1,737 results
Search Results
2. Enhancing Thai Food Classification: A CNN-Based Approach with Transfer Learning.
- Author
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Matarat, Korakot
- Subjects
THAI cooking ,CONVOLUTIONAL neural networks ,IMAGE recognition (Computer vision) ,CLASSIFICATION ,DEEP learning - Abstract
In this research paper, we delve into the classification of Thai cuisine images. Despite Thailand's renowned reputation for its multicultural culinary landscape, there is a noticeable gap in dedicated studies on Thai food classification. This paper seeks to fill that void by applying deep learning methodologies, specifically Convolutional Neural Networks (CNNs), to the identification of Thai cuisine. Thai cuisine, shaped by regional and intra-regional variations, serves as a powerful cultural representation for the nation. The study employs image recognition through CNN and integrates transfer learning to enhance classification performance. The collaborative learning process between CNN and transfer learning contributes to achieving a noteworthy accuracy rate of 84%. While previous research has often overlooked the specificity of Thai cuisine, our aim is to shed light on the potential of deep classification networks, offering an engaging illustration for both researchers and food enthusiasts alike contributing to the broader field of food image classification. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
3. A COMPARATIVE EXPLORATION OF ACTIVATION FUNCTIONS FOR IMAGE CLASSIFICATION IN CONVOLUTIONAL NEURAL NETWORKS.
- Author
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MAKHDOOM, FAIZA and RAHMAN, JAMSHAID UL
- Subjects
ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,MACHINE learning ,DIGITAL image processing ,COMPUTER vision - Abstract
Activation functions play a crucial role in enabling neural networks to carry out tasks with increased flexibility by introducing non-linearity. The selection of appropriate activation functions becomes even more crucial, especially in the context of deeper networks where the objective is to learn more intricate patterns. Among various deep learning tools, Convolutional Neural Networks (CNNs) stand out for their exceptional ability to learn complex visual patterns. In practice, ReLu is commonly employed in convolutional layers of CNNs, yet other activation functions like Swish can demonstrate superior training performance while maintaining good testing accuracy on different datasets. This paper presents an optimally refined strategy for deep learning-based image classification tasks by incorporating CNNs with advanced activation functions and an adjustable setting of layers. A thorough analysis has been conducted to support the effectiveness of various activation functions when coupled with the favorable softmax loss, rendering them suitable for ensuring a stable training process. The results obtained on the CIFAR-10 dataset demonstrate the favorability and stability of the adopted strategy throughout the training process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. DEEP LEARNING-BASED IRAQI BANKNOTES CLASSIFICATION SYSTEM FOR BLIND PEOPLE.
- Author
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Awad, Sohaib Rajab, Sharef, Baraa T., Salih, Abdulkreem M., and Malallah, Fahad Layth
- Subjects
COMPUTER vision ,CONVOLUTIONAL neural networks ,PEOPLE with visual disabilities ,MACHINE learning ,DEEP learning ,HUMAN-computer interaction - Abstract
Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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5. An ensemble deep learning model for automatic classification of cotton leaves diseases.
- Author
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Kukadiya, Hirenkumar, Arora, Nidhi, Meva, Divyakant, and Srivastava, Shilpa
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DEEP learning ,CONVOLUTIONAL neural networks ,COTTON ,AUTOMATIC classification ,PLANT diseases ,RATE setting - Abstract
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Forecasting crude oil prices with alternative data and a deep learning approach
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Zhang, Xiaotao, Xia, Zihui, He, Feng, and Hao, Jing
- Published
- 2024
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7. A survey on computer vision approaches for automated classification of skin diseases
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Gupta, Pragya, Nirmal, Jagannath, and Mehendale, Ninad
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- 2024
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8. A Comprehensive Review on Deep Learning Algorithms for Wind Power Prediction.
