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Relevant bibliographies by topics / Deeplearning

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Academic literature on the topic 'Deeplearning'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2024

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Journal articles on the topic "Deeplearning"

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Pulaparthi, Naga MahaLakshmi, Madhulika Dabbiru, Charishma Penkey, and Dr Nrusimhadri Naveen. "Brain Stroke Detection Using DeepLearning." International Journal of Research Publication and Reviews 4, no. 4 (April 2023): 2468–73. http://dx.doi.org/10.55248/gengpi.4.423.35996.

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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 209–15. http://dx.doi.org/10.18280/ria.350304.

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Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

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A. J., Anju, and J. E. Judith. "Optimized Deeplearning Algorithm for Software Defects Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (August 31, 2023): 173–88. http://dx.doi.org/10.17762/ijritcc.v11i9s.7409.

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Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%.

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Motwani, Nilesh Parmanand, and Soumya S. "Human Activities Detection using DeepLearning Technique- YOLOv8." ITM Web of Conferences 56 (2023): 03003. http://dx.doi.org/10.1051/itmconf/20235603003.

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Using a mask during the pandemic has occasionally been crucial and difficult. The use of universal masks can greatly lower and possibly even stop the spread of viruses within communities. So, mask detection has become a very critical task for security agencies in all the buildings, Government offices & other places. With the advent of GPUs, high computing machines, and Deep Convolution Neural Networks (DCCN), automatic Face & Mask Detection is possible by considering the image processing feature of extracting, 3-dimensional shapes from 2- dimensional images. This paper discuss about the YOLOv8 model to confirm its overall applicability, on two datasets namely FDDB & MASK. This helps to examine the behavior of the feature from the Mask dataset, which is intended for COVID-19 Mask Detection alone. Mask is the main dataset in this experiment. Above this, the ImageNet dataset is utilized for pretraining and FDDB (Face Detection Dataset & Benchmarks) datasets for recognizing face of a human being. The precision of models on FDDB is 58.9 % & on MASK dataset is 66.5%.

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Sherly, S. Irin, J. Sandhiya, S. Priyanga, M. A. Sharon Victoriya, and K. Sorna Ajantha. "Prediction of Cardiovascular Disease using DeepLearning Algorithm." Journal of Cognitive Human-Computer Interaction 5, no. 1 (2023): 20–31. http://dx.doi.org/10.54216/jchci.050102.

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The leading cause of death, which affects millions of individuals globally is the cardiovascular disease. Heart problems are a major issue in health care, particularly in the field of cardiology. Due to a number of risk factors, including diabetes, high blood pressure, high cholesterol, an irregular pulse rate, obesity, and smoking, cardiac illness is difficult to detect. Due to these limitations, researchers are now using Data Mining and Deep Learning Algorithms to predict heart related disorders. The Cardio Vascular Disease (CVD) is as complicated as it sounds if left untreated. So, the early prediction of this could save millions of people from silent attacks, myocardial infarction etc. Many machine learning algorithms like Naïve Bayes, K-Nearest Neighbor Algorithm (KNN), Decision Trees (DT), Genetic algorithm (GA) are used for cardiovascular disease prediction using text datasets and their efficiencies tend to differ. Generally, convolutional neural network (CNN) algorithm is mostly used for prediction using images. But our concept is to switch over this and predict heart disease using the CNN algorithm for Cleveland dataset which consists of numerical. In this dataset we consider 14 attributes and used K Nearest Neighbor and CNN algorithm. In terms of accuracy, CNN beats KNN, proving that deep learning algorithms may support decision-making and prediction-making based on vast volumes of data supplied by the healthcare sector.

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Li, Xun, Xin Yun, Zhengfan Zhao, Kaibin Zhang, and Xiaohua Wang. "Lightweight Deeplearning Method for Multi-vehicle Object Recognition." Information Technology and Control 51, no. 2 (June 23, 2022): 294–312. http://dx.doi.org/10.5755/j01.itc.51.2.30667.

