Encoder based Deep neural Network (SAE-DNN) for image classification is efficiently achieve high accuracy especially noisy data and dimensionality reduces. The SAE-DNN technique help to remove noise in data by learning the image pattern efficiently, which improve classification accuracy in noisy environments. Initial data is obtained from CIFAR-100 dataset and Pre-processing involves data augmentation, where transformations like translation and rotation are frequently applied during training phase of neural networks for image classification. The MobileNetV2 involved feature extraction, which efficiently extracts relevant features from training data. The proposed SAE-DNN technique are evaluated achieve better outcomes such as accuracy of 92.02% on CIFAR100 dataset existing technique like Fully Convolutional neural Network (FCNN) and Mobile Net V2 with Wavelet-Attention Convolutional neural Network (WA-CNN) are evaluated for proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Semantic segmentation of urban land classes using a multi-scale dataset

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第2期45卷 653-675页

作者： Wang, Qian Hu, Chunhua Wang, Hanzhao Wang, Rui Xie, Yuning Zhao, Yuankun Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing Jiangsu Provinc Peoples R China Nanjing Forestry Univ Coll Artificial Intelligence Nanjing Jiangsu Provinc Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Jiangsu Provinc Peoples R China Nanjing Forestry Univ Coll Artificial Intelligence Nanjing 210037 Jiangsu Provinc Peoples R China

The use of remote sensing imagery for land cover and land use classification has made significant advancements in recent years. However, it becomes particularly challenging to enhance the semantic representation of high-resolution networks while dealing with uneven land categories and merging multi-scale data without compromising the accuracy of semantic segmentation. To tackle this challenge, this paper presents a novel method for classifying high-resolution remote sensing images based on a deep neural network that performs semantic segmentation of urban construction lands into five categories: vegetation, water, buildings, roads, and bare soil. The network incorporates a U-shaped high-resolution neural network and the advanced high-resolution network (HRNet) framework. The parallel storage of feature maps with different resolutions enables the exchange of information between them. The data pre-processing module addresses the issue of data imbalance in the semantic segmentation of urban construction lands, resulting in an increase in Intersection over Union (IoU) values for different land types by 3.75%-12.01%. Additionally, a target context representation module is introduced to enhance the feature representation of pixels by calculating the relationship between pixels and multiple target regions. Moreover, a polarization attention mechanism is proposed to extract the characteristics of geographical objects in all directions and achieve a stronger semantic representation. This method provides a novel approach to accurately and effectively extract information on construction lands and advance the development of monitoring algorithms for urban construction lands. To validate the proposed U-HRNet-OCR+PSA network, a comparative analysis was conducted with six classical networks, including DeepLabv3+, PSPNet, U-Net, U-Net++, HRNet, and HRNet-OCR, as well as the relatively new ViT-adapter-L, Oneformer and Internimage-H. The experiments demonstrate that the U-HRNet-OCR+P

关键词： applications image processing land use - land cover

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Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2025年第1期24卷 1-100页

作者： Luo, Xiangzhong Liu, Di Kong, Hao Huai, Shuo Chen, Hui Xiong, Guochu Liu, Weichen Nanyang Technol Univ Nanyang Singapore Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs in real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate this computational gap and enable ubiquitous embedded intelligence, we focus in this survey on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems. We also envision promising future directions and trends, which have the potential to deliver more ubiquitous embedded intelligence. We believe th

关键词： Embedded computing systems embedded intelligence artificial intelli- gence efficient deep learning algorithms efficient network design efficient neural architecture search effi- cient model compression efficient on-device learning efficient large language models efficient deep learning software and hardware intelligent embedded applications

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Revolutionizing crop disease detection with computational deep learning: a comprehensive review

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第3期196卷 302-302页

作者： Ngugi, Habiba N. Ezugwu, Absalom E. Akinyelu, Andronicus A. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Ave ZA-3201 Pietermaritzburg KwaZulu Natal South Africa North West Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Univ Free State Dept Comp Sci & Informat Bloemfontein South Africa Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Sunway Univ Sch Engn & Technol Petaling Jaya 27500 Malaysia Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Yuan Ze Univ Coll Engn Taoyuan Taiwan

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocat

关键词： Plant disease detection Severity estimation Deep learning Machine learning

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IoT-Based Multisensors Fusion for Activity Recognition via Key Features and Hybrid Transfer Learning

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IEEE ACCESS 2025年 13卷 14727-14742页

作者： Jalal, Ahmad Khan, Danyal Sadiq, Touseef Alotaibi, Moneerah Alotaibi, Sultan Refa Aljuaid, Hanan Rahman, Hameedur Air Univ Fac Comp & AI Islamabad 44000 Pakistan Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Univ Agder Ctr Artificial Intelligence Res Dept Informat & Commun Technol N-4879 Grimstad Norway Shaqra Univ Dept Comp Sci Coll Sci & Humanities Dawadmi Shaqra Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia

Human activity recognition (HAR) has attracted significant attention in various fields, including healthcare, smart homes, and human-computer interaction. Accurate HAR can enhance user experience, provide critical health insights, and enable sophisticated context-aware applications. This paper presents a comprehensive system for HAR utilizing both RGB videos and inertial measurement unit (IMU) sensor data. The system employs a multi-stage processing pipeline involving preprocessing, segmentation, feature extraction, and classification to achieve high accuracy in activity recognition. In the preprocessing stage, frames are extracted from RGB videos, and IMU sensor data undergoes denoising. The segmentation phase applies Naive Bayes segmentation for video frames and Hamming windows for sensor data to prepare them for feature extraction. Key features are extracted using techniques such as ORB (Oriented FAST and Rotated BRIEF), MSER (Maximally Stable Extremal Regions), DFT (Discrete Fourier Transform), and KAZE for image data, and LPCC (Linear Predictive Cepstral Coefficients), PSD (Power Spectral Density), AR Coefficient, and entropy for sensor data. Feature fusion is performed using Linear Discriminant Analysis (LDA) to create a unified feature set, which is then classified using ResNet50 (Residual neural Network) to recognize activities such as using a smartphone, cooking, and reading a newspaper. The system was evaluated using the LARa and HWU-USP datasets, achieving classification accuracies of 92% and 93%, respectively. These results demonstrate the robustness and effectiveness of the proposed HAR system in diverse scenarios.