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Sharma, Geetika, Lal, Madan, and Singh Attwal, Kanwal Preet
- Subjects
WIND power ,MACHINE learning ,DEEP learning ,RENEWABLE energy sources ,LONG-term memory ,CONVOLUTIONAL neural networks ,RECURRENT neural networks - Abstract
In recent years, various energy crisis and environmental considerations have prompted the use of renewable energy resources. Renewable energy resources like solar, wind, hydro, biomass, etc. have been a continuous source of clean energy. Wind energy is one of the renewable energy resources that has been widely used all over the world. The wind power is mainly dependent on wind speed which is a random variable and its unpredictable behavior creates various challenges for wind farm operators like energy dispatching and system scheduling. Hence, predicting wind power energy becomes crucial. This has led to the development of various forecasting models in the recent decades. The most commonly used deep learning algorithms for wind power prediction are- RNN (Recurrent Neural Network), LSTM (Long Short- Term Memory) and CNN (Convolutional Neural Network). This paper presents the working of these algorithms and provides a timeline review of the research papers that used these algorithms for wind power prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2022
9. Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases
- Author
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Panjeta, Manisha, Reddy, Aryan, Shah, Rushabh, and Shah, Jash
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- 2024
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10. A Comparative Study on Facial Emotion Recognition using Deep Neural Networks.
- Author
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S., Asha and Sundarrajan, R.
- Subjects
EMOTION recognition ,ARTIFICIAL neural networks ,FACIAL expression & emotions (Psychology) ,HUMAN facial recognition software ,EMOTIONS ,FEATURE extraction ,DEEP learning ,FACIAL muscles - Abstract
Emotions are strongly associated with individuals’ mood and personality. In the field of Human Computer Interaction, human face plays a very vital role. According to studies made by researchers’ majority of the information conveyed through facial expressions than verbal communication. In day-to-day life, human expresses different types of feelings such as Happiness, Anger, Sadness, Fear, Disgust and Surprise which is considered as “Universal Emotions”. It has always been difficult for computers to recognize human emotions. Thus, a substantial effort was made by the researchers to build the Facial Emotion Recognition system and which was considered as the best tool for recognizing emotions through facial expressions. In this paper, a detailed study on different methods that can be used in facial emotion recognition is done. For this study, the literature is collected from various reputable research published. This survey paper is based on the current approaches to face detection and feature extraction techniques for FER and also presented the real-time applications [ABSTRACT FROM AUTHOR]
- Published
- 2024
11. Enhancing cyberbullying detection: a comparative study of ensemble CNN–SVM and BERT models.
- Author
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Saini, Hiteshi, Mehra, Himashri, Rani, Ritu, Jaiswal, Garima, Sharma, Arun, and Dev, Amita
- Abstract
Technological improvements have increased the number of people who use online social networking sites, resulting in an increase in cyberbullying. Bullies can attack victims through a large network of online social networking platforms. Cyberbullying is an umbrella term encompassing a wide range of online abuse, including but not limited to harassment, doxing, and reputation attacks. These attacks frequently leave the victim(s) with persistent mental scars, leading to desperate measures such as depression, self-harm, and suicidal thoughts. Given the effects of cyberbullying, there is an urgent need to prosecute and prevent such crimes. This paper gives a comprehensive review as well the empirical analysis of the machine learning, ensemble based and transformer-based models for the cyberbullying detection. This paper proposes two architectures to efficiently detect cyberbullying pattern. The proposed ensemble model makes use of CNN to extract the relevant features and the classification is performed by the SVM. Another proposed architecture utilizes the pre-trained model BERT to detect cyberbullying behavior on online platforms. Both the proposed models were tested on two separate datasets and achieved maximum accuracy of 96.88 and 97.34% for ensemble and BERT models, respectively. This paper provides a thorough examination of the various methodologies used for cyberbullying detection and conducts an empirical and comparative analysis of the presented models with traditional and current algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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12. Investigating the effectiveness of deep learning approaches for deep fake detection.
- Author
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Berrahal, Mohammed, Boukabous, Mohammed, Yandouzi, Mimoun, Grari, Mounir, and Idrissi, Idriss
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DEEP learning ,MACHINE learning ,CONVOLUTIONAL neural networks ,GENERATIVE adversarial networks ,IMAGE processing ,SELF-deception - Abstract
As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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13. Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review.
- Author
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Islam, Mahmudul, Rashel, Masud Rana, Ahmed, Md Tofael, Islam, A. K. M. Kamrul, and Tlemçani, Mouhaydine
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ARTIFICIAL intelligence ,MACHINE learning ,DEEP learning ,CONVOLUTIONAL neural networks ,PROCESS capability ,PHOTOVOLTAIC power systems - Abstract
Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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14. A survey on recent trends in deep learning for nucleus segmentation from histopathology images
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Basu, Anusua, Senapati, Pradip, Deb, Mainak, Rai, Rebika, and Dhal, Krishna Gopal
- Published
- 2024
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15. Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text.
