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Littérature scientifique sur le sujet « TensorFlow model »

Auteur : Grafiati
Publié le 6 juin 2025
Mis à jour le 12 juin 2025

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Articles de revues sur le sujet "TensorFlow model"

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Jaiswal, Gourav. "Stock Prediction Model Using TensorFlow." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 99–103. http://dx.doi.org/10.22214/ijraset.2021.39207.

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Abstract: In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keywords: Stock Prediction, Machine Learning, Data Visualization, Yahoo Finance Dataset
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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 frame, and the model predicts if a mask is worn. This information can be used for alerts or notifications, promoting mask compliance in public spaces like airports, schools, and public transport. This versatile system offers applications in public health and security, leveraging TensorFlow's deep learning and Python's real-time processing capabilities for disease control and public safety. Key Words: Real-time face mask detection, Convolutional Neural Network (CNN), TensorFlow & Keras, Transfer learning
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Yegane Aliyeva, Goncha Mammadova, Yegane Aliyeva, Goncha Mammadova. "REVOLUTIONIZING HEALTHCARE WITH DEEP LEARNING: APPLICATIONS OF TENSORFLOW IN DIGITAL MEDICINE." ETM - Equipment, Technologies, Materials 28, no. 04 (2025): 12–17. https://doi.org/10.36962/etm28042025-12.

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This paper explores the transformative impact of TensorFlow, a deep learning framework, on the evolving field of digital medicine. The integration of artificial intelligence (AI) and machine learning (ML) into healthcare has enabled the development of advanced diagnostic tools, automated clinical workflows, and personalized treatment plans. TensorFlow, developed by Google, provides scalable solutions for building complex neural networks, particularly for image analysis, electronic health records (EHRs), genetic research, and speech-based diagnostics. This study presents a detailed examination of TensorFlow’s core features, followed by a case study on dermatological disease diagnosis using convolutional neural networks (CNNs). Furthermore, the paper outlines existing challenges, including data privacy concerns and model interpretability, while highlighting future directions for the integration of AI in digital healthcare systems. By leveraging TensorFlow, healthcare professionals can enhance early detection capabilities, optimize resource allocation, and improve patient outcomes globally. Keywords: Digital medicine, TensorFlow, deep learning, healthcare AI, medical imaging, electronic health records, convolutional neural networks.
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Pang, Bo, Erik Nijkamp, and Ying Nian Wu. "Deep Learning With TensorFlow: A Review." Journal of Educational and Behavioral Statistics 45, no. 2 (2019): 227–48. http://dx.doi.org/10.3102/1076998619872761.

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This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.
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Shaikh, Kashif, Dnyaneshwar Pawar, Gaurav Patil, Rahul Patil, and Prof Hemant Wani. "Image Classification by Tensorflow." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1373–78. http://dx.doi.org/10.22214/ijraset.2023.53817.

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Abstract: The task of relating what an image represents is called image bracket. An image bracket model is trained to fete colorful classes of images. For illustration, you may train a model to fete prints representing three different types of creatures rabbits, pussycats, and tykes . But in our design we've used the datasets of cotton splint which helps us to find the complaint on the cotton splint as well. Tensorflow is an google open source machine literacy frame for dataflow programming across a range of task. As well as it's a open source library for deep literacy operation. It's firstly developed for large numerical calculation without keeping deep literacy in mind. In the design of image bracket, one model was erected to sort images. By using the model which was erected in the design, filmland can be classified effectively and snappily. At the morning of the design, an applicable data set was chosen. also, the model was created by using TensorFlow. Next, the model would be trained to get the parameters with good fitting. Eventually, in order to estimate the model effectively, several graphs of confirmation delicacy were created. In the process of completing this design, the members of Team have learned the capability to construct convolutional neural network models using python. What’s more, the members of the platoon also develop a good capability of data analysis.
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Kumar, Sandeep, Rajeev Ratan, and J. V. Desai. "Cotton Disease Detection Using TensorFlow Machine Learning Technique." Advances in Multimedia 2022 (August 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/1812025.