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The recognition method based on deep learning has a large amount of calculation for the changes of different traffic densities in the actual traffic environment. In this paper, an integrated recognition method YOLOv4-L is proposed for reducing computational complexity based on the YOLOv4. The characteristics of multi-lane traffic flow with different flow densities were analyzed for statistical data sets, and k-means++ clustering algorithm was used to optimize the prior frame parameters to improve the matching degree between the prior frame. GhostNet was used to replace CSPDarknet53 of original network structure of YOLOv4 as the feature extraction network. The depthwise separable convolution module was introduced to replace the original 3×3 common convolution in feature extraction network, reduce model parameters and improve detection speed. The network model is further improved both with accuracy and robustness with the help of comprehensive method of Mosaic data enhancement, learning rate cosine annealing and label smoothing. Experimental results show that, Recognition speed is greatly improved at the expense of minimal recognition accuracy reduction: the recognition speed improvement value is 47.81%, 49.15% , 56.06% in detection speed (FPS), respectively in free flow, synchronous flow and blocked flow, the reduction value of accuracy is 2.21%, 0.67%,, 0.05% mAP, respectively.

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Doroshenko, А. Yu, V. M. Shpyg, and R. V. Kushnirenko. "Deeplearning-based approach to improving numerical weather forecasts." PROBLEMS IN PROGRAMMING, no. 3 (September 2023): 91–98. http://dx.doi.org/10.15407/pp2023.03.091.

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This paper briefly describes the history of numerical weather prediction development. The difficulties, which occur in the modelling of atmospheric processes, their nature and possible ways of their mitigation, are described. It also indicates alternative methods of improving the quality of meteorological forecasts. A brief history of deep learning and possible ways of its application to meteorological problems are given. Then, the paper describes the format used to store the 2m temperature forecasts of the COSMO numerical regional model. The proposed neural network architecture enables correcting the forecast errors of the numerical model. We conducted the experiments on the data of eight meteorological stations of the Kyiv region, so we obtained eight trained neural network models. The results showed that the proposed architecture enables obtaining better-quality forecasts in more than 50% of cases. Root-mean-square errors of the resulting forecasts decreased, and it is a widespread skill-score of improved-quality forecasts in meteorological science.

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Tiku, Johanes Christianto, Wahyu Andi Saputra, and Novian Adi Prasetyo. "Pengembangan Sistem Deteksi Memakai Masker Menggunakan Open CV, Tensorflow dan Keras." JURIKOM (Jurnal Riset Komputer) 9, no. 4 (August 30, 2022): 1183. http://dx.doi.org/10.30865/jurikom.v9i4.4739.

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In 2020 the country had contracted the covid 19 virus. The very significant spread made the government have to issue regulations regarding the implementation of health protocols for wearing mask to inhibit the development of the covid 19 virus in the community in real time. By utilizing technological developments in the field of deeplearning and computer vision, this study aims to detect maskon people's faces based on the classification made in the system of wearing maskand not wearing memakai maskers. This study used tensorflow/hard, opencv and deeplearning to perform classification and detection on faces. Based on the results of the confusion matrix test using 100 test data with a group of 50 wearing maskand 50 not wearing memakai maskers, it resulted in 91% Accuracy to detect maskon the faceTRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

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Jo and Kim. "NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition." Symmetry 11, no. 10 (October 3, 2019): 1234. http://dx.doi.org/10.3390/sym11101234.

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Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.

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Sidana, Khushi. "REAL TIME YOGA POSE DETECTION USING DEEPLEARNING: A REVIEW." International Journal of Engineering Applied Sciences and Technology 7, no. 7 (November 1, 2022): 61–65. http://dx.doi.org/10.33564/ijeast.2022.v07i07.011.

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With the increase in the number of yoga practitioners every year, the risk of injuries as a result of incorrect yoga postures has also increased. A selftraining model that can evaluate the posture of individuals is the optimal solution for this issue. This objective can be attained with the aid of computer vision and deep learning. A model that can detect theyoga pose performed by an individual, evaluate it in comparison to the pose performed by an expert, and provide the individual with instructive feedback would be an effective solution to this problem. Recently, numerous researchers have conducted experiments on the detection and performance of yoga poses in real time. This paper discusses the methods undertaken in brief and compares the tools and algorithms they used for conducting pose estimation, pose detection as well as pose assessment. Itdiscusses the accuracy, precision, and similarity of pose classification obtained by the researchers and the future scope of the research.