- Author
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Bogdanchikov, Andrey, Ayazbayev, Dauren, and Varlamis, Iraklis
- Subjects
ARTIFICIAL neural networks ,DEEP learning ,IMAGE fusion ,NATURAL language processing ,MACHINE learning ,CORPORA ,KNOWLEDGE graphs - Abstract
The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models' training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents' classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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16. A Review on Machine Learning and Deep Learning Based Systems for the Diagnosis of Brain Cancer
- Author
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Saha, Prottoy, Das, Shanta Kumar, and Das, Rudra
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- 2024
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17. Transfer learning evaluation based on optimal convolution neural networks architecture for bearing fault diagnosis applications.
- Author
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Alabsi, Mohammed, Pearlstein, Larry, Nalluri, Nithya, Franco-Garcia, Michael, and Leong, Zachary
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CONVOLUTIONAL neural networks ,FAULT diagnosis ,MACHINE learning ,ADDITIVE white Gaussian noise ,DEEP learning ,RANDOM noise theory - Abstract
Intelligent fault diagnosis utilizing deep learning algorithms is currently a topic of great interest. When developing a new Convolutional Neural Network (CNN) architecture to address machine diagnosis problem, it is common to use a deep model, with many layers, many feature maps, and large kernels. These models are capable of learning complex relationships and can potentially achieve superior performance on test data. However, not only does a large network potentially impose undue computational complexity for training and eventual deployment, it may also lead to more brittleness—where data outside of the curated dataset used in CNN training and evaluation is poorly handled. Accordingly, this paper will investigate a methodical approach for identifying a quasi-optimal CNN architecture to maximize robustness when a model is trained under one set of operating conditions, and deployed under a different set of conditions. Optuna software will be used to optimize a baseline CNN model for robustness to different rotational speeds and bearing Model #'s. To further improve the network generalization capabilities, this paper proposes the addition of white Gaussian noise to the raw vibration training data. Results indicate that the number of trainable weights and associated multiplications in the optimized model were reduced by almost 95% without jeopardizing the network classification accuracy. Additionally, moderate Additive White Gaussian Noise (AWGN) improved the model adaptation capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
- Author
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Mohammed A. Fadhel, Muthana Al-Amidie, Ye Duan, Jinglan Zhang, Omran Al-Shamma, Amjad J. Humaidi, Laith Farhan, Ayad Q. Al-Dujaili, José Santamaría, and Laith Alzubaidi
- Subjects
Information Systems and Management ,lcsh:Computer engineering. Computer hardware ,Computer Networks and Communications ,Computer science ,Image classification ,Survey Paper ,GPU ,lcsh:TK7885-7895 ,02 engineering and technology ,Machine learning ,computer.software_genre ,Convolutional neural network ,Field (computer science) ,lcsh:QA75.5-76.95 ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Medical image analysis ,Deep learning applications ,0202 electrical engineering, electronic engineering, information engineering ,FPGA ,Point (typography) ,lcsh:T58.5-58.64 ,business.industry ,lcsh:Information technology ,Deep learning ,Supervised learning ,Robotics ,Transfer learning ,Hardware and Architecture ,Convolution neural network (CNN) ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Deep neural network architectures ,Artificial intelligence ,lcsh:Electronic computers. Computer science ,Transfer of learning ,business ,computer ,Information Systems - Abstract
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
- Published
- 2021
19. Application of machine learning methods in fault detection and classification of power transmission lines: a survey
- Author
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Shakiba, Fatemeh Mohammadi, Azizi, S. Mohsen, Zhou, Mengchu, and Abusorrah, Abdullah
- Published
- 2023
- Full Text
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20. Using Traffic Sensors in Smart Cities to Enhance a Spatio-Temporal Deep Learning Model for COVID-19 Forecasting.
