364 results
Search Results
2. Automation of Rice Leaf Diseases Prediction Using Deep Learning Hybrid Model VVIR
- Author
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Gouse, Sheikh, Dulhare, Uma N., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Rajagopal, Sridaran, editor, Faruki, Parvez, editor, and Popat, Kalpesh, editor
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- 2022
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3. Deep Time Series Forecasting Models: A Comprehensive Survey.
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Liu, Xinhe and Wang, Wenmin
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DEEP learning ,ARTIFICIAL neural networks ,TIME series analysis ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,LANGUAGE models - Abstract
Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach
- Author
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Jake Y. Akers, Su Kara, and Peter Chang
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Computer science ,Radiography ,Convolutional neural network (CNN) ,Endotracheal tube (ETT) ,MIMIC-CXR ,Carina ,Convolutional neural network ,Cross-validation ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,030202 anesthesiology ,Iterative refinement ,Position (vector) ,Classifier (linguistics) ,Intubation, Intratracheal ,Humans ,Radiology, Nuclear Medicine and imaging ,Original Paper ,Radiological and Ultrasound Technology ,business.industry ,Deep learning ,Pattern recognition ,Computer Science Applications ,Trachea ,Binary classification ,Neural Networks, Computer ,Artificial intelligence ,business ,Chest radiograph (CXR) - Abstract
Rapid and accurate assessment of endotracheal tube (ETT) location is essential in the intensive care unit (ICU) setting, where timely identification of a mispositioned support device may prevent significant patient morbidity and mortality. This study proposes a series of deep learning-based algorithms which together iteratively identify and localize the position of an ETT relative to the carina on chest radiographs. Using the open-source MIMIC Chest X-Ray (MIMIC-CXR) dataset, a total of 16,000 patients were identified (8000 patients with an ETT and 8000 patients without an ETT). Three different convolutional neural network (CNN) algorithms were created. First, a regression loss function CNN was trained to estimate the coordinate location of the carina, which was then used to crop the original radiograph to the distal trachea and proximal bronchi. Second, a classifier CNN was trained using the cropped inputs to determine the presence or absence of an ETT. Finally, for radiographs containing an ETT, a third regression CNN was trained to both refine the coordinate location of the carina and identify the location of the distal ETT tip. Model accuracy was assessed by comparing the absolute distance of prediction and ground-truth coordinates as well as CNN predictions relative to measurements documented in original radiologic reports. Upon five-fold cross validation, binary classification for the presence or absence of ETT demonstrated an accuracy, sensitivity, specificity, PPV, NPV, and AUC of 97.14%, 97.37%, 96.89%, 97.12%, 97.15%, and 99.58% respectively. CNN predicted coordinate location of the carina, and distal ETT tip was estimated within a median error of 0.46 cm and 0.60 cm from ground-truth annotations respectively. Overall final CNN assessment of distance between the carina and distal ETT tip was predicted within a median error of 0.60 cm from manual ground-truth annotations, and a median error of 0.66 cm from measurements documented in the original radiology reports. A serial cascaded CNN approach demonstrates high accuracy for both identification and localization of ETT tip and carina on chest radiographs. High performance of the proposed multi-step strategy is in part related to iterative refinement of coordinate localization as well as explicit image cropping which focuses algorithm attention to key anatomic regions of interest.
- Published
- 2021
5. Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms.
- Author
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Li, Xingdong, Wang, Xinyu, Sun, Shufa, Wang, Yangwei, Li, Sanping, and Li, Dandan
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CONVOLUTIONAL neural networks ,WILDFIRES ,ARTIFICIAL neural networks ,FOREST fires ,FIREFIGHTING ,WILDFIRE prevention ,ARTIFICIAL intelligence - Abstract
The prediction of wildfire spreading is necessary for managing and fighting the forest fire. The traditional models require higher accuracy of the input parameters, which is impossible in real forest fires. The paper proposed a fire-spreading model based on the dynamic data of the fire field to improve its adaptability. The model is designed using a convolutional neural network with mixed-inputs and attention mechanisms (MI-AM-CNN). It predicts the burn map after a period of time through the multiple-channel image containing terrain variables and the current burn map, and the scalars containing climate variables. The channel and spatial attention modules are integrated to handle the advanced features that contain important fire variables information and strengthen the influence of important features on the prediction. Based on the FARSITE, a large number of data sets are generated for training, validating, and testing the models in the paper. The proposed model MI-AM-CNN is compared with the state-of-the-art neural network models. Quantitative results show that MI-AM-CNN has the highest performance in predicting effectiveness and efficiency, and it can be applied recursively to get the continuous predicted results. In addition, the prediction results of MI-AM-CNN on the historical fire data demonstrate the ability of its application in the real fire case. The MI-AM-CNN can be used as a predictive method in firefighting operations, and its predicted results can provide theoretical support for the forest fire spread prediction method based on artificial intelligence. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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6. Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions
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C. H. van der Vaart, Anique T. M. Grob, F. van den Noort, M. K. van de Waarsenburg, M. van Stralen, Cornelis H. Slump, and Multi-Modality Medical Imaging
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Valsalva Maneuver ,Intraclass correlation ,UT-Hybrid-D ,Urogenital System ,convolutional neural network ,Gestational Age ,Convolutional neural network ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Pregnancy ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Segmentation ,030212 general & internal medicine ,Abdominal Muscles ,Ultrasonography ,Original Paper ,030219 obstetrics & reproductive medicine ,Radiological and Ultrasound Technology ,business.industry ,Deep learning ,segmentation ,deep learning ,Obstetrics and Gynecology ,Echogenicity ,Pattern recognition ,Pelvic Floor ,transperineal ultrasound ,General Medicine ,Convolutional Neural Network (CNN) ,Original Papers ,Hausdorff distance ,medicine.anatomical_structure ,Reproductive Medicine ,Evaluation Studies as Topic ,Test set ,urogenital hiatus ,Female ,puborectalis muscle ,Artificial intelligence ,Nerve Net ,business ,Puborectalis muscle ,Muscle Contraction - Abstract