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Dissertations / Theses on the topic "Deeplearning"

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Kapoor, Rishika. "Malaria Detection Using Deep Convolution Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749143868579.

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Accornero, Andrea. "Covid-19 x-ray Analisi con reti neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24952/.

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Machine Learning e Deep Learning nell'ambito medico. Focus sull'imaging medico. Analisi di un DataSet con 188 radiografie di toraci tramite addestramento con Reti Neurali Convoluzionali, mostrando andamento del Misclassification Rate al variare dei parametri della Rete.

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Fall, Ahmad. "Interpretability of Neural Networks applied to Electrocardiograms : Translational Applications in Cardiovascular Diseases." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS473.pdf.

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L’électrocardiogramme (ECG) est un outil non invasif permettant d’évaluer l’activité électrique du cœur. Ils sont largement utilisés dans la détection d’anomalies cardiaques. Les algorithmes d’apprentissage profond permettent la détection automatique de schémas complexes dans les données ECG, ce qui offre un potentiel important pour l’amélioration du diagnostic médical. Toutefois, leur adoption est freinée par un faible niveau de confiance des cliniciens et un besoin massif de données pour entrainer les modèles. L’intelligence artificielle, en particulier l’apprentissage profond (deep learning), permet d’explorer des représentations hiérarchiques de données complexes, ce qui permet de mieux comprendre les interactions internes. Néanmoins, l’interprétabilité des modèles est cruciale pour gagner la confiance des spécialistes et permettre une utilisation générale. Ces travaux de thèse, réalisés en étroite collaboration avec des spécialistes en cardiologie, visent à développer un nouvel algorithme d’interprétabilité pour les réseaux de neurones appliqués aux données ECG. Notre étude se concentre sur une pathologie cardiaque spécifique, la Torsades de pointes (TdP). La TdP est une arythmie mortelle associée à divers facteurs, notamment médicamenteux et/ou des mutations congénitales. Une prédiction précise de ce risque peut améliorer les soins aux patients et potentiellement sauver des vies. Nous avons commencé par concevoir un réseau de neuronnes pour prédire le risque de TdP à l’aide de données ECG. Ensuite, nous avons développé un nouvel algorithme d’interprétabilité baptisé Evocclusion, qui permet de mieux comprendre le processus de décision du réseau de neurones. Cet algorithme vise à fournir des informations lisibles par l’homme sur les prédictions du modèle, afin d’accroître la confiance des cliniciens et des spécialistes. Enfin, nous présentons deux autres méthodes développées pour améliorer l’analyse de l’ECG et la méthode d’interprétabilité. La qualité du signal est un aspect crucial dans l’analyse d’ECGs. Ainsi, nous proposons une nouvelle méthode utilisant un autoencodeur de débruitage pour réduire de manière significative le bruit présent dans les données ECG et reconstruire partiellement le signal. Cette technique améliore la fiabilité des données d’entrée pour des analyses approfondies et garantit que les réseaux de neurones ont accès à des informations de haute qualité. Nous avons également développé des réseaux supplémentaires pour segmenter l’ECG et extraire les battements, les ondes P et T et complexes QRS. Cette segmentation permet une compréhension plus approfondie des composants de l’ECG et ouvre la voie à de nouvelles analyses sur des composantes spécifiques du signal. En outre, nous fournissons une méthode pour évaluer un vecteur score de qualité ECG, ce qui nous permet de nous concentrer sur les parties du signal qui ont un bon score de qualité. Cette approche garantit que les informations les plus fiables sont utilisées pour l’analyse et les cliniciens, ce qui réduit le risque de faux positifs et négatifs. Cette recherche vise à renforcer la confiance dans l’utilisation de réseau de neurones, ce qui permettra d’améliorer l’automatisation des tâches complexes en médecine et ailleurs, et, en fin, d’améliorer le traitement des patients
Electrocardiograms (ECGs) are non-invasive tools for assessing the electrical activity of the heart, they are widely used to detect cardiac abnormalities. Deep learning algorithms enable automatic detection of complex patterns in ECG data, offering significant potential for improved cardiac diagnosis. However, their adoption is hindered by a low level of trust among medical professionals and a substantial need for data to train the models. Artificial intelligence, particularly deep learning, allows for exploration of hierarchical representations of complex data, leading to a better understanding of internal interactions. Nevertheless, interpretability of the models are crucial to gain specialists’ trust and facilitate widespread implementation. This thesis aims to develop a novel interpretability algorithm for neural networks applied to ECG analysis, working in close collaboration with cardiology specialists. Our study focuses on a specific cardiac pathology, Torsades-de-Pointes (TdP). TdP is a life threatening arrhythmia associated with various factors, including medications and congenital mutations. Accurate prediction of this risk can enhance patient care and potentially save lives. We started by designing a neural network algorithm for predicting the risk of TdP using ECG data. Second, we developed a new interpretability algorithm named Evocclusion, that enables a better understanding of the neural network’s decision process. This algorithm aims to provide human readable insights into the model’s predictions, leading to increased trust among clinicians and specialists. Third, we present two main frameworks developed to improve ECG analysis and the interpretability method. A crucial aspect of ECG analysis is signal quality. Therefore, we propose a new method using a denoising autoencoder to significantly remove noise from the ECG data and partially recover the waveform from alterations. This technique improves the reliability of the input data for subsequent analysis and ensures that the neural networks have access to high quality information. We also developed neural networks to segment the ECG and extract beats, P and T waves, and QRS complexes. These segmentation results enable a deeper understanding of the ECG components and facilitate further analysis. Additionally, we provide a method to assess a quality score vector of the ECG, enabling us to focus on parts of the signal that have a good quality score. This approach ensures that the most reliable information is used for analysis and clinicians which reduces the risk of false positives and negatives. This research seeks to enhance trust in artificial intelligence, leading to better automation of complex tasks in medicine and beyond, ultimately improving patient outcomes