- Author
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Muñoz-Organero, Mario
- Subjects
VEHICLE detectors ,INTELLIGENT sensors ,SMART cities ,DEEP learning ,COVID-19 pandemic ,COMPUTER network traffic ,MACHINE learning - Abstract
Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained and validated with real data from the city of Madrid in Spain for 84 weeks, combining information from 4372 traffic measuring points and 143 COVID-19 PCR test centers. The results are compared with a baseline model designed for the extraction of spatio-temporal patterns from COVID-19-only sequences of images, showing that using traffic information enhances the results when forecasting a new wave of infections (MSE values are reduced by a 70% factor). The information that traffic data has on the spread of the COVID-19 virus is also analyzed, showing that traffic data alone is not sufficient for accurate COVID-19 forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. A Deep Learning Algorithm for Evaluating the Quality of English Teaching.
- Author
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Li, Nan
- Subjects
MACHINE learning ,DEEP learning ,CONVOLUTIONAL neural networks ,EFFECTIVE teaching ,ANALYTIC hierarchy process ,SUPPORT vector machines - Abstract
Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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22. EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network.
- Author
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Tiwari, Smita, Goel, Shivani, and Bhardwaj, Arpit
- Subjects
CONVOLUTIONAL neural networks ,SIGNAL classification ,ELECTROENCEPHALOGRAPHY ,MACHINE learning ,HILBERT transform ,DEEP learning ,BRAIN-computer interfaces ,WAKEFULNESS - Abstract
The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
23. Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing
- Author
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Krysko, N. V., Skrynnikov, S. V., Shchipakov, N. A., Kozlov, D. M., and Kusyy, A. G.
- Published
- 2023
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24. Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network
- Author
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Sourav Das and Anup Kumar Kolya
- Subjects
Text corpus ,Predictive analysis ,Phrase ,Computer science ,Cognitive Neuroscience ,Twitter ,Stability (learning theory) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Convolutional neural network ,Sentiment analysis ,Mathematics (miscellaneous) ,Deep convolutional network ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Artificial neural network ,business.industry ,Deep learning ,020206 networking & telecommunications ,Coronavirus ,Test case ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Covid-19 ,computer ,Research Paper - Abstract
Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.
- Published
- 2021
25. Active Sonar Image Classification Using Deep Convolutional Neural Network Evolved by Robust Comprehensive Grey Wolf Optimizer.
- Author
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Najibzadeh, Maryam, Mahmoodzadeh, Azar, and Khishe, Mohammad
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CONVOLUTIONAL neural networks ,IMAGE recognition (Computer vision) ,SONAR imaging ,MACHINE learning ,DEEP learning ,LIFTING & carrying (Human mechanics) - Abstract
This paper proposes a deep convolutional neural network (DCNN) to design an accurate active sonar image classifier. In order to have a real-time classifier with low complexity, The LeNet-5 is utilized as the most straightforward deep network with the fewest parameters. For the sake of having a real-time training and test phase, the three fully connected layers are replaced by an extreme learning machine (ELM). However, tuning the ELM's input layer parameters is challenging; therefore, this paper tries to tune them using the grey wolf optimizer (GWO). Contrary to other research works and considering the sonar problem's characteristics, we model the problem as a multimodal function. Therefore, comprehensive learning concepts and a novel constraint-handling technique are exerted on the GWO to address the multimodality and the constraints of the sonar image classification task and to have a robust optimizer. Given the vital role of the reliable dataset in deep learning approaches, in the following, an operational underwater sonar test scenario is designed, and an experimental dataset is generated. The designed model is then benchmarked on two benchmark active sonar datasets. The results are investigated by qualified research with classic DCNN, Block-wise Classifier (BWC), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). The investigation outcomes confirm that the designed model, with an average accuracy of 98.69% and computation time equal to 883.44 s, reports the best accuracy and time complexity among other benchmark models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