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Agriculture is a main source of income for farmers in India. Farmers produce many seasonal local crops based on their location. Cotton is the most produced crop across India. Cotton is a commercial crop, and farmers get good capital from cotton. This will increase the income of the farmer. However, one of the basic problems with cotton is that it is easily exposed to many diseases. These diseases need to be identified as early as possible to avoid production loss. In this paper, the CNN algorithm is used to create the prediction model by leveraging the TensorFlow’s Keras API. This model is further used in mobile app development which helps the farmers identify cotton disease and recommend the pesticides which can be used to overcome the disease. The TensorFlow open-source platform was used to prepare the ML model. The TensorFlow Tflite model is created, and after that, the model is converted into the Core ML model, which is used in iOS app to make the disease predication. Google’s core API is used to convert the TensorFlow model into the Core ML model. The label dataset was used to create the model. The Swift language is used in app development. The model accuracy was around 90%. Currently, boll rot and fungal leafspot disease are detected in this app. However, the app can be further extended for other cotton diseases too.
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Xu, Wencai. "Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow." Journal of Physics: Conference Series 2066, no. 1 (2021): 012070. http://dx.doi.org/10.1088/1742-6596/2066/1/012070.

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Abstract Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.
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Xu, Wencai. "The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism." Journal of Physics: Conference Series 2066, no. 1 (2021): 012002. http://dx.doi.org/10.1088/1742-6596/2066/1/012002.

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Abstract With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.
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Chovanec, Martin, Martin Hasin, Martin Havrilla, and Eva Chovancová. "Detection of HTTP DDoS Attacks Using NFStream and TensorFlow." Applied Sciences 13, no. 11 (2023): 6671. http://dx.doi.org/10.3390/app13116671.

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This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.
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Gao, Jiyang. "Facial Expression Recognition Based on TensorFlow." Advances in Engineering Technology Research 13, no. 1 (2025): 886. https://doi.org/10.56028/aetr.13.1.886.2025.

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Facial expression recognition is an important research direction in the field of computer vision, which has a wide range of application potential, including human-computer interaction, emotional computing, security monitoring, and so on. In this study, a facial expression recognition method based on the TensorFlow framework is proposed, which uses a convolutional neural network (CNN) to automatically extract facial features and classify emotions. By training with the FER2013 dataset, we construct a multi-layer convolutional neural network model and use data enhancement technology to improve the generalization ability of the model. Experimental results show that the proposed method can effectively identify seven basic emotions (happiness, sadness, anger, disgust, surprise, fear, and neutrality), and the classification accuracy on the FER2013 dataset reaches 70%, which is superior to other traditional facial expression recognition methods. Through the analysis of the confusion matrix and classification report, we find that the model is confused in some emotional categories (such as fear and sadness), and the accuracy and robustness can be further improved by optimizing the model structure or introducing stronger regularization methods in future work.
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Thèses sur le sujet "TensorFlow model"

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Ferm, Oliwer. "Real-time Object Detection on Raspberry Pi 4 : Fine-tuning a SSD model using Tensorflow and Web Scraping." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39455.