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Alshatta, Mohammad Samer. "Real Time Gym Activity Detection using Monocular RGB Camera." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41440.

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Action detection is an attractive area for researchers in computer vision, healthcare, physiotherapy, psychology, and others. Intensive work has been done in this area due to its wide range of applications such as security surveillance, video tagging, Human-Computer Interaction (HCI), robotics, medical diagnosis, sports analysis, interactive gaming, and many others. After the deep learning booming results in computer vision tasks like image classification, many researchers have tried to extend the success of deep learning models to video classification and activity recognition. The research question of this thesis is to study the use of the 2D human poses extracted by a DNN-based model from RGB frames only, for the online activity detection task and comparing it with the state of the art solutions that utilize the human 3D skeletal data extracted by a depth sensor as an input. At the same time, this work showed the importance of input pre-processing and filtering on improving the performance of the online human activity detector. Detecting gym exercises and counting the repetitions in real-time using the human skeletal data versus the 2D poses have been studied in-depth in this work. The contributions of this work are as follows: 1) generating RGB-D dataset for a set of gym exercises, 2) proposing a novel real-time skeleton-based Double Representational RNN (DR-RNN) network architecture for the online action detection, 3) Demonstrating the ability of the proposed model to achieve satisfiable results using pose estimation models applied on RGB frames, 4) introducing a novel learnable exponential filter for the online low latency filtering applications.

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Stymne, Jakob, and Odeback Oliver Welin. "Evaluation of Temporal Convolutional Networks for Nanopore DNA Sequencing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295624.