26. Convolution Neural Network Based Prediction for Eye Gaze Estimation.
- Author
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G., Arpitha and A., Meenakshi Sundaram
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CONVOLUTIONAL neural networks ,GAZE ,EYE ,DEEP learning - Abstract
Levels of progress in progress have truly made it possible to get various kinds of biometric information from individuals, enabling bases on assessment of human conditions in cure, auto prospering, advancing, and various zones. These evaluations have particularly featured eye improvement as a convincing marker with respect to human conditions, and assessment on its applications is adequately being pursued. The contraptions as of now for the most part used for assessing eye overhauls rely on the video-oculography (VOG) procedure, wherein the course of look is outlined by managing eye pictures crushed a camera. Applying convolutional neural network (ConvNet) to the getting ready of eye pictures has been seemed to enable exact and unprecedented look assessment. Ordinary picture overseeing, in any case, is begun on execution using a PC, making it difficult to finish consistent look. We hence propose another eye picture overseeing framework that cycles look assessment and event disclosure starting with one fulfillment then onto the accompanying using a self-governing engineered lightweight ConvNet. This paper evaluates the course of action of the proposed lightweight ConvNet, the frameworks for learning and appraisal used, and the proposed methodology's ability to meanwhile see look heading and event occasion using a truly unassuming memory and at lower computational complex nature than standard ways of thinking. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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27. ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model.
- Author
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Li, Zhiheng, Gu, Tongcheng, Li, Bing, Xu, Wubin, He, Xin, and Hui, Xiangyu
- Subjects
CONVOLUTIONAL neural networks ,MULTICASTING (Computer networks) ,MACHINE learning - Abstract
Featured Application: This paper studies attention-related optimizations and innovations for the ConvNeXt network proposed in January 2022, providing a reference for subsequent researchers to optimize this network. Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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28. Intrusion Detection Algorithm Based on Convolutional Neural Network and Light Gradient Boosting Machine.
- Author
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Wang, Qian, Zhao, Wenfang, Wei, Xiaoyu, Ren, Jiadong, Gao, Yuying, and Zhang, Bing
- Subjects
CONVOLUTIONAL neural networks ,ALGORITHMS ,CLASSIFICATION algorithms ,DATA conversion ,MACHINERY ,ACID-base imbalances - Abstract
Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
29. Identifying Brand Consistency by Product Differentiation Using CNN.
- Author
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Hung-Hsiang Wang and Chih-Ping Chen
- Subjects
CONVOLUTIONAL neural networks ,PRODUCT differentiation ,DEEP learning ,BRAND name products ,PRINCIPAL components analysis ,TIME series analysis - Abstract
This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands. The investigation collected 6,042 images ofmouse devices and divided them into the Early Stage and the Late Stage. Results show the highest accuracy of 81.4% with the CNN model, and the evaluation score of brand style consistency is 0.36, implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world. The relationship between product appearance variation, brand style consistency, and evaluation score is beneficial for predicting new product styles and future product style roadmaps. In addition, the CNN heat maps highlight the critical areas of design features of different styles, providing alternative clues related to the blurred boundary. The study provides insights into practical problems for designers, manufacturers, and marketers in product design. It not only contributes to the scientific understanding of design development, but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency. Designers can use these techniques to find features that influence brand style. Then, capture these features as innovative design elements and maintain core brand values. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
30. Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer.
- Author
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Pan, WenKai, Zhu, Dong, Wang, Jutao, and Zhu, Haiyan
- Subjects
DEEP learning ,CONVOLUTIONAL neural networks ,ROBOT vision ,COLOR image processing ,PATTERN recognition systems ,MACHINE learning - Abstract
This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites.
- Author
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Jang, Seon Young, Oh, Byung Tae, and Oh, Eunsung
- Abstract
This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model's adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm.
- Author
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Draz, Moatasem. M., Emam, Osama, and Azzam, Safaa. M.
- Subjects
PARTICLE swarm optimization ,DEEP learning ,CONVOLUTIONAL neural networks ,STANDARD deviations ,MACHINE learning ,COMPUTER software industry - Abstract
Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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33. Industrial defective chips detection using deep convolutional neural network with inverse feature matching mechanism.
- Author
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Ullah, Waseem, Khan, Samee Ullah, Kim, Min Je, Hussain, Altaf, Munsif, Muhammad, Lee, Mi Young, Seo, Daeho, and Baik, Sung Wook
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CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,MACHINE learning ,DEEP learning ,ANOMALY detection (Computer security) ,INDUSTRIAL goods - Abstract
The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). AD in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increasingly recognize the significance of employing intelligent systems to detect flaws and defects in product parts. However, industrial settings pose several challenges: diverse categories, limited abnormal samples and vagueness. Hence, there is a growing demand for advanced image AD techniques within industrial product manufacturing. In this paper, an intelligent industrial defective chips detection framework is proposed which mainly consists of three core components. First, the convolutional features of the efficient backbone model is effectively utilized to balance the computational complexity and performance of industrial resource-constrained devices. Secondly, a novel inverse feature matching followed by masking method is proposed to enhance the explanability that localizes the abnormal regions of the abnormal chips. Finally, to evaluate our proposed method a comprehensive ablation study is conducted, where different machine learning and deep learning algorithms are analysed to claim the superiority of our method. Furthermore, to help the research community, a benchmark dataset is collected from real-world industry manufacturing for defective chip detection. The empirical results from the dataset demonstrate the strength and effectiveness of the proposed model compared to the other models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Attention-Oriented CNN Method for Type 2 Diabetes Prediction.