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Edge AI is a growing area. The use of deep learning on low cost machines, such as the Raspberry Pi, may be used more than ever due to the easy use, availability, and high performance. A quantized pretrained SSD object detection model was deployed to a Raspberry Pi 4 B to evaluate if the throughput is sufficient for doing real-time object recognition. With input size of 300x300, an inference time of 185 ms was obtained. This is an improvement as of the previous model; Raspberry Pi 3 B+, 238 ms with a input size of 96x96 which was obtained in a related study. Using a lightweight model is for the benefit of higher throughput as a trade-off for lower accuracy. To compensate for the loss of accuracy, using transfer learning and tensorflow, a custom object detection model has been trained by fine-tuning a pretrained SSD model. The fine-tuned model was trained on images scraped from the web with people in winter landscape. The pretrained model was trained to detect different objects, including people in various environments. Predictions shows that the custom model performs significantly better doing detections on people in snow. The conclusion from this is that web scraping can be used for fine-tuning a model. However, the images scraped is of bad quality and therefore it is important to thoroughly clean and select which images that is suitable to keep, given a specific application.<br>Användning av djupinlärning på lågkostnadsmaskiner, som Raspberry Pi, kan idag mer än någonsin användas på grund av enkel användning, tillgänglighet, och hög prestanda. En kvantiserad förtränad SSD-objektdetekteringsmodell har implementerats på en Raspberry Pi 4 B för att utvärdera om genomströmningen är tillräcklig för att utföra realtidsobjektigenkänning. Med en ingångsupplösning på 300x300 pixlar erhölls en periodtid på 185 ms. Detta är en stor förbättring med avseende på prestanda jämfört med den tidigare modellen; Raspberry Pi 3 B+, 238 ms med en ingångsupplösning på 96x96 som erhölls i en relaterad studie. Att använda en kvantiserad modell till förmån för hög genomströmning bidrar till lägre noggrannhet. För att kompensera för förlusten av noggrannhet har, med hjälp av överföringsinlärning och Tensorflow, en skräddarsydd modell tränats genom att finjustera en färdigtränad SSD-modell. Den finjusterade modellen tränas på bilder som skrapats från webben med människor i vinterlandskap. Den förtränade modellen var tränad att känna igen olika typer av objekt, inklusive människor i olika miljöer. Förutsägelser visar att den skräddarsydda modellen detekterar människor med bättre precision än den ursprungliga. Slutsatsen härifrån är att webbskrapning kan användas för att finjustera en modell. Skrapade bilder är emellertid av dålig kvalitet och därför är det viktigt att rengöra all data noggrant och välja vilka bilder som är lämpliga att behålla gällande en specifik applikation.
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Javed, Muhammad Haseeb. "Characterizing and Accelerating Deep Learning and Stream Processing Workloads using Roofline Trajectories." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574445196024129.

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Awan, Ammar Ahmad. "Co-designing Communication Middleware and Deep Learning Frameworks for High-Performance DNN Training on HPC Systems." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587433770960088.

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Kudláč, Vladan. "Modul pro výuku výslovnosti cizích jazyků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445584.

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Cílem této práce je vylepšit implementaci modulu pro mobilní aplikace pro výuku výslovnosti, najít místa vhodná pro optimalizaci a provést optimalizaci s cílem zvýšit přesnost, snížit čas zpracování a snížit paměťovou náročnost zpracování.
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Štarha, Dominik. "Meření podobnosti obrazů s pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377018.

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This master´s thesis deals with the reseach of technologies using deep learning method, being able to use when processing image data. Specific focus of the work is to evaluate the suitability and effectiveness of deep learning when comparing two image input data. The first – theoretical – part consists of the introduction to neural networks and deep learning. Also, it contains a description of available methods, their benefits and principles, used for processing image data. The second - practical - part of the thesis contains a proposal a appropriate model of Siamese networks to solve the problem of comparing two input image data and evaluating their similarity. The output of this work is an evaluation of several possible model configurations and highlighting the best-performing model parameters.
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Hettiarachchi, Salinda. "Analysis of different face detection andrecognition models for Android." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42446.