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Nanopore sequencing, a recently developed methodfor DNA sequencing, involves applying a constant electricfield over a membrane and translocating single-stranded DNAmolecules through membrane pores. This results in an electricalsignal, which is dependent on the structure of the DNA. The aimof this project is to train and evaluate a non-causal temporalconvolution neural network in order to accurately translate suchelectrical raw signal into the corresponding nucleotide sequence.The training dataset is sampled from the E. coli bacterial genomeand the phage Lambda virus. We implemented and evaluatedseveral different temporal convolutional architectures. Using anetwork with five residual blocks with five convolutional layersin each block yields maximum performance, with a predictionaccuracy of 76.1% on unseen test data. This result indicates thata temporal convolution network could be an effective way tosequence DNA data.
Nanopore sequencing är en nyligen utvecklad metod för DNA-sekvensering som innebär att man applicerar ett konstant elektriskt fält över ett membran och translokerar enkelsträngade DNA-molekyler genom membranporer. Detta resulterar i en elektrisk signal som beror på DNA-strukturen. Målet med detta projekt är att träna och evaluera icke-kausula ”temporal convolutional networks” som ska kunna översätta denna ofiltrerade elektriska signalen till den motsvarande nukleotidsekvensen. Träningsdatan är ett urval av genomen från bakterien E. coli och viruset phage Lambda. Vi implementerade och utvärderade ett antal olika nätverksstrukturer. Ett nätverk med fem residuala block med fem faltande lager i varje block gav maximal prestation, med en precision på 76.1% på testdata. Detta resultat indikerar att ett ”temporal convolution network” skulle kunna vara ett effektivt sätt att sekvensera DNA.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

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Di, Ielsi Luca. "Analisi di serie temporali riguardanti dati energetici mediante architetture neurali profonde." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10504/.

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Il presente lavoro di tesi riguarda lo studio e l'impiego di architetture neurali profonde (nello specifico stacked denoising auto-encoder) per la definizione di un modello previsionale di serie temporali. Il modello implementato è stato applicato a dati industriali riguardanti un impianto fotovoltaico reale, per effettuare una predizione della produzione di energia elettrica sulla base della serie temporale che lo caratterizza. I risultati ottenuti hanno evidenziato come la struttura neurale profonda contribuisca a migliorare le prestazioni di previsione di strumenti statistici classici come la regressione lineare multipla.

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Ingelhag, Anders. "Samarbete mellan tekniklärare vid framtagning av undervisningsmaterial." Thesis, Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33658.

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Ingången till detta arbete är att författaren upplever framtagning av material att använda i undervisningen som en mycket tidkrävande process. För lärare som börjar undervisa i en ny kurs blir detta extra tydligt när allt material ska tas fram. Med material menas planeringar, lektionsinnehåll, instuderingsuppgifter och bedömningsmaterial. Författaren har förförståelsen att lärare skulle vinna på att samarbeta och dela material mellan sig. I arbetet undersöks, genom en enkätundersökning, hur gymnasielärare i teknik gör när de tar fram material till en ny kurs eller utvecklar materialet till en kurs. Vidare undersöks vilka eventuella hinder det finns för samarbete och vilket material lärare helst vill få tillgång till från kollegor. Resultatet visar att den absoluta majoriteten av lärarna i den undersökta gruppen inte ser några hinder att dela sitt material. Gymnasielärarna i teknik delar med sig. Det kan dock finnas praktiska hinder för om att läraren är ensam på sin skola att undervisa i ämnet, finns det ingen att samarbeta med. Eller att det inte finns någon gemensam lättanvänd Community, en mötesplats på internet, att dela på. I arbetet förs också en diskussion kring möjligheter med digitalisering av material.

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Racette, Olsén Michael. "Electrocardiographic deviation detection : Using long short-term memory recurrent neural networks to detect deviations within electrocardiographic records." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76411.

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Artificial neural networks have been gaining attention in recent years due to theirimpressive ability to map out complex nonlinear relations within data. In this report,an attempt is made to use a Long short-term memory neural network for detectinganomalies within electrocardiographic records. The hypothesis is that if a neuralnetwork is trained on records of normal ECGs to predict future ECG sequences, it isexpected to have trouble predicting abnormalities not previously seen in the trainingdata. Three different LSTM model configurations were trained using records fromthe MIT-BIH Arrhythmia database. Afterwards the models were evaluated for theirability to predict previously unseen normal and anomalous sections. This was doneby measuring the mean squared error of each prediction and the uncertainty of over-lapping predictions. The preliminary results of this study demonstrate that recurrentneural networks with the use of LSTM units are capable of detecting anomalies.