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Zhao, Jian, Gao, Hanlin, Yang, Chen, An, Tianbo, Kuang, Zhejun, and Shi, Lijuan
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TYPE 2 diabetes ,ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,MACHINE learning ,HEALTH & Nutrition Examination Survey ,OUTLIER detection ,FORECASTING - Abstract
Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. A hybrid framework for glaucoma detection through federated machine learning and deep learning models.
- Author
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Aljohani, Abeer and Aburasain, Rua Y.
- Subjects
DEEP learning ,MACHINE learning ,GLAUCOMA ,CONVOLUTIONAL neural networks ,VISION disorders ,COMPUTER-assisted image analysis (Medicine) - Abstract
Background: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts. Method: Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment. Result: The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis. Conclusion: In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology's potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. MACHINE LEARNING TECHNIQUES IN PLANT DISEASE DETECTION AND CLASSIFICATION - A STATE OF THE ART.
- Author
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John, Sreya and Rose, Arul Leena
- Subjects
DEEP learning ,MACHINE learning ,NOSOLOGY ,PLANT parasites ,FERTILIZERS ,IMAGE processing - Abstract
As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. Skin Diseases Classification Using Deep Leaning Methods
- Author
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UDRIȘTOIU, ANCA-LOREDANA, STANCA, ARIANA ELENA, GHENEA, ALICE ELENA, VASILE, CORINA MARIA, POPESCU, MIHAELA, UDRIȘTOIU, ȘTEFAN CRISTINEL, IACOB, ANDREEA VALENTINA, CASTRAVETE, STEFAN, GRUIONU, LUCIAN GHEORGHE, and GRUIONU, GABRIEL
- Subjects
Original Paper ,integumentary system ,Machine learning ,deep learning ,convolutional neural network ,dermatoscopic images, medical - Abstract
Due to the high incidence of skin tumors, the development of computer aided-diagnosis methods will become a very powerful diagnosis tool for dermatologists. The skin diseases are initially diagnosed visually, through clinical screening and followed in some cases by dermoscopic analysis, biopsy and histopathological examination. Automatic classification of dermatoscopic images is a challenge due to fine-grained variations in lesions. The convolutional neural network (CNN), one of the most powerful deep learning techniques proved to be superior to traditional algorithms. These networks provide the flexibility of extracting discriminatory features from images that preserve the spatial structure and could be developed for region recognition and medical image classification. In this paper we proposed an architecture of CNN to classify skin lesions using only image pixels and diagnosis labels as inputs. We trained and validated the CNN model using a public dataset of 10015 images consisting of 7 types of skin lesions: actinic keratoses and intraepithelial carcinoma/Bowen disease (akiec), basal cell carcinoma (bcc), benign lesions of the keratosis type (solar lentigine/seborrheic keratoses and lichen-planus like keratosis, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhages, vasc).
- Published
- 2020
38. Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning
- Author
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Jérôme Bosche, Federica Cilia, Gilles Dequen, Mahmoud Elbattah, Jean-Luc Guérin, Barbara Le Driant, Romuald Carette, Luc Vandromme, Centre de Recherche en Psychologie : Cognition, Psychisme et Organisations - UR UPJV 7273 (CRP-CPO), Université de Picardie Jules Verne (UPJV), Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), CHirurgie, IMagerie et REgénération tissulaire de l’extrémité céphalique - Caractérisation morphologique et fonctionnelle - UR UPJV 7516 (CHIMERE), and This article was supported by the research quality bonus of the University of Picardie Jules Verne at Amiens.