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Human key point tracking such as face detection and recognition has become an increasingly popular research topic. It is a platform independent functionality and already being implemented on a wide range of platforms. Android is one such platform that runs on mobile phones and top of many edge devices such as car devices and smart home appliances. In the current times, AI and ML related applications are slightly moving into those edge devices due to various reasons such as security and low latency. The hardware enhancements are also backing this trend that happened over the last few years. Many solutions and algorithms have been proposed in this context, and various frameworks and models have also been developed. Even though there are different models available, they tend to deliver varying results in terms of performance. Evaluating these different alternatives to find an optimized solution is a problem worth addressing. In this thesis project, several selected face detection and recognition models have been implemented in an Android device, and their performance been evaluated. Google ML Kit showed the best results among the face detection methods since it took only around 68 milliseconds on average to detect a face. Out of the three face recognition algorithms evaluated, FaceNet was the most accurate as it showed an accuracy above 95% for most cases. Meanwhile, MobileFaceNet was the fastest algorithm, and it took only around 90 milliseconds on average to produce and output. Eventually, a face recognition application was also developed using the best performing models selected from the experiment.
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Álvarez, Robles Enrique Josué. "Supervised Learning models with ice hockey data." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167718.

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The technology developments of the last years allow measuring data in almost every field and area nowadays, especially increasing the potential for analytics in branches in which not much analytics have been done due to complicated data access before. The increased number of interest in sports analytics is highly connected to the better technology now available for visual and physical sensors on the one hand and sports as upcoming economic topic holding potentially large revenues and therefore investing interest on the other hand. With the underlying database, precise strategies and individual performance improvements within the field of professional sports are no longer a question of (coach)experience but can be derived from models with statistical accuracy. This thesis aims to evaluate if the available data together with complex and simple supervised machine learning models could generalize from the training data to unseen situations by evaluating performance metrics. Data from games of the ice hockey team of Linköping for the season 2017/2018 is processed with supervised learning algorithms such as binary logistic regression and neural networks. The result of this first step is to determine the strategies of passes by considering both, attempted but failed and successful shots on goals during the game. For that, the original, raw data set was aggregated to game-specific data. After having detected the distinct strategies, they are classified due to their rate of success.
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Pandiscia, Nicola. "Analisi di sequenze video per rilevazioni demografiche ed emotive da software su microcontroller." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il seguente progetto è volto ad implementare sul microcontroller "Raspberry Pi v.4 Model B" un software utilizzante a mo' di scatola nera, anche, in parte, una rete neurale che sulla base di una classificazione precedentemente realizzata da soggetti terzi e sulla base di opportuni modelli preaddestrati sfrutti un meccanismo di apprendimento supervisionato per stimare ragionevolmente, secondo opportuni criteri, il sesso, la fascia d'età, lo stato emotivo (caricaturale, ossia forzato) e la distanza approssimativa di uno o più soggetti ripresi frontalmente in volto da una telecamera in tempo reale.
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Boscarino, Andrea. "Deep Learning Models with Stochastic Targets: an Application for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20078/.

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Il presente elaborato di tesi è parte di un ampio progetto finanziato dall’Unione Europea, sotto il programma Horizon 2020 per la ricerca e l’innovazione, Open Transprecision Computing (OPRECOMP). Il progetto, della durata di 4 anni, punta a superare l’assunto conservativo secondo cui ogni calcolo compiuto da sistemi e applicazioni computazionali debba essere eseguito utilizzando la massima precisione numerica. Tale assunto è finora risultato sensato in vista di un’efficienza computazionale sempre migliore col passare del tempo, secondo la legge di Moore. Com’è noto, nell’era attuale tale legge ha iniziato a perdere di validità con l’approssimarsi dei limiti fisici che impediscono ulteriori miglioramenti di grande ordine previsti di anno in anno, dando piuttosto spazio a miglioramenti marginali. L’approccio proposto dal progetto OPRECOMP (il cui sviluppo vuole beneficiare applicazioni che spaziano dai piccoli nodi computazionali per l’Internet-of-Things, fino ai centri computazionali di High Performance Computing) è basato sul paradigma del Transprecision Computing, che supera l’assunto della massima precisione in favore di calcoli approssimati; tramite tale paradigma si arriva ad un doppio vantaggio: computazioni più efficienti e brevi, e soprattutto, risparmio energetico. Per fare ciò, OPRECOMP sfrutta il principio secondo cui quasi ogni applicazione computazionale utilizza nodi intermedi di calcolo, le cui precisioni possono essere tarate (in modo controllato) con conseguenze minime sull’affidabilità del risultato finale. All'interno dell'elaborato vengono esplorate soluzioni e metodologie di machine learning (e in particolare modelli stocastici, ovvero distribuzioni probabilistiche caratterizzate da errore medio e varianza) con lo scopo di apprendere la relazione che incorre tra la scelta del numero di bit utilizzati per le variabili di alcuni benchmark matematici e il relativo errore rilevato rispetto alla stessa computazione eseguita a precisione massima.
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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.
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Livres sur le sujet "TensorFlow model"