Objectives To measure the length, width and area of the urogenital hiatus (UH), and the length and mean echogenicity (MEP) of the puborectalis muscle (PRM), automatically and observer‐independently, in the plane of minimal hiatal dimensions on transperineal ultrasound (TPUS) images, by automatic segmentation of the UH and the PRM using deep learning. Methods In 1318 three‐ and four‐dimensional (3D/4D) TPUS volume datasets from 253 nulliparae at 12 and 36 weeks' gestation, two‐dimensional (2D) images in the plane of minimal hiatal dimensions with the PRM at rest, on maximum contraction and on maximum Valsalva maneuver, were obtained manually and the UH and PRM were segmented manually. In total, 713 of the images were used to train a convolutional neural network (CNN) to segment automatically the UH and PRM in the plane of minimal hiatal dimensions. In the remainder of the dataset (test set 1 (TS1); 601 images, four having been excluded), the performance of the CNN was evaluated by comparing automatic and manual segmentations. The performance of the CNN was also tested on 117 images from an independent dataset (test set 2 (TS2); two images having been excluded) from 40 nulliparae at 12 weeks' gestation, which were acquired and segmented manually by a different observer. The success of automatic segmentation was assessed visually. Based on the CNN segmentations, the following clinically relevant parameters were measured: the length, width and area of the UH, the length of the PRM and MEP. The overlap (Dice similarity index (DSI)) and surface distance (mean absolute distance (MAD) and Hausdorff distance (HDD)) between manual and CNN segmentations were measured to investigate their similarity. For the measured clinically relevant parameters, the intraclass correlation coefficients (ICCs) between manual and CNN results were determined. Results Fully automatic CNN segmentation was successful in 99.0% and 93.2% of images in TS1 and TS2, respectively. DSI, MAD and HDD showed good overlap and distance between manual and CNN segmentations in both test sets. This was reflected in the respective ICC values in TS1 and TS2 for the length (0.96 and 0.95), width (0.77 and 0.87) and area (0.96 and 0.91) of the UH, the length of the PRM (0.87 and 0.73) and MEP (0.95 and 0.97), which showed good to very good agreement. Conclusion Deep learning can be used to segment automatically and reliably the PRM and UH on 2D ultrasound images of the nulliparous pelvic floor in the plane of minimal hiatal dimensions. These segmentations can be used to measure reliably UH dimensions as well as PRM length and MEP. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.
- Published
- 2019
7. COMPARATIVE STUDY ON DIFFERENT CNN ARCHITECTURES DEVELOPED ON MICROSTRUCTURAL CLASSIFICATION IN AL-SI ALLOYS.
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KALKAN, M. F., ALADAG, M., KURZYDLOWSKI, K. J., YILMAZ, N. F., and YAVUZ, A.
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CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,SUPERVISED learning ,DEEP learning ,CLASSIFICATION - Abstract
Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don't always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. Beyond Vision: Potential Role of AI-enabled Ocular Scans in the Prediction of Aging and Systemic Disorders.
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Omar, Moez Osama, Abad Ali, Muhammad Jabran, Qabillie, Soliman Elias, Haji, Ahmed Ibrahim, Takriti, Mohammed Bilal, Atif, Ahmed Hesham, and Imran, Rangraze
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AGING ,OPHTHALMOLOGY ,ARTIFICIAL intelligence ,DEEP learning ,BIOFLUORESCENCE - Abstract
In all medical subfields, including ophthalmology, the development of artificial intelligence (AI), particularly cutting-edge deep learning frameworks, has sparked a quiet revolution. The eyes and the rest of the body are anatomically related because of the unique microvascular and neuronal structures they possess. Therefore, ocular image-based AI technology may be a helpful substitute or extra screening method for systemic disorders, particularly in areas with limited resources. This paper provides an overview of existing AI applications for the prediction of systemic diseases from multimodal ocular pictures, including retinal diseases, neurological diseases, anemia, chronic kidney disease, autoimmune diseases, sleep disorders, cardiovascular diseases, and various others. It also covers the process of aging and its predictive biomarkers obtained from AI-based retinal scans. Finally, we also go through these applications existing problems and potential future paths. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. A Model for EEG-Based Emotion Recognition: CNN-Bi-LSTM with Attention Mechanism.
- Author
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Huang, Zhentao, Ma, Yahong, Wang, Rongrong, Li, Weisu, and Dai, Yongsheng
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DEEP learning ,EMOTION recognition ,AFFECTIVE neuroscience ,NEUROSCIENCES ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,COMPUTER science ,FEATURE extraction - Abstract
Emotion analysis is the key technology in human–computer emotional interaction and has gradually become a research hotspot in the field of artificial intelligence. The key problems of emotion analysis based on EEG are feature extraction and classifier design. The existing methods of emotion analysis mainly use machine learning and rely on manually extracted features. As an end-to-end method, deep learning can automatically extract EEG features and classify them. However, most of the deep learning models of emotion recognition based on EEG still need manual screening and data pre-processing, and the accuracy and convenience are not high enough. Therefore, this paper proposes a CNN-Bi-LSTM-Attention model to automatically extract the features and classify emotions based on EEG signals. The original EEG data are used as input, a CNN and a Bi-LSTM network are used for feature extraction and fusion, and then the electrode channel weights are balanced through the attention mechanism layer. Finally, the EEG signals are classified to different kinds of emotions. An emotion classification experiment based on EEG is conducted on the SEED dataset to evaluate the performance of the proposed model. The experimental results show that the method proposed in this paper can effectively classify EEG emotions. The method was assessed on two distinctive classification tasks, one with three and one with four target classes. The average ten-fold cross-validation classification accuracy of this method is 99.55% and 99.79%, respectively, corresponding to three and four classification tasks, which is significantly better than the other methods. It can be concluded that our method is superior to the existing methods in emotion recognition, which can be widely used in many fields, including modern neuroscience, psychology, neural engineering, and computer science as well. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. Fault classification in the process industry using polygon generation and deep learning.