关键词： Feature extraction Videos image segmentation Human activity recognition Accuracy Noise Convolutional neural networks Data mining Residual neural networks Noise reduction Human activity recognition (HAR) RGB videos IMU sensor data Naive Bayes segmentation recurrent neural network (RNN) Shapley additive explanations (SHAP)

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Insights Into YOLOv9-Based Breast Mass Detection Using Ultrasound images 13

Insights Into YOLOv9-Based Breast Mass Detection Using Ultra...

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13th International Conference on image processing Theory Tools and applications

作者： Erragzi, Nouhaila Moussaid, Abdelghani Zrira, Nabila Benmiloud, Ibtissam Sebihi, Rajaa Ngote, Nabil Mohammed V Univ Fac Sci LPHE Modeling & Simulat Rabat Morocco Natl Super Sch Mines Rabat CPS2E Lab MECAtron Team Rabat Morocco Natl Super Sch Mines Rabat LISTD Lab ADOS Team Rabat Morocco Natl Super Sch Mines Rabat Abulcasis Int Univ Hlth Sci Rabat Morocco

ISBN: (纸本)9798331541859;9798331541842

The most prevalent form of cancer globally is breast cancer, which predominantly impacts women. Early detection ensures successful treatment of breast cancer, significantly improving patients' survival chances. Various imaging modalities, including mammography and ultrasound, are utilized for breast cancer screening. Incorporating new technologies is essential for better patient management, particularly for those with malignant masses. artificial intelligence can assist radiologists by training neural networks to detect breast lesions on mammograms or ultrasounds using deep learning techniques. In this article, the YOLOv9 network is trained on two public ultrasound databases, UDIAT and BUSIS. The network successfully localized malignant and benign masses with a precision of 83%, a recall of 82%, and a mAP of 87% in the UDIAT dataset. In the BUSIS dataset, our model achieved a precision of 75%, a recall of 88%, and a mAP of 90%. Furthermore, we used real Moroccan cases to evaluate the model's performance.

关键词： Ultrasound artificial Intelligence Deep Learning Breast Lesion Detection YOLOv9

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Fault detection of induction machine using artificial neural networks 8

Fault detection of induction machine using artificial neural...

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8th IEEE International Conference on image and Signal processing and their applications, ISPA 2024

作者： Bentrad, Moutaz Bellah Ghoggal, Adel Bahi, Tahar Bouziane, Seif Eddine LGEB Mohamed Khider University Electrical Department Annaba Algeria LGEB Mohamed Khider University Electrical Department Biskra Algeria LASA Badji Mokhtar University Electrical Department Annaba Algeria National School of Artificial Intelligence Intelligent Systems Engineering Department Algiers Algeria

ISBN: (纸本)9798350309249

The diagnosis and monitoring of industrial machinery faults is crucial to the Industrial Revolution but is frequently difficult and labor-intensive. Due to its quick computation, accurate prediction and robustness in performance, the use of artificial intelligence techniques has become a crucial component of condition monitoring of rotating machinery. This paper aims to use the artificial intelligence (neural network technique) to be able to detect and identify air gap eccentricity (static and mixed) and its influence on the stator current signature, the aim is to demonstrate the reliability and accuracy of these techniques to help us make the decision, The achieved simulation results allow distinguishing the types of eccentricity based on the Motor Current Signature Analysis technique data. © 2024 IEEE.

关键词： Induction machine

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Are Emerging Machine Learning Models Dependable at the Nanoscales?

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IEEE NANOTECHNOLOGY MAGAZINE 2024年第6期18卷 33-42页

作者： Liu, Shanshan Wang, Ziheng Gao, Zhen Reviriego, Pedro Niknia, Farzad Tang, Xiaochen Zhou, Jun Lombardi, Fabrizio Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Northeastern Univ Dept Elect & Comp Engn Boston MA 02215 USA Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Politecn Madrid ETSI Telecomunicac Madrid 28040 Spain

Machine learning models by emerging neural networks (NNs) are being developed to solve advanced and complex tasks;for example, multi-branch NNs (such as Siamese and Triplet networks) have been proposed to perform similarity checking tasks, and large-scale models (such as Bidirectional Encoder Representations from Transformers (BERT) and Stable Diffusion) have been developed for nature language and image processing. These emerging NNs that often have a large model size, are potentially resilient to small errors during computation;however, when considering random errors originating from the underlying hardware implementation (such as soft errors on nanoscale circuits induced by radiation or voltage scaling), their complexity poses an obvious question: can these models still operate dependably when implemented by nano-scaled CMOS technologies? This article explores this concern by analyzing and evaluating the dependability of emerging NNs in the presence of hardware induced errors;the results are expected to provide insights for relevant error protection techniques when applying these models in safety-critical applications.

关键词： Computational modeling Encoding Bidirectional control artificial neural networks Hardware Nanoscale devices Analytical models Transformers Training Machine learning neural networks Semiconductor device modeling Voltage control CMOS technology Performance evaluation Emerging neural network multi-branch model large-scale model soft error dependability

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