Relevant bibliographies by topics / Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras"

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Nizami Huseyn, Elcin. "DEEP LEARNING METHOD FOR EARLY PROGNOSIS OF PARKINSON’S DISEASE ACUTENESS." NATURE AND SCIENCE 02, no. 03 (2020): 7–12. http://dx.doi.org/10.36719/2707-1146/03/7-12.

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Generally, Parkinson’s disease (PD) in medicine is a long-term neurodegenerative and progressive disorder. In some brain parts, as the dopamine generating neurons die or they are damaged. Then people begin to have difficulty in walking, writing, speaking or making other basic missions Some of the indications of the disease worsen over time and thus result in increased acuteness of Parkinson's disease. We have proposed a methodology for the prognosis of Parkinson’s disease acuteness. In this scientific article, we used deep neural networks in UCI's Parkinson's telemonitoring voice dataset patie
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Shah, Dhairya. "Car Image Classification and Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 2096–101. http://dx.doi.org/10.22214/ijraset.2021.38336.

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Abstract: Vehicle positioning and classification is a vital technology in intelligent transportation and self-driving cars. This paper describes the experimentation for the classification of vehicle images by artificial vision using Keras and TensorFlow to construct a deep neural network model, Python modules, as well as a machine learning algorithm. Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex, and less accurate. The outcomes are used to assess the best cam
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Shrivastava, Anurag. "Deep Learning model based on CNN using Keras and TensorFlow to determine real time melting point of chemical substances." ELCVIA Electronic Letters on Computer Vision and Image Analysis 23, no. 1 (2024): 47–67. http://dx.doi.org/10.5565/rev/elcvia.1527.

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Deep learning is a subset of machine learning that uses artificial neural networks inspired by human cognitive systems. Although this is a newly approach recently it became very popular and effective. In many applications deep learning become most successful approach where machine learning has been successful at certain rates. In the succession of these the proposed deep learning model is suitable for melting point detection apparatus which determine melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classi
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Wilkins, J., M. V. Nguyen, and B. Rahmani. "Application of Convolutional Neural Network In LAWN Measurement." Signal & Image Processing : An International Journal 12, no. 1 (2021): 1–8. http://dx.doi.org/10.5121/sipij.2021.12101.

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Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, deep learning method, specifically Convolutional neural network, was applied to measure the lawn area. We used Keras and TensorFlow in Python to develop a model that was trained on the dataset of houses then tuned the parameters with GridSearchCV in ScikitLearn (a machine learning library in Python) to estimate the lawn area. Convolutional neural network o
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Malik, Indu, Anurag Singh Baghel, and Harshit Bhardwaj. "Deep learning for sustainable agriculture: Weed classification model to optimize herbicide application." Journal of Autonomous Intelligence 7, no. 5 (2024): 1403. http://dx.doi.org/10.32629/jai.v7i5.1403.

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<p>Herbicides, chemical substances designed to eliminate weeds, find widespread use in agriculture to eradicate unwanted plants and enhance crop productivity, despite their adverse impacts on both human health and the environment. The study involves the construction of a neural network classifier employing a Convolutional Neural Network (CNN) through Keras to categorize images with corresponding labels. This research paper introduces two distinct neural networks: a basic neural network and a hybrid variant combining CNN with Keras. Both networks undergo training and testing, yielding an
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Lafta, Noor Abdalkareem. "A Comprehensive Analysis of Keras: Enhancing Deep Learning Applications in Network Engineering." Babylonian Journal of Networking 2023 (November 26, 2023): 94–100. https://doi.org/10.58496/bjn/2023/012.

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Python is currently one of the most popular programming languages that is used in the sector; it has surpassed many of its predecessors. There can be numerous reasons that make this programming language chosen by many developers – and much attention is paid to the availability of numerous libraries for various purposes. One of the major differences that Keras has when compared to other such libraries is the fact that it emphasises on usability according to the basic principles. First, Keras provides many options to choose from for deploying models in production, second, good performance is sup
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G.G.H.M.T.R., Bandara, and Siyambalapitiya R. "Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons." International Journal of Soft Computing and Engineering (IJSCE) 9, no. 6 (2020): 1–6. https://doi.org/10.35940/ijsce.E3357.039620.

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The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of
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P.G., Prof Patil. "Real Time Face Mask Detection with TensorFlow and Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30324.