- Subjects
genetic structures ,Computer science ,diagnosis ,[SHS.PSY]Humanities and Social Sciences/Psychology ,Health Informatics ,Human Factors and Ergonomics ,autism spectrum disorder ,Machine learning ,computer.software_genre ,Convolutional neural network ,eye tracking ,03 medical and health sciences ,0302 clinical medicine ,Data visualization ,medicine ,data visualization ,0501 psychology and cognitive sciences ,Set (psychology) ,ASS ,Original Paper ,business.industry ,Deep learning ,screening ,05 social sciences ,Eye movement ,deep learning ,medicine.disease ,artificial intelligence ,ML ,Visualization ,machine learning ,AI ,adolescent ,[SCCO.PSYC]Cognitive science/Psychology ,Eye tracking ,Autism ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery ,050104 developmental & child psychology - Abstract
Background The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.
- Published
- 2021
39. Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.
- Author
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Zhen Zhao, Joon Huang Chuah, Khin Wee Lai, Chee-Onn Chow, Munkhjargal Gochoo, Samiappan Dhanalakshmi, Na Wang, Wei Bao, and Xiang Wu
- Subjects
DEEP learning ,ALZHEIMER'S disease ,MACHINE learning ,DIAGNOSIS ,MAGNETIC resonance imaging ,CONVOLUTIONAL neural networks - Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Improving the performance of deep learning models using statistical features: The case study of COVID‐19 forecasting
- Author
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Hossein Abbasimehr, Reza Paki, and Aram Bahrini
- Subjects
2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,62‐07 ,General Mathematics ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Context (language use) ,97r40 ,Machine learning ,computer.software_genre ,01 natural sciences ,Convolutional neural network ,Special Issue Paper ,0101 mathematics ,Combined method ,Mathematics ,Special Issue Papers ,business.industry ,Deep learning ,010102 general mathematics ,General Engineering ,deep learning ,COVID‐19 pandemic ,010101 applied mathematics ,hybrid methods ,Memory model ,Artificial intelligence ,business ,computer ,statistical features - Abstract
COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.
- Published
- 2021
41. Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model
- Author
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Mo, Lina, Qi, Xiaogang, and Liu, Lifang
- Published
- 2024
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42. IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach.
- Author
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Obaidi, Marwah Qasim and Derbel, Nabil
- Subjects
DEEP learning ,CONVOLUTIONAL neural networks ,MACHINE learning ,WATER pumps ,ARTIFICIAL intelligence - Abstract
One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images.
- Author
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Gallo, Ignazio, Boschetti, Mirco, Rehman, Anwar Ur, and Candiani, Gabriele
- Subjects
CONVOLUTIONAL neural networks ,BLENDED learning ,MACHINE learning ,SUPERVISED learning ,CHLOROPHYLL - Abstract
The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention.
- Author
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Kang Xiaofeng, Hu Kun, and Ran Li
- Subjects
ACOUSTIC emission ,ARTIFICIAL neural networks ,MACHINE learning ,RECURRENT neural networks ,DEEP learning - Abstract
Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection and attention mechanism, each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient (MFCC), Tempogram, and short-time Fourier transform (STFT) spectral respectively. Experimental results show that, in comparison with traditional classification methods and single-stream CNN networks, TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%, which demonstrates the availability of the model proposed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition
- Author
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Nick F. Linton, Graham D. Cole, Darrel P. Francis, James P. Howard, Daniel Rueckert, Anil A. Bharath, Kerry Hall, Greg Leonard, Sharon Sutanto, Yousuf Razvi, Vijay Ramadoss, Matthew Shun-Shin, Aaraby Ragavan, Sameer Zaman, and Rosetrees Trust
- Subjects
Cardiomyopathy, Dilated ,Scanner ,medicine.medical_specialty ,Artificial intelligence ,Cardiac & Cardiovascular Systems ,030204 cardiovascular system & hematology ,Convolutional neural network ,Proof of Concept Study ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Automation ,0302 clinical medicine ,Cardiac magnetic resonance imaging ,Predictive Value of Tests ,Machine learning ,Image Interpretation, Computer-Assisted ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,1102 Cardiorespiratory Medicine and Haematology ,Cardiac imaging ,Aorta ,Original Paper ,Science & Technology ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Deep learning ,Radiology, Nuclear Medicine & Medical Imaging ,Reproducibility of Results ,Magnetic resonance imaging ,Cardiomyopathy, Hypertrophic ,Magnetic Resonance Imaging ,Pleural Effusion ,Nuclear Medicine & Medical Imaging ,Great vessels ,Cardiovascular System & Cardiology ,Radiology ,Neural Networks, Computer ,Cardiology and Cardiovascular Medicine ,business ,Life Sciences & Biomedicine ,Neural networks - Abstract
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency. Electronic supplementary material The online version of this article (10.1007/s10554-020-02050-w) contains supplementary material, which is available to authorized users.