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Karunanidhi, Vikraman. Deploying TensorFlow Models to a Web Application. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6699-1.

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Paper, David. State-of-the-Art Deep Learning Models in TensorFlow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8.

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Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow. O'Reilly Media, Incorporated, 2020.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow. Apress, 2019.

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Tung, K. C. TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models. O'Reilly Media, Incorporated, 2021.

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Singh, Pramod, and Avinash Manure. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python. Apress, 2020.

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Singh, Pramod, and Avinash Manure. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python. Apress, 2020.

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Capelo, Luis. Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications. Packt Publishing, 2018.

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Ahirwar, Kailash. Generative Adversarial Networks Projects: Build Next-Generation Generative Models Using TensorFlow and Keras. Packt Publishing, Limited, 2019.

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Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Empirical Work. Apress L. P., 2020.

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Chapitres de livres sur le sujet "TensorFlow model"

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Radziukas, Raulis, Rytis Maskeliūnas, and Robertas Damaševičius. "Prediction of Poker Moves Using Sequential Model and TensorFlow." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30275-7_40.

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Tyagi, Shobha, Pronika Chawla, Nilesh Barat, Mohit, Prem Kumar, and Bandaru Sai Karthik. "Smart home security using TensorFlow, HOG and SVM model." In Progressive Computational Intelligence, Information Technology and Networking. CRC Press, 2025. https://doi.org/10.1201/9781003650010-115.

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Fradj, Walid Ben, Mohamed Turki, and Faiez Gargouri. "Deep Learning Based on TensorFlow and Keras for Predictive Monitoring of Business Process Execution Delays." In Model and Data Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49333-1_12.

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Tolba, Zakaria, and Makhlouf Derdour. "Deep Neural Network Based TensorFlow Model for IoT Lightweight Cipher Attack." In Artificial Intelligence and Its Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96311-8_11.

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Jothilakshmi, P., C. Gomatheeswari Preethika, and R. Mohanasundaram. "Design of Smart Weed Detection and Evacuation Robot Using TensorFlow Model Maker." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6550-2_35.

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Guan, Ji, Wang Fang, and Mingsheng Ying. "Verifying Fairness in Quantum Machine Learning." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_20.

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AbstractDue to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure—Tensor Networks—and implemented on Google’s TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($$2^{27}$$ 2 27 -dimensional state space) tripling ($$2^{18}$$ 2 18 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
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Paper, David. "TensorFlow Datasets." In State-of-the-Art Deep Learning Models in TensorFlow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8_3.

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Stein, Peter, Jibinraj Antony, Simon Bergweiler, and Christian Schorr. "Generalized Authoring Tool for Computer Vision Machine Learning Application Deployments." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_10.

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Abstract Automated authoring enables simplified deployment of applications and services for complex use cases, especially in the field of machine learning. This paper presents the development and implementation of a specialized authoring tool that can be used for computer vision applications, enabling automated creation of machine learning services. The proposed authoring tool realizes a microservices architecture to facilitate the conversion and deployment of machine learning inference services, especially in image classification and object detection use cases. The authoring process addresses the interoperability issues commonly faced in machine learning frameworks, leveraging the Open Neural Network Exchange (ONNX) for model conversion into a standardized format. By encapsulating machine learning tools in containerized applications, this authoring tool offers a modular solution that can be easily adapted to various industrial applications. The developed authoring tool integrates the common machine learning frameworks PyTorch and TensorFlow, coupling DevOps methodologies such as CI/CD, ensuring a robust, maintainable, and user-friendly system that meets the growing needs of machine learning use cases in manufacturing.
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Singh, Pramod, and Avinash Manure. "TensorFlow Models in Production." In Learn TensorFlow 2.0. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5558-2_6.