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The COVID-19 pandemic has driven the development of real-time face mask detection systems. This project details a system built with TensorFlow and Python for this purpose. It involves three steps: data collection, model training, and real-time detection. First, a dataset of labeled images (masked/unmasked faces) is prepared. Then, a Convolutional Neural Network (CNN) is trained using TensorFlow and Keras to classify faces. Transfer learning can be used for improved performance. Finally, the trained model is integrated with OpenCV for real-time video processing. Faces are identified in each fra
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Yousef, Syfian. "Applying Keras-Based Deep Learning for Intelligent Analysis in Network Security and Monitoring Systems." Babylonian Journal of Networking 2025 (July 21, 2025): 106–15. https://doi.org/10.58496/bjn/2025/009.

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With the advent of digital age, network access and protection of sensitive data from unauthorized access or use has been a great challenge. Face detection and recognition is becoming a prevalent method in network security system by utilising the biometric principles. In this survey, we use Convolutional Neural Networks (CNNs) and the Keras deep learning framework to improve network security by building efficient face detection systems. A high-level and user-friendly API implemented by Keras (over TensorFlow), which makes it very easy to use deep learning models for tasks such as face detection
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Adelia, Risa, Nabila Khairunisa, and Reza Zulfiqri. "IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM MENDETEKSI SAMPAH ORGANIK, PLASTIK, DAN KERTAS." JUTIM (Jurnal Teknik Informatika Musirawas) 9, no. 1 (2024): 29–37. http://dx.doi.org/10.32767/jutim.v9i1.2233.

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Abstrak Sampah menjadi permasalahan yang sering ditemukan di berbagai negara. Dalam beberapa dekade terakhir, produksi sampah terus meningkat yang dapat menyebabkan masalah lingkungan yang semakin serius. Oleh karena itu, pengolahan sampah yang efektif dan tepat waktu sangat diperlukan untuk menjaga lingkungan hidup yang sehat. Dalam studi ini, kami mengusulkan pendekatan pengolahan sampah menggunakan Convolutional Neural Network (CNN) untuk mendeteksi jenis sampah organik, plastik, dan kertas berdasarkan gambar sebagai input untuk dilatih dengan model yang sudah disediakan. Dataset yang digun
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Dissertations / Theses on the topic "Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras"

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Halle, Alex, and Alexander Hasse. "Topologieoptimierung mittels Deep Learning." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.

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Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimieru
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Karlsson, David. "Ljudklassificering med Tensorflow och IOT-enheter : En teknisk studie." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39331.

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Artificial Inteligens and machine learning has started to get established as reco- gnizable terms to the general masses in their daily lives. Applications such as voice recognicion and image recognicion are used widely in mobile phones and autonomous systems such as self-drivning cars. This study examines how one can utilize this technique to classify sound as a complement to videosurveillan- ce in different settings, for example a busstation or other areas that might need monitoring. To be able to do this a technique called Convolution Neural Ne- twork has been used since this is a popular ar
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Hubík, Daniel. "Odhad kanálu v OFDM systémech pomocí deep learning metod." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400540.

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This paper describes a wireless communication model based on IEEE 802.11n. Typical methods for channel equalisation and estimation are described, such as the least squares method and the minimum mean square error method. Equalization based on deep learning was used as well. Coded and uncoded bit error rate was used as a performance identifier. Experiments with topology of the neural network has been performed. Programming languages such as MATLAB and Python were used in this work.
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Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.

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Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In s
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Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.

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Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the
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Serečunová, Stanislava. "Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377657.

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This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for segmentation of objects from the image. Practical part of the work was devoted to testing of an existing network architectures. For this purpose, an open-source software library Tensorflow, implemented in Python programming language, was used. A frequent problem incorporating the use of convolutional neural networks is the requirement on large amount of input data
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Третяк, Ігор Олегович. "Розробка системи комп'ютерного зору для розпізнавання емоцій". Магістерська робота, 2020. https://dspace.znu.edu.ua/jspui/handle/12345/1658.