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Elhefnawy, Mohamed, Ragab, Ahmed, and Ouali, Mohamed-Salah
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INDUSTRY classification ,DEEP learning ,HAMILTONIAN graph theory ,POLYGONS ,PULP mills ,ARTIFICIAL intelligence ,FEATURE extraction - Abstract
This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate "end-to-end learning" in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor.
- Author
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Park, Joungmin, Shin, Jinyoung, Kim, Raehyeong, An, Seongmo, Lee, Sangho, Kim, Jinyeol, Oh, Jongwon, Jeong, Youngwoo, Kim, Soohee, Jeong, Yue Ri, and Lee, Seung Eun
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ARTIFICIAL intelligence ,STRAWBERRIES ,CONVOLUTIONAL neural networks ,K-nearest neighbor classification ,GATE array circuits - Abstract
Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. A Self-Supervised Workflow for Particle Picking in Cryo-EM
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Sean McSweeney, Donal M McSweeney, and Qun Liu
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Cryo-electron microscopy ,Computer science ,2d class averages ,computer.software_genre ,Biochemistry ,Convolutional neural network ,particle improvement ,convolutional neural network (cnn) ,Computational science ,03 medical and health sciences ,0302 clinical medicine ,Cutoff ,General Materials Science ,Instrumentation ,030304 developmental biology ,automation ,0303 health sciences ,Class (computer programming) ,Crystallography ,business.industry ,InformationSystems_INFORMATIONSYSTEMSAPPLICATIONS ,Deep learning ,Process (computing) ,deep learning ,Pattern recognition ,General Chemistry ,Condensed Matter Physics ,Research Papers ,Automation ,Class (biology) ,Workflow ,QD901-999 ,cryo-em ,particle picking ,Particle ,Artificial intelligence ,Data mining ,business ,computer ,030217 neurology & neurosurgery - Abstract
A self-supervised workflow uses a 2D class average to progressively train a convolutional neural network for automated particle picking in cryo-EM., High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.
- Published
- 2020
13. Enhancing the Swimmer Movement Techniques Using Cloud Computing and Artificial Intelligence
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Xurui, Liu and Guobao, Zhang
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- 2023
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14. Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis.
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Wang, Yang, Yang, Fuqian, Zhang, Jun, Wang, Huidong, Yue, Xianwen, and Liu, Shanshan
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DEEP learning ,ARTIFICIAL intelligence ,BREAST cancer ,DIAGNOSIS ,EARLY detection of cancer ,CONVOLUTIONAL neural networks - Abstract
In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in Chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer CT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (CDNN) through the convolution neural network (CNN). In this paper, the fuzzy C-means clustering algorithm (FCM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy C-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on CNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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15. An ML-Based Approach for Optimizing the Productivity and Efficiency of the Apparel Industry by Focusing on Trainee Employees.
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R. A. S. T., Rathnayake, D. N., Munmulla, Rathnayake, Samadhi, M. I. M., Peiris, Asini Bovindya, E. A., and Karunasena, Anuradha
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TECHNOLOGICAL innovations ,DEEP learning ,MACHINE learning ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,CLOTHING industry - Abstract
In the apparel industry, training is crucial because it brings skilled workers and promotes increased productivity. However, typical manual approaches frequently fail to accelerate the training process, resulting in unsatisfactory results properly. In this paper, the authors describe an innovative strategy to increase the productivity and efficiency of sewing machine operator training processes by using a machine learning-driven web-based application. The proposed application leverages the power of machine learning models to identify and solve crucial areas for improvement. It specifically detects wrong hand movements, incorrect trainee sitting postures, defects in sewed garments, and errors in dexterity tests during the training period of the sewing operators. Notably, the Graphical Neural Network (GNN) model detects erroneous hand movements with an astonishing 85% accuracy. The Convolutional Neural Network (CNN) model excels in detecting incorrect sitting postures, with an impressive 75% accuracy. Furthermore, the CNN model detects garment defects with an accuracy of 95%, while the CNN model detects test result errors in dexterity tests with an astounding 97% test accuracy. By using the proposed web tool for screening, the authors expect to see a significant increase in trainee productivity and efficiency. Lastly, the machine learning-driven web-based application is a great tool for optimizing the garment industry's training process. Future plans include increasing the application's functionality, introducing new features, and investigating its applicability across multiple sectors within garment manufacturing. By adopting this unique approach, the apparel sector can achieve significant gains in training outcomes, resulting in a more skilled and efficient workforce. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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16. Convolutional Neural Network Based Image Processing System.
- Author
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Hankil Kim, Jinyoung Kim, and Hoekyung Jung
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NEURAL circuitry ,IMAGE processing ,PATTERN perception ,DEEP learning ,ARTIFICIAL intelligence - Abstract
This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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17. Classification of crystal structure using a convolutional neural network
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Jaeyoung Jung, Satendra Pal Singh, Kee-Sun Sohn, Jiyong Chung, Woon Bae Park, Myoungho Pyo, Keemin Sohn, and Namsoo Shin
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Diffraction ,Materials science ,Computer Science::Neural and Evolutionary Computation ,computational modelling ,Crystal system ,inorganic materials ,02 engineering and technology ,Crystal structure ,convolutional neural network (CNN) ,artificial neural network (ANN) ,powder X-ray diffraction ,crystal system ,crystal structure prediction ,properties ofsolids ,010402 general chemistry ,01 natural sciences ,Biochemistry ,Convolutional neural network ,Position (vector) ,General Materials Science ,lcsh:Science ,business.industry ,Group (mathematics) ,Pattern recognition ,General Chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Research Papers ,0104 chemical sciences ,Crystal structure prediction ,lcsh:Q ,Artificial intelligence ,Symmetry (geometry) ,0210 nano-technology ,business ,properties of solids - Abstract
A deep-machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been employed for the classification of crystal system, extinction group and space group for given powder X-ray diffraction patterns of inorganic materials., A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.