- Published
- 2020
46. Vision-based size classification of iron ore pellets using ensembled convolutional neural network.
- Author
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Deo, Arya Jyoti, Sahoo, Animesh, Behera, Santosh Kumar, and Das, Debi Prasad
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CONVOLUTIONAL neural networks ,IRON ores ,PELLETIZING ,DATA augmentation ,MACHINE learning - Abstract
In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. Image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection
- Author
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Hamed Nasr Eldin T. Mohamed, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa, and Mohamed Loey
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Computer science ,0211 other engineering and technologies ,Convolutional Neural Network ,02 engineering and technology ,Overfitting ,Machine learning ,computer.software_genre ,Convolutional neural network ,Diabetic Eye Disease ,Machine Learning ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,021110 strategic, defence & security studies ,Original Paper ,Diabetic Retinopathy ,business.industry ,Deep learning ,General Medicine ,Diabetic retinopathy ,medicine.disease ,Deep Transfer Learning ,020201 artificial intelligence & image processing ,Artificial intelligence ,Transfer of learning ,business ,F1 score ,computer - Abstract
Introduction Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF). Aim With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future. Methods In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem. Results The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity.
- Published
- 2019
48. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects
- Author
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Alexander Reiterer, Rodrigo Suarez-Ibarrola, Arkadiusz Miernik, Simon Hein, and Misgana Negassi
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Urology ,02 engineering and technology ,Machine learning ,computer.software_genre ,Convolutional neural network ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Data acquisition ,Medical image analysis ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Image Processing, Computer-Assisted ,Humans ,Bladder cancer ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Deep learning ,Frame (networking) ,Cystoscopy ,medicine.disease ,Topic Paper ,Visualization ,Cystoscopic images ,030220 oncology & carcinogenesis ,020201 artificial intelligence & image processing ,Artificial intelligence ,Neural Networks, Computer ,business ,computer ,Neural networks ,Forecasting - Abstract
BackgroundOptimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.ObjectiveTo provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition.Evidence acquisitionA detailed search of original articles was performed using the PubMed-MEDLINE database to identify recent English literature relevant to ML, DL and CNN applications in cystoscopic image recognition.Evidence synthesisIn total, two articles and one conference abstract were identified addressing the application of AI methods in cystoscopic image recognition. These investigations showed accuracies exceeding 90% for tumor detection; however, future work is necessary to incorporate these methods into AI-aided cystoscopy and compared to other tumor visualization tools. Furthermore, we present results from the RaVeNNA-4pi consortium initiative which has extracted 4200 frames from 62 videos, analyzed them with the U-Net network and achieved an average dice score of 0.67. Improvements in its precision can be achieved by augmenting the video/frame database.ConclusionAI-aided cystoscopy has the potential to outperform urologists at recognizing and classifying bladder lesions. To ensure their real-life implementation, however, these algorithms require external validation to generalize their results across other data sets.
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- 2020
49. Self-healing hybrid intrusion detection system: an ensemble machine learning approach.
- Author
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Kushal, Sauharda, Shanmugam, Bharanidharan, Sundaram, Jawahar, and Thennadil, Suresh
- Subjects
INTRUSION detection systems (Computer security) ,MACHINE learning ,COMPUTER network traffic ,CONVOLUTIONAL neural networks ,RECURRENT neural networks ,FALSE alarms - Abstract
The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Impacts of DEM type and resolution on deep learning-based flood inundation mapping.
- Author
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Fereshtehpour, Mohammad, Esmaeilzadeh, Mostafa, Alipour, Reza Saleh, and Burian, Steven J.
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DEEP learning , *CONVOLUTIONAL neural networks , *MACHINE learning , *DIGITAL elevation models , *FLOOD risk , *WATER depth , *FLOODS - Abstract
The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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