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Третяк І. О. Розробка системи комп'ютерного зору для розпізнавання емоцій : кваліфікаційна робота магістра спеціальності 121 "Інженерія програмного забезпечення" / наук. керівник О. В. Кудін. Запоріжжя : ЗНУ, 2020. 52 с.<br>UA : Робота викладена на 52 сторінках друкованого тексту, містить 15 рисунків, 8 джерел, 1 додаток. Об’єкт дослідження: сучасні системи комп'ютерного зору. Мета роботи: розробка системи комп’ютерного зору для розпізнавання емоцій. Метод дослідження: аналітичний. У роботі досліджуються сучасні системи комп'ютерного зору. Розглядаються різні засоби та технології для вирішення
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Books on the topic "Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras"

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Python Deep Learning: Exploring Deep Learning Techniques and Neural Network Architectures with Pytorch, Keras, and TensorFlow. de Gruyter GmbH, Walter, 2019.

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Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow. 2nd ed. Packt Publishing, 2019.

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Verdhan, Vaibhav. Computer Vision Using Deep Learning: Neural Network Architectures with Python, Keras, and TensorFlow. Apress L. P., 2021.

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Silaparasetty, Vinita. Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras. Apress, 2020.

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Burns, Samuel. Python Deep learning: Develop your first Neural Network in Python Using TensorFlow, Keras, and PyTorch. Independently published, 2019.

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Panwar, Nitin. Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. de Gruyter GmbH, Walter, 2018.

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Bali, Raghav, Dipanjan Sarkar, and Tamoghna Ghosh. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, 2018.

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Wiley, Joshua F., Yuxi (Hayden) Liu, Pablo Maldonado, and Mark Hodnett. Deep Learning with R for Beginners: Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. Packt Publishing, Limited, 2019.

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Book chapters on the topic "Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras"

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Hyder, Mohd, and Gouri Mohd Nafees. "HARNESSING DEEP LEARNING AND NATURAL LANGUAGE PROCESSING (NLP)." In ENGINEERING THE FUTURE: MACHINE LEARNING AND DATA SCIENCE IN PRACTICE. NOBLE SCIENCE PRESS, 2023. http://dx.doi.org/10.52458/9789388996747.nsp2023.eb.ch-02.

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This chapter embarks on a journey through the dynamic landscapes of Deep Learning and Natural Language Processing (NLP) with a focus on the TensorFlow and Keras frameworks, as well as spaCy and NLTK libraries. In this chapter, we demystify the world of deep learning, exploring neural networks, activation functions, and model training. We introduce TensorFlow, a powerful open-source framework, and Keras, a user-friendly high-level API that simplifies deep neural network creation. Case studies illuminate deep learning's real-world impact, spanning image classification to healthcare applications.
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Karthikeyan, S., and A. Muthukumaravel. "Predictive Analytics in Adult Obesity Detection Through Deep Learning Methods." In Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9420-5.ch003.

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The global rise in obesity rates has necessitated the development of advanced predictive tools to combat this health crisis. This research explores using predictive analytics, particularly deep learning methods, to improve the early detection and prediction of adult obesity. Leveraging large datasets from sources such as the National Health and Nutrition Examination Survey (NHANES), this study integrates machine learning algorithms with health data to identify patterns that can predict obesity risk factors with high accuracy. The dataset includes diverse variables such as dietary habits, physi
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Husna, Asma, Saman Hassanzadeh Amin, and Bharat Shah. "Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods." In Advances in Logistics, Operations, and Management Science. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3805-0.ch005.

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Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long shor
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Thomas, J. Joshua, Lim Ting Wei, Y. Bevish Jinila, and R. Subhashini. "Smart Computerized Essay Scoring Using Deep Neural Networks for Universities and Institutions." In Handbook of Research on Smart Technology Models for Business and Industry. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3645-2.ch006.

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This chapter develops a web-based automated text scoring (ATS) system that can grade essays and check for spelling errors. The main reason behind this work is to alleviate the labour-intensive marking of essays and ensures equality in scoring for high-stakes exams like TOEFL. The researcher had performed a detailed investigation on deep learning techniques used in the field of ATS and developed a recurrent neural network model that can score essays in an end-to-end approach. Using the developed deep learning model, a web application was also developed to showcase the process of ATS by letting
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Banerjee, Indranil, and Dr Bijoy Kumar Mandal. "ROBUST FEATURE BASED CANCER CLASSIFICATION." In Futuristic Trends in Artificial Intelligence Volume 3 Book 11. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bkai11p4ch4.