- Published
- 2017
18. Application of Artificial Intelligence in Claim Management and Fire Surveying in the context of Bangladesh.
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Nahar, Nujhat, Naheen, Intisar Tahmid, and Hasan, Sayed Jobaer
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FIRE management ,ARTIFICIAL intelligence ,NATURAL language processing ,COMPUTER vision ,CONVOLUTIONAL neural networks - Abstract
With the advancement of technology, the whole insurance business is getting more and more automated day by day. In Bangladesh, the insurance industry is getting involved as technology as well. Even though the concept of Artificial intelligence (AI) is quite new in the insurance business, it is having a massive stride in other financial corporations. Some classes of AI like Natural Language Processing (NLP), Computer Vision (CV) are getting used in different financial organizations. Claim management and surveyance are two of the most important parts of the insurance system. In this paper, we explore the possibility of using AI in these two fields of insurance in the context of Bangladesh. After discussing the initial terminologies, we discuss the steps following which we can utilize AI in claim management and fire surveying. We also discuss the network architecture of AI models in the paper. And finally, we discuss our progress so far in implementing AI in the Bangladesh insurance business. [ABSTRACT FROM AUTHOR]
- Published
- 2020
19. Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study.
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Altabey, Wael A., Wu, Zhishen, Noori, Mohammad, and Fathnejat, Hamed
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STRUCTURAL health monitoring ,DEEP learning ,OPTICAL fiber detectors ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,FIBER Bragg gratings ,ALGORITHMS - Abstract
In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33 % accuracy (P%), 91.18 % regression rate (R%) and a 90.54 % F1-score (F%). [ABSTRACT FROM AUTHOR]
- Published
- 2023
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20. Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.
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Kairys, Arturas, Pauliukiene, Renata, Raudonis, Vidas, and Ceponis, Jonas
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DIABETIC foot ,FOOT ,COMPUTER vision ,DIABETES complications ,CONVOLUTIONAL neural networks ,FEATURE extraction ,ARTIFICIAL intelligence - Abstract
It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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21. A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG.
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Wenhan Liu, Qianxi Guo, Siyun Chen, Sheng Chang, Hao Wang, Jin He, and Qijun Huang
- Subjects
ARTIFICIAL intelligence ,FIELD programmable gate arrays ,CONVOLUTIONAL neural networks ,HEART beat ,ELECTROCARDIOGRAPHY - Abstract
In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fullymapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
22. Breast Cancer Detection in Thermography Using Convolutional Neural Networks (CNNs) with Deep Attention Mechanisms.
- Author
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Alshehri, Alia and AlSaeed, Duaa
- Subjects
CONVOLUTIONAL neural networks ,ARTIFICIAL neural networks ,COMPUTER-aided diagnosis ,BREAST ,THERMOGRAPHY ,EARLY detection of cancer ,MAMMOGRAMS ,BREAST cancer - Abstract
Featured Application: Medical diagnosis and computer-aided diagnosis systems. Breast cancer is one of the most common types of cancer among women. Accurate diagnosis at an early stage can reduce the mortality associated with this disease. Governments and health organizations stress the importance of early detection of breast cancer as it is related to an increase in the number of available treatment options and increased survival. Early detection gives patients the best chance of receiving effective treatment. Different types of images and imaging modalities are used in the detection and diagnosis of breast cancer. One of the imaging types is "infrared thermal" breast imaging, where a screening instrument is used to measure the temperature distribution of breast tissue. Although it has not been used often, compared to mammograms, it showed promising results when used for early detection. It also has many advantages as it is non-invasive, safe, painless, and inexpensive. The literature has indicated that the use of thermal images with deep neural networks improves the accuracy of early diagnosis of breast malformation. Therefore, in this paper, we aim to investigate to what extent convolutional neural networks (CNNs) with attention mechanisms (AMs) can provide satisfactory detection results in thermal breast cancer images. We present a model for breast cancer detection based on deep neural networks with AMs using thermal images from the Database for Research Mastology with Infrared Image (DMR-IR). The model will be evaluated in terms of accuracy, sensitivity and specificity, and will be compared against state-of-the-art breast cancer detection methods. The AMs with the CNN model achieved encouraging test accuracy rates of 99.46%, 99.37%, and 99.30% on the breast thermal dataset. The test accuracy of CNNs without AMs was 92.32%, whereas CNNs with AMs achieved an improvement in accuracy of 7%. Moreover, the proposed models outperformed previous models that were reviewed in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
23. An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array.
- Author
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Zhang, Chen, Wang, Xin'an, Yong, Shanshan, Zhang, Yining, Li, Qiuping, and Wang, Chenyang
- Subjects
CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,STATIC random access memory ,IMAGE segmentation ,ENERGY consumption - Abstract
Deep convolutional neural networks (CNNs) have shown strong abilities in the application of artificial intelligence. However, due to their extensive amount of computation, traditional processors have low energy efficiency when executing CNN algorithms, which is unacceptable for portable devices with limited hardware cost and battery capacity, so designing a CNN-specific processor is necessary. In this paper, we propose an energy-efficient CNN processor architecture for lightweight devices with a processing elements (PEs) array consisting of 384 PEs. Using the systolic array-based PE array, it realizes parallel operations between filter rows and between channels of output feature maps, supporting the acceleration of 3D convolution and fully connected computation with various parameters by configuring internal instruction registers. The computing strategy based on the proposed systolic dataflow achieves less hardware overhead compared with other strategies, and the reuse of image values and weight values, which effectively reduce the power of memory access. A memory system with a multi-level storage structure combined with register file (RF) and SRAM is used in the proposed CNN processor, which further reduces the energy overhead of computing. The proposed CNN processor architecture has been verified on a ZC706 FPGA platform using VGG-16 based on the proposed image segmentation method, the evaluation results indicate that the peak throughput achieves 115.2 GOP/s consuming 3.801 W at 150 MHz, energy efficiency and DSP efficiency reaches 30.32 GOP/s/W and 0.26 GOP/s/DSP, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