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Antonini, Lorenzo. "Reinforcement Learning Middleware Solutions for Android-oriented Distributed Deployments." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Oggigiorno i dispositivi mobili sono l’artefatto tecnologico a maggior contatto con le persone. Pieni di sensori in grado di percepire l’ambiente circostante e dalla notevole potenza computazionale, risultano essere l’ambiente perfetto per creare applicazioni in grado di predire le azioni future e di evolvere in base alle continue scelte dell’utilizzatore. Negli ultimi anni si è fatto sempre più prorompente, nell’ambito dell’intelligenza artificiale, il Reinforcement Learning. Molte conoscenze matematiche e di programmazione sono necessarie per sfruttare al meglio questa famiglia di algoritmi all’interno delle proprie applicazioni. In questa tesi presentiamo DroidForce, un middleware per lo sfruttamento semplificato nelle proprie applicazioni di agenti di Reinforcement Learning. DroidForce espone una serie di API intuitive per la creazione e l’allenamento di questi agenti. Questo permetterà allo sviluppatore di potersi concentrare sulla logica applicativa, sfruttando la potenza del Reinforcement Learning come una black box aggiungendo poche linee di codice. Implementeremo DroidForce come una libreria Android e attraverso una applicazione demo mostreremo che rappresenta una soluzione efficiente ed efficace per integrare il Reinforcement Learning all’interno dei dispositivi mobili.

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Majtán, Martin. "Trénovatelná segmentace obrazu s použitím hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241142.

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Diploma thesis is aimed to trainable image segmentation using deep neural networks. In the paper is explained the principle of digital image processing and image segmentation. In the paper is also explained the principle of artificial neural network, model of artificial neuron, training and activation of artificial neural network. In practical part of the paper is created an algorithm of sliding window to generate sub-images from image from magnetic rezonance. Generated sub-images are used to train, test and validate of the model of neural network. In practical part of the paper si created the model of the artificial neural network, which is used to trainable image segmentation. Model of the neural network is created using the Deeplearning4j library and it is optimized to parallel training using Spark library.

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Books on the topic "Deeplearning"

1

Raj, Rahul. Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j. Packt Publishing, 2019.

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Karim, Md Rezaul. Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs. Packt Publishing, 2018.

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Joshi, Nisheeth. Hands-On Artificial Intelligence with Java for Beginners: Build intelligent apps using machine learning and deep learning with Deeplearning4j. Packt Publishing, 2018.

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Kienzler, Romeo. Mastering Apache Spark 2.x - Second Edition: Scale your machine learning and deep learning systems with SparkML, DeepLearning4j and H2O. Packt Publishing - ebooks Account, 2017.

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Book chapters on the topic "Deeplearning"

1

Himabindu, Y., R. Manjusha, and Latha Parameswaran. "Detection and Removal of RainDrop from Images Using DeepLearning." In Computational Vision and Bio-Inspired Computing, 1355–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_142.

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Nagar, Pritesh, Hemant Kumar Menaria, and Manish Tiwari. "Novel Approach of Intrusion Detection Classification Deeplearning Using SVM." In First International Conference on Sustainable Technologies for Computational Intelligence, 365–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0029-9_29.

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Kahla, Mayssa Ben, Dalel Kanzari, and Ahmed Maalel. "DeepLCP: Towards a DeepLearning Approach to Prevent Lung Cancer." In Digital Health in Focus of Predictive, Preventive and Personalised Medicine, 17–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3_3.

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Renard, Arnaud, Jean-Matthieu Etancelin, and Michael Krajecki. "romeoLAB: A High Performance Training Platform for HPC, GPU and DeepLearning." In Communications in Computer and Information Science, 55–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73353-1_4.

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Diao, Chunyan, Dafang Zhang, Wei Liang, Kuan-Ching Li, and Man Jiang. "CRFST-GCN: A Deeplearning Spatial-Temporal Frame to Predict Traffic Flow." In Algorithms and Architectures for Parallel Processing, 3–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95384-3_1.