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Paper, David. "Build TensorFlow Input Pipelines." In State-of-the-Art Deep Learning Models in TensorFlow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8_1.

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Actes de conférences sur le sujet "TensorFlow model"

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Ahmed, Rameel, Noman Shabbir, Muhammad Wasif Raza, Ayesha Zeb, and Hassan Elahi. "Evaluation of Model Degradation in PaddleOCR, UltOCR, and TrOCR Across Baseline and TensorFlow Lite Environments." In 2024 International Conference on Robotics and Automation in Industry (ICRAI). IEEE, 2024. https://doi.org/10.1109/icrai62391.2024.10894257.

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Firmansyah, Teguh, Amelia Nur Safitri, Dina Estiningtyas Lufianawati, and Irma Saraswati. "Machine Learning Model Based on Universal Sentence Encoder and TensorFlow for Matching Algorithm on Collabolio Collaborative Platfrom." In 2024 International Conference on Informatics Electrical and Electronics (ICIEE). IEEE, 2024. https://doi.org/10.1109/iciee63403.2024.10920398.

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Trinanda Putra, Karisma, Hasan Zidni, Rifdha Turrizka, Hsueh-Ting Chu, Dinh-Trung Vu, and Prayitno. "An 8-bit Quantized Globalization Model for Cat Skin Disease Detection Using Convolutional Neural Networks and TensorFlow Lite." In 2024 International Conference on Information Technology and Computing (ICITCOM). IEEE, 2024. https://doi.org/10.1109/icitcom62788.2024.10762204.

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Jain, Eshika, Sanyam Kapoor, and Manpreet Singh. "Advanced Chicken Disease Detection with Keras and TensorFlow Deep Learning Models." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760413.

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Mulla, Rahesha, Suvarna Joshi, Aditya Devchakke, Aditya Dawda, and Avadhoot Durgude. "Enhancing American Sign Language Recognition: A Comparative Study of LSTM and TensorFlow Zoo Models for Static and Non-Static Gesture Detection." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985235.

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Abadi, Martín, Michael Isard, and Derek G. Murray. "A computational model for TensorFlow: an introduction." In PLDI '17: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2017. http://dx.doi.org/10.1145/3088525.3088527.

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Hasabnis, Niranjan. "Auto-Tuning TensorFlow Threading Model for CPU Backend." In 2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC). IEEE, 2018. http://dx.doi.org/10.1109/mlhpc.2018.8638636.

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Bhat, Guru Prasad, and Nagaraj G. Cholli. "Effective object detection using Tensorflow facilitated YOLOv3 model." In 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2021. http://dx.doi.org/10.1109/csitss54238.2021.9683109.

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Bira, Calin, and Valentin-Gabriel Voiculescu. "TL-TensorFlow CNN model and dataset for electronic equipment." In Advanced Topics in Optoelectronics, Microelectronics and Nanotechnologies 2020, edited by Marian Vladescu, Ionica Cristea, and Razvan D. Tamas. SPIE, 2020. http://dx.doi.org/10.1117/12.2572157.

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Feng, Sen. "Handwritten Digital Detection Based on Tensorflow Building SSD Model." In 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE). IEEE, 2019. http://dx.doi.org/10.1109/iciase45644.2019.9074012.

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Rapports d'organisations sur le sujet "TensorFlow model"

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LaRocca, Stephen, and Gerardo Cervantes. Extending OpenNMT's TensorFlow Lite to Include Transformer Models. DEVCOM Army Research Laboratory, 2021. http://dx.doi.org/10.21236/ad1144269.

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