24. Interpretable Artificial Intelligence through Locality Guided Neural Networks.
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Tan, Randy, Gao, Lei, Khan, Naimul, and Guan, Ling
- Subjects
- *
DEEP learning , *ARTIFICIAL intelligence , *CONVOLUTIONAL neural networks , *SELF-organizing maps , *IMAGE processing - Abstract
In current deep learning architectures, each of the deeper layers in networks tends to contain hundreds of unorganized neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons using correlation as the criteria, humans can observe how clusters of neighbouring neurons interact with each other. Research in Explainable Artificial Intelligence (XAI) aims to all alleviate the black-box nature of current AI methods and make them understandable by humans. In this paper, we extend our previous algorithm for XAI in deep learning, called Locality Guided Neural Network (LGNN). LGNN preserves locality between neighbouring neurons within each layer of a deep network during training. Motivated by Self-Organizing Maps (SOMs), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. Our algorithm can be easily plugged into current state of the art Convolutional Neural Network (CNN) models to make the neighbouring neurons more correlated. A cluster of neighbouring neurons activating for a class makes the network both quantitatively and qualitatively more interpretable when visualized, as we show through our experiments. This paper focuses on image processing with CNNs, but can theoretically be applied to any type of deep learning architecture. In our experiments, we train VGG and WRN networks for image classification on CIFAR100 and Imagenette. Our experiments analyse different perceptible clusters of activations in response to different input classes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
25. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox.
- Author
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Jiang, Guoqian, He, Haibo, Yan, Jun, and Xie, Ping
- Subjects
ARTIFICIAL neural networks ,SIGNAL convolution ,MULTISCALE modeling ,ARTIFICIAL intelligence ,FAULT diagnosis ,DEEP learning ,WIND turbines ,GEARBOXES - Abstract
This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
26. Design and Analysis of a Neural Network Inference Engine Based on Adaptive Weight Compression.
- Author
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Ko, Jong Hwan, Kim, Duckhwan, Na, Taesik, and Mukhopadhyay, Saibal
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NEURAL circuitry ,NEURAL computers ,ARTIFICIAL intelligence ,MULTILAYER perceptrons ,PERCEPTRONS - Abstract
Neural networks generally require significant memory capacity/bandwidth to store/access a large number of synaptic weights. This paper presents design of an energy-efficient neural network inference engine based on adaptive weight compression using a JPEG image encoding algorithm. To maximize compression ratio with minimum accuracy loss, the quality factor of the JPEG encoder is adaptively controlled depending on the accuracy impact of each block. With 1% accuracy loss, the proposed approach achieves $63.4{\times }$ compression for multilayer perceptron (MLP) and $31.3 {\times }$ for LeNet-5 with the MNIST dataset, and $15.3 {\times }$ for AlexNet and $10.2 {\times }$ for ResNet-50 with ImageNet. The reduced memory requirement leads to higher throughput and lower energy for neural network inference ($3 {\times }$ effective memory bandwidth and $22 {\times }$ lower system energy for MLP). [ABSTRACT FROM AUTHOR]
- Published
- 2019
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- View/download PDF
27. E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning.
- Author
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Petroșanu, Dana-Mihaela, Pîrjan, Alexandru, Căruţaşu, George, Tăbușcă, Alexandru, Zirra, Daniela-Lenuța, and Perju-Mitran, Alexandra
- Subjects
SALES forecasting ,DEEP learning ,DIRECTED acyclic graphs ,ELECTRONIC commerce - Abstract
As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN's architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Cofopose: Conditional 2D Pose Estimation with Transformers.
- Author
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Aidoo, Evans, Wang, Xun, Liu, Zhenguang, Tenagyei, Edwin Kwadwo, Owusu-Agyemang, Kwabena, Kodjiku, Seth Larweh, Ejianya, Victor Nonso, and Aggrey, Esther Stacy E. B.
- Subjects
ARTIFICIAL vision ,COMPUTER vision ,ARTIFICIAL intelligence - Abstract
Human pose estimation has long been a fundamental problem in computer vision and artificial intelligence. Prominent among the 2D human pose estimation (HPE) methods are the regression-based approaches, which have been proven to achieve excellent results. However, the ground-truth labels are usually inherently ambiguous in challenging cases such as motion blur, occlusions, and truncation, leading to poor performance measurement and lower levels of accuracy. In this paper, we propose Cofopose, which is a two-stage approach consisting of a person and keypoint detection transformers for 2D human pose estimation. Cofopose is composed of conditional cross-attention, a conditional DEtection TRansformer (conditional DETR), and an encoder-decoder in the transformer framework; this allows it to achieve person and keypoint detection. In a significant departure from other approaches, we use conditional cross-attention and fine-tune conditional DETR for our person detection, and encoder-decoders in the transformers for our keypoint detection. Cofopose was extensively evaluated using two benchmark datasets, MS COCO and MPII, achieving an improved performance with significant margins over the existing state-of-the-art frameworks. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
29. A review on AI-based medical image computing in head and neck surgery.
- Author
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Xu, Jiangchang, Zeng, Bolun, Egger, Jan, Wang, Chunliang, Smedby, Ă–rjan, Jiang, Xiaoyi, and Chen, Xiaojun
- Subjects
COMPUTER-assisted image analysis (Medicine) ,IMAGE registration ,ARTIFICIAL intelligence ,DIAGNOSTIC imaging ,DEEP learning ,PHYSICIANS ,MEDICAL physics ,NECK - Abstract
Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
30. Credit card fraud detection using a deep learning multistage model.
- Author
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Zioviris, Georgios, Kolomvatsos, Kostas, and Stamoulis, George
- Subjects
CREDIT card fraud ,ARTIFICIAL neural networks ,DEEP learning ,FRAUD investigation ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence - Abstract
The banking sector is on the eve of a serious transformation and the thrust behind it is artificial intelligence (AI). Novel AI applications have been already proposed to deal with challenges in the areas of credit scoring, risk assessment, client experience and portfolio management. One of the most critical challenges in the aforementioned sector is fraud detection upon streams of transactions. Recently, deep learning models have been introduced to deal with the specific problem in terms of detecting and forecasting possible fraudulent events. The aim is to estimate the unknown distribution of normal/fraudulent transactions and then to identify deviations that may indicate a potential fraud. In this paper, we elaborate on a novel multistage deep learning model that targets to efficiently manage the incoming streams of transactions and detect the fraudulent ones. We propose the use of two autoencoders to perform feature selection and learn the latent data space representation based on a nonlinear optimization model. On the delivered significant features, we subsequently apply a deep convolutional neural network to detect frauds, thus combining two different processing blocks. The adopted combination has the goal of detecting frauds over the exposed latent data representation and not over the initial data. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