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Brain cancer has a very short life expectancy. Accurately diagnosing brain cancer is an important step in developing an appropriate treatment plan for brain cancer treatment and rehabilitation. Computer-based cancer detection systems and convolutional neural networks have created success stories and machine learning-based disease detection has made significant progress. The present work fills this gap. Here, we used a statistics-based feature-extraction method and machine learning-based extraction method to distinguish cancer genes from non-cancer genes. Deep learning algorithms have also been
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Conference papers on the topic "Deep Learning Deep Neural Network ConvolutionNeuralNetwork Tensorflow Keras"

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Murali, Saritha, Vaishnavi Gupta, Soumyojyoti Saha, Harsh Sharan, Prajwal Sinha, and Kumar Baibhav. "Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574762.

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Wilkins, J. Wilkins, M. V. Nguyen Nguyen, and B. Rahmani Rahmani. "Image Processing Failure and Deep Learning Success in Lawn Measurement." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102001.

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Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, image processing and deep learning methods were used to find the best way to measure the lawn area. Three image processing methods using OpenCV compared to convolutional neural network, which is one of the most famous, and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional neural network or shortly CNN sh
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Nambiar, Sanjay, P. Arjun, Deepak R. Venkateswar, and M. Rajavel. "Weather Image Classification Using Convolution Neural Network." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-2kzgh5.

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A real-world weather prediction system that detects and describes weather condition in image data is becoming prominent subject in machine vision . These systems are designed to address the challenge of weather classification using machine vision. Advances in the fields of Artificial Intelligence and Machine Learning enables applications to take on the image recognition capabilities to identify the input image . Deep learning is a vast field and narrow focusing a bit and takes up the challenge of solving an Image Classification process. Proposed deep learning algorithms by tensorflow or keras
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Koffi, Itoro Udofort, and Aniefiok Livinus. "Prediction of Drift Velocity Closure Relationship in Multiphase Flow Models Using Deep Learning Approach." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/211926-ms.

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Abstract Multiphase flow modelling is of major importance in the design of pipelines, separation plants, and many other systems found in the chemical and petroleum industries. Many multiphase flow models apply a number of closure relationships; one of such is the drift velocity. Empirical correlations, with varying range of applicability and predictive capability, are typically relied upon by researchers to predict this parameter. This work therefore presents the development of a machine learning approach for predicting drift velocity in horizontal and non-horizontal pipelines. Python programm
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Koffi, Itoro Udofort. "A Deep Learning Approach for the Prediction of Oil Formation Volume Factor." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208627-stu.

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Abstract Accurate knowledge of Pressure-Volume-Temperature (PVT) properties is crucial in reservoir and production engineering computational applications. One of these properties is the oil formation volume factor (Bo), which assumes a significant role in calculating some of the prominent petroleum engineering terms and parameters, such as depletion rate, oil in place, reservoir simulation, material balance equation, well testing, reservoir production calculation, etc. These properties are ideally measured experimentally in the laboratory, based on downhole or recommended surface samples. Fast
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Bhowmik, Subrata. "Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31296-ms.

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Abstract Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In
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Eller, Harrison, Sanjana Singh, and Sumit Soni. "Predicting Gas Turbine NOx Emissions With Machine Learning." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-127627.

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Abstract Gas Turbine operations result in production of Nitrogen Oxides (NOx) and Carbon Monoxide (CO), both of which are pollutants that are to be strictly monitored and controlled. To control NOx within permissible limits, Combustion Emissions Monitoring System (CEMS) readings are to be continuously monitored and flagged for any anomaly in NOx emissions. This paper covers the method for developing a proof of concept of a Machine Learning (ML) model that predicts NOx from a selected pair of General Electric 7FA (7FA) and Siemens Westinghouse 501F (SW501F) gas turbines that are actively monito
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Loh, Arthur Chuan Xing, and Jun Kit Chaw. "Real-Time Human Facial Expression Recognition for Extended Software Usability Testing." In International Conference on Digital Transformation and Applications (ICDXA 2020). Tunku Abdul Rahman University College, 2020. http://dx.doi.org/10.56453/icdxa.2020.1023.

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The main objective of this project is to extend software usability testing through human facial expression recognition in software engineering. Measuring the satisfaction of software using questionnaires may be misleading due to the difficulties in expressing their satisfaction through natural language. Therefore, this project proposes to extend the usability testing with emotion recognition based on multimodal inputs by defining test scenarios with required emotional state distinction on the scenarios. This project is equipped with a real-time human expression recognition software which displ
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