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Zairi, Khadidja. "DeepLearning for Computer Vision Problems." In Advanced Deep Learning Applications in Big Data Analytics, 92–109. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2791-7.ch005.

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Deep learning is a combined area between neural network and machine learning. Over the last years, deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields. With computer vision being one of the most prominent cases, the deep learning methodology applies nonlinear transformations and model abstractions of high levels in large databases. Therefore, an overview of DL methodology is provided along with its major modal principals and its hierarchy, which are presented and compared with the more conventional algorithms. Likewise, its popularity and usefulness in the artificial intelligence world are discussed, and some important techniques that increase DL performance are highlighted.

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Bhargavi, K. "Deep Learning Architectures and Tools." In Deep Learning Applications and Intelligent Decision Making in Engineering, 55–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2108-3.ch002.

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Deep learning is one of the popular machine learning strategies that learns in a supervised or unsupervised manner by forming a cascade of multiple layers of non-linear processing units. It is inspired by the way of information processing and communication pattern of the typical biological nervous system. The deep learning algorithms learn through multiple levels of abstractions and hierarchy of concepts; as a result, it is found to be more efficient than the conventional non-deep machine learning algorithms. This chapter explains the basics of deep learning by highlighting the necessity of deep learning over non-deep learning. It also covers discussion on several recently developed deep learning architectures and popular tools available in market for deep learning, which includes Tensorflow, PyTorch, Keras, Caffe, Deeplearning4j, Pylearn2, Theano, CuDDN, CUDA-Convnet, and Matlab.

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Conference papers on the topic "Deeplearning"

1

Cai, Jingjing, Jianping Li, Wei Li, and Ji Wang. "Deeplearning Model Used in Text Classification." In 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2018. http://dx.doi.org/10.1109/iccwamtip.2018.8632592.

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Rafi, Sk Mohammad, and Shaheda Akthar. "ECG Classification using a Hybrid Deeplearning Approach." In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021. http://dx.doi.org/10.1109/icais50930.2021.9395897.

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Kumar, Manish, and Swati Srivastava. "Emotion Detection through Facial Expression using DeepLearning." In 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702451.

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Srividya, K., S. Nagaraj, B. Puviyarasi, T. Sathies Kumar, A. Robinson stain Rufus, and G. Sreeja. "Deeplearning Based Bird Deterrent System for Agriculture." In 2021 4th International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2021. http://dx.doi.org/10.1109/iccct53315.2021.9711779.

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Rpunithavathi, Dr, Mr Mukesh Sai M, Mr Hiruthik R, Mr Sripadmesh S, and Mr Kishore RV. "Deepfake Detection with Deeplearning Using Resnet CNN Algorithm." In International Conference on Recent Trends in Data Science and its Applications (ICRTDA 2023). Denmark: River Publishers, 2023. http://dx.doi.org/10.13052/rp-9788770040723.209.

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Haritha, D., M. Krishna Pranathi, and M. Reethika. "COVID Detection from Chest X-rays with DeepLearning: CheXNet." In 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE, 2020. http://dx.doi.org/10.1109/icccs49678.2020.9277077.

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Suriya Prakash, A., D. Vigneshwaran, R. Seenivasaga Ayyalu, and S. Jayanthi Sree. "Traffic Sign Recognition using Deeplearning for Autonomous Driverless Vehicles." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418437.

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Wang, Qizhi, and ZiRu Wang. "Research on Deploying the Deeplearning Models with Embedded Devices." In 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2019. http://dx.doi.org/10.1109/cyber46603.2019.9066643.

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MK, Husna, and Mredhula L. "Prediction of Brain Tumor on MRI Images Using Deeplearning." In 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS). IEEE, 2021. http://dx.doi.org/10.1109/icmss53060.2021.9673656.

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Li, Hao, and Guomin Li. "Research on Facial Expression Recognition Based on LBP and DeepLearning." In 2019 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2019. http://dx.doi.org/10.1109/icris.2019.00032.

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