31. AI-Powered COVID-19 Health Management based on Radiological Imaging: Bio-Ethics Perspective.
- Author
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Mishra, Nirbhay Kumar, Agrawal, Jhama, and Baba, Misha Hamid
- Subjects
- *
ARTIFICIAL intelligence , *REVERSE transcriptase polymerase chain reaction , *CORONAVIRUS diseases , *MEDICAL personnel , *MEDICAL technology , *COVID-19 - Abstract
Artificial Intelligence (AI) technologies across the health care spectrum have evolved as the biggest instrument for managing catastrophically spread (COVID-19) pandemic. The outbreak of novel coronavirus has posed numerous arduous challenges such as well-being, Financial, ecological, social, and ethical challenges to the entire world populace and disrupted global economy. This paper aims to thoroughly explore and analyze the role of AI coupled with radiological imaging as one important tool for covid-19 screening, prediction, forecasting, and contact tracing using chest X-ray images and Computer Tomography scan over the conventional RT-PCR (Reverse Transcription Polymerase Chain Reaction) technique, which is the conventional technique used in the prognosis of the novel corona. This paper further accentuates Al's role in the fight to curb this pandemic and suggestsan innovative solution for COVID-19 health management by minimizing human contact and protecting front-line healthcare personnel, administrative staff, and the public at large. A systematic approach for literature review is carried out on the reputed database. This research makes a seminal contribution by compiling the most upto-date state-of-the-art scientific approaches to the COVID-19 assessment., using real-world datasets and the ethical perspective of AI-powered solutions for COVID-19 health management. The findings are reviewed and concluded in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2022
32. Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition.
- Author
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Beaulieu, Alexandre, Thullier, Florentin, Bouchard, Kévin, Maître, Julien, and Gaboury, Sébastien
- Subjects
DEEP learning ,HUMAN activity recognition ,ARTIFICIAL intelligence ,ULTRA-wideband radar ,AMBIENT intelligence ,SMART homes ,MULTISENSOR data fusion - Abstract
The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
33. Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis.
- Author
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Reddy, Kallam Anji, Regula, Thirupathi, Sharmila, Karramareddy, Srinivas, P. V. V. S., and Rahman, Syed Ziaur
- Subjects
EMOTION recognition ,CONVOLUTIONAL neural networks ,SPECKLE interference ,FACIAL expression & emotions (Psychology) ,ARTIFICIAL intelligence - Abstract
The human face serves as a potent biological medium for expressing emotions, and the capability to interpret these expressions has been fundamental to human interaction since time immemorial. Consequently, the extraction of emotions from facial expressions in images, using machine learning, presents an intriguing yet challenging avenue. Over the past few years, advancements in artificial intelligence have significantly contributed to the field, replicating aspects of human intelligence. This paper proposes a Facial Emotion Recognition Convolutional Neural Network (FERCNN) model, addressing the limitations in accurately processing raw input images, as evidenced in the literature. A notable improvement in performance is observed when the input image is injected with noise prior to training and validation. Gaussian, Poisson, Speckle, and Salt & Pepper noise types are utilized in this noise injection process. The proposed model exhibits superior results compared to well-established CNN architectures, including VGG16, VGG19, Xception, and Resnet50. Not only does the proposed model demonstrate greater performance, but it also reduces training costs compared to models trained without noise injection at the input level. The FER2013 and JAFFE datasets, comprising seven different emotions (happy, angry, neutral, fear, disgust, sad, and surprise) and totaling 39,387 images, are used for training and testing. All experimental procedures are conducted via the Kaggle cloud infrastructure. When Gaussian, Poisson, and Speckle noise are introduced at the input level, the suggested CNN model yields evaluation accuracies of 92.17%, 95.07%, and 92.41%, respectively. In contrast, the highest accuracies achieved by existing models such as VGG16, VGG19, and Resnet 50 are 45.97%, 63.97%, and 54.52%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Strategy for Reliable, Efficient and Secure IoT Using Artificial Intelligence.
- Author
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Alkali, Yusuf, Routray, Indira, and Whig, Pawan
- Subjects
ARTIFICIAL intelligence ,COMPUTER network security ,INTERNET of things ,CLOUD computing ,CYBERTERRORISM ,MACHINE learning - Abstract
The Internet of Things (IoT), one of the leading cutting-edge innovations, has become an economically attractive field of focus for the scientific community. It requires several system interconnections and device interactions with humans. In order to manage its data exchange and analysis, IoT needs a cloud computing environment; Artificial Intelligence (AI) is needed at the same time via the Internet and cloud-based network of networks. These interconnected IoT systems can interact and share information with each other using their respective identifiers and embedded sensors on each device. We live in the age of big data, and it has become very important to easily and reliably interpret the captured big data. However, while AI is currently playing a greater role in strengthening conventional safety, there are significant challenges to cloud security and IoT computer networking. There are many security concerns that cloud be a danger to the community. In comparison, several of the IoT systems installed on a public network that is wirelessly accessible are now under persistent cyber attack. The paper suggests a hybrid identification paradigm as a response strategy that utilizes Machine Learning (ML) and AI to mitigate and combat IoT cyberattacks both at the host and network levels in cloud computing environments. [ABSTRACT FROM AUTHOR]
- Published
- 2022
35. Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review.
- Author
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Hoque, Mohammed Enamul and Kipli, Kuryati
- Subjects
DEEP learning ,RETINAL imaging ,ARTIFICIAL intelligence ,FEATURE extraction ,IMAGE segmentation ,THERAPEUTICS - Abstract
Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning, and retinal database. For the associated publications the reference lists of selected articles were further investigated. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios.
- Author
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Wang, Yu, Liu, Miao, Yang, Jie, and Gui, Guan
- Subjects
COGNITIVE radio ,ARTIFICIAL intelligence ,SIGNAL processing ,NEURAL circuitry ,MACHINE learning - Abstract
Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
37. Radar Automatic Target Recognition Based on Real-Life HRRP of Ship Target by Using Convolutional Neural Network.
- Author
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TSUNG-PIN CHEN, CHIH-LUNG LIN, KUO-CHIN FAN, WAN-YU LIN, and CHIAO-WEN KAO
- Subjects
AUTOMATIC target recognition ,RADAR targets ,CONVOLUTIONAL neural networks ,DEEP learning ,ARTIFICIAL intelligence - Abstract
High-resolution range profile (HRRP) is one of the most important approaches for radar automatic target recognition (RATR), which can project the target echoes from the scattering center of a ship target onto the radar line of sight (RLOS). This paper proposes an approach to use convolutional neural networks (CNNs) to recognize HRRP ship targets and a two-dimensional HRRP data format as the input of the CNN network. Compared with traditional pattern recognition approaches of handcrafted features based on researchers' prior knowledge and experience, the target recognition approach with deep neural network helps to avoid excessive use of artificially designed rules to extract features, and deep learning can automatically get the deep description features of the target. The approach presented in this paper has three main advantages: (1) Experiments conducted on the ship's HRRP dataset collected from the actual coastline are more realistic than most other papers using simulated datasets; (2) Proposed two-dimensional binary-map HRRP data format has good recognition performance, so it can be known that proper data preprocessing can improve recognition accuracy; (3) It can be seen from the experimental results that the CNN-based method proves that CNN can automatically learn the discriminative deep features of HRRP. It is feasible to use CNN to radar automatic target recognition based on real-life radar HRRP of ship targets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. Weakly Supervised Object Detection via Object-Specific Pixel Gradient.
- Author
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Shen, Yunhang, Ji, Rongrong, Wang, Changhu, Li, Xi, and Li, Xuelong
- Subjects
ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,NEURAL circuitry - Abstract
Most existing object detection algorithms are trained based upon a set of fully annotated object regions or bounding boxes, which are typically labor-intensive. On the contrary, nowadays there is a significant amount of image-level annotations cheaply available on the Internet. It is hence a natural thought to explore such “weak” supervision to benefit the training of object detectors. In this paper, we propose a novel scheme to perform weakly supervised object localization, termed object-specific pixel gradient (OPG). The OPG is trained by using image-level annotations alone, which performs in an iterative manner to localize potential objects in a given image robustly and efficiently. In particular, we first extract an OPG map to reveal the contributions of individual pixels to a given object category, upon which an iterative mining scheme is further introduced to extract instances or components of this object. Moreover, a novel average and max pooling layer is introduced to improve the localization accuracy. In the task of weakly supervised object localization, the OPG achieves a state-of-the-art 44.5% top-5 error on ILSVRC 2013, which outperforms competing methods, including Oquabet al.and region-based convolutional neural networks on the Pascal VOC 2012, with gains of 2.6% and 2.3%, respectively. In the task of object detection, OPG achieves a comparable performance of 27.0% mean average precision on Pascal VOC 2007. In all experiments, the OPG only adopts the off-the-shelf pretrained CNN model, without using any object proposals. Therefore, it also significantly improves the detection speed, i.e., achieving three times faster compared with the state-of-the-art method. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
39. Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images.
- Author
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Alaiad, Ahmad, Migdady, Aya, Al-Khatib, Ra’ed M., Alzoubi, Omar, Zitar, Raed Abu, and Abualigah, Laith
- Subjects
DEEP learning ,CONVOLUTIONAL neural networks ,DIAGNOSIS ,COMPUTER-assisted image analysis (Medicine) ,ARTIFICIAL intelligence ,DIAGNOSTIC imaging - Abstract
Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods.
- Author
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Abir, Wahidul Hasan, Khanam, Faria Rahman, Alam, Kazi Nabiul, Hadjouni, Myriam, Elmannai, Hela, Bourouis, Sami, Dey, Rajesh, and Khan, Mohammad Monirujjaman
- Subjects
DEEP learning ,ARTIFICIAL intelligence ,MACHINE learning ,CONVOLUTIONAL neural networks ,DEEPFAKES ,TRUST - Abstract
Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human eye. Therefore, automated solutions employed by DL can be an efficient approach for detecting deepfake. Though the “black-box” nature of the DL system allows for robust predictions, they cannot be completely trustworthy. Explainability is the first step toward achieving transparency, but the existing incapacity of DL to explain its own decisions to human users limits the efficacy of these systems. Though Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of these systems. This work proposes to provide a comprehensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explanations (LIME) to assure its validity and reliability. This study identifies real and deepfake images using different Convolutional Neural Network (CNN) models to get the best accuracy. It also explains which part of the image caused the model to make a specific classification using the LIME algorithm. To apply the CNN model, the dataset is taken from Kaggle, which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in size. For experimental results, Jupyter notebook, TensorFlow, NumPy, and Pandas were used as software, InceptionResnetV2, DenseNet201, InceptionV3, and ResNet152V2 were used as CNN models. All these models’ performances were good enough, such as InceptionV3 gained 99.68% accuracy, ResNet152V2 got an accuracy of 99.19%, and DenseNet201 performed with 99.81% accuracy. However, InceptionResNetV2 achieved the highest accuracy of 99.87%, which was verified later with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and dependability demonstrate its preference for detecting deepfake images effectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events
- Author
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Asfand-e-yar, Muhammad, Hashir, Qadeer, Shah, Asghar Ali, Malik, Hafiz Abid Mahmood, Alourani, Abdullah, and Khalil, Waqar
- Published
- 2024
- Full Text
- View/download PDF
42. CLASSIFYING SKIN MOLES USING CONVOLUTIONAL NEURAL NETWORKS.
- Author
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Stratulat-Diaconu, Adriana and Cocu, Adina
- Subjects
CONVOLUTIONAL neural networks ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence - Abstract
The purpose of the paper was to develop an application that is capable to upload a picture and analyze it in order to determine melanoma lesions using artificial intelligence techniques. The proposed application is designed to use a previously trained convolutional neural network to recognize melanoma. For training, the examples from two known benchmarks were used and several attempts were made to find the best model driven by the neural network. The predictability rate is 0.95. The average time for obtaining the respond is 7 seconds. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
43. Speech recognition in English cultural promotion via recurrent neural network.
- Author
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Wang, Jian
- Subjects
ARTIFICIAL neural networks ,SPEECH perception ,RECURRENT neural networks ,ARTIFICIAL intelligence ,UNIVERSAL language - Abstract
With the rapid development of artificial intelligence, English is the most widely used language in the world, and English speech recognition has become one of the hot spots in the field of artificial intelligence. In order to promote English culture accurately, this paper uses recurrent neural network and convolutional neural network to recognize the tone and tone color of English comprehensively and studies the optimal number of layers and neuron nodes when the recognition accuracy is highest. When the number of layers is 7 and the number of nodes is 256, the recognition accuracy is the highest, reaching 98%. It provides convenience for English teaching and further promotes the spread of English culture. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
44. Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM.
- Author
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Ullah, Amin, Muhammad, Khan, Del Ser, Javier, Baik, Sung Wook, and de Albuquerque, Victor Hugo C.
- Subjects
VIDEO surveillance ,OPTICAL flow ,ARTIFICIAL neural networks ,HUMAN activity recognition ,ELECTRONIC surveillance ,COMPUTER vision ,COMPUTER engineering - Abstract
Nowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments 1 https://github.com/Aminullah6264/Activity%5fRec%5fML-LSTM. are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
45. A Novel Instruction Driven 1-D CNN Processor for ECG Classification.
- Author
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Deng, Jiawen, Yang, Jie, Wang, Xin'an, and Zhang, Xing
- Subjects
CONVOLUTIONAL neural networks ,ELECTROCARDIOGRAPHY ,COMPLEMENTARY metal oxide semiconductors ,CLASSIFICATION ,ARTIFICIAL intelligence ,MEDICAL examinations of athletes - Abstract
Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm
2 with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
46. Comparative Study on Different CNN Architectures Developed on Microstructural Classification in Al-Si Alloys
- Author
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M.F. Kalkan, M. Aladag, K.J. Kurzydlowski, N.F. Yilmaz, and A. Yavuz
- Subjects
artificial intelligence ,microstructural characterization ,al-si alloy ,convolutional neural network (cnn) ,material classification ,Mining engineering. Metallurgy ,TN1-997 ,Materials of engineering and construction. Mechanics of materials ,TA401-492 - Abstract
Recent advances in artificial intelligence have opened up new avenues for microstructure characterization, notably in metallic materials. Physical and mechanical properties generally depend on the microstructure of the metallic material. On the other hand, microstructural characterization takes time and calls for specific techniques that don’t always lead to conclusive results quickly. To address this issue, this research focuses on the application of artificial intelligence approaches to microstructural categorization. We demonstrate the advantages of the AI approach using an example of Al-Si alloy, a material that is widely employed in a variety of industries. To specify a suitable convolutional neural network (CNN) approach for the microstructural classification of the Al-Si alloy, CNN models were trained and compared using DenseNet201, Inception v3, InceptionResNetV2, ResNet152V2, VGG16, and Xception architectures. Resulting from the comparison, it was determined that the developed supervised transfer learning model can execute the microstructural classification of Al-Si alloy microstructural images. This paper is an attempt to advance methods of microstructure recognition/classification/characterization by using Deep Learning approaches. The significance of the established model is demonstrated and its accordance with the literature data. Also, necessity is shown of developing material models and optimization through systematic microstructural investigation, production conditions, and material attributes.
- Published
- 2024
- Full Text
- View/download PDF
47. Efficient Prediction of Bridge Conditions Using Modified Convolutional Neural Network
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Kumar, Amit, Singla, Sandeep, Kumar, Ajay, Bansal, Aarti, and Kaur, Avneet
- Published
- 2022
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48. Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images
- Author
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Uemura, Keisuke, Otake, Yoshito, Takao, Masaki, Makino, Hiroki, Soufi, Mazen, Iwasa, Makoto, Sugano, Nobuhiko, and Sato, Yoshinobu
- Published
- 2022
- Full Text
- View/download PDF
49. HIERARCHICAL STRUCTURE BASED CONVOLUTIONAL NEURAL NETWORK FOR FACE RECOGNITION.
- Author
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KHALAJZADEH, HURIEH, MANSOURI, MOHAMMAD, and TESHNEHLAB, MOHAMMAD
- Subjects
ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,HUMAN facial recognition software ,INFORMATION processing ,DATABASES - Abstract
In this paper, a hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing. The weight sharing ability of convolutional neural networks (CNNs) is considered as a level of hierarchy in these networks. Weight sharing reduces the number of free parameters and improves the generalization ability. In the proposed structure, a small CNN which is used for feature extractor is shared between the whole input image pixels. A scalable architecture for implementing extensive CNNs is resulted using a smaller and modularized trainable network to solve a large and complicated task. The proposed structure causes less training time, fewer numbers of parameters and higher test data accuracy. The recognition accuracy for recognizing unseen data shows improvement in generalization. Also presented are application examples for face recognition. The comprehensive experiments completed on ORL, Yale and JAFFE face databases show improved classification rates and reduced training time and network parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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- View/download PDF
50. Design of an Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation.
- Author
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Jong Hwan Ko, Burhan Mudassar, Taesik Na, and Saibal Mukhopadhyay
- Subjects
ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,QUANTUM computing ,ENERGY consumption ,CONSUMPTION (Economics) - Abstract
Convolutional neural networks (CNNs) require high computation and memory demand for training. This paper presents the design of a frequency-domain accelerator for energy-efficient CNN training. With Fourier representations of parameters, we replace convolutions with simpler pointwise multiplications. To eliminate the Fourier transforms at every layer, we train the network entirely in the frequency domain using approximate frequencydomain nonlinear operations. We further reduce computation and memory requirements using sinc interpolation and Hermitian symmetry. The accelerator is designed and synthesized in 28nm CMOS, as well as prototyped in an FPGA. The simulation results show that the proposed accelerator significantly reduces training time and energy for a target recognition accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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