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Journal articles on the topic 'Tensorflow I'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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

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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 mul

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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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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

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Vats, Navender, Md Abid Iqbal, and Suraj Aggarwal. "AUTONOMOUS CAR USING TENSORFLOW." Innovative Computing and Communication: An International Journal 1, no. 4 (2020): 17–19. https://doi.org/10.5281/zenodo.4743778.

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Autonomous car, as the name suggests, a car that can drive on its own is the very concept that is being discussed in the major machine learning concepts. The basic approach for these types of projects is based upon the machine learning methods. Now is the time for the big advancement in this sector and is being in the process too in many of the developed and the developing countries. The goal of our project was to basically train a deep network to understand the steering behavior while driving, to make a fully autonomous car on the stimulator but being on a very realistic approach. For the pur

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Kashyap, Anand Kumar, Himanshu Srivastava, Devanand Yadav, Abhishek Nishad, Shivam Verma, and Abhishek Shahi. "Object Detection Using Tensorflow Lite." International Journal of Research Publication and Reviews 4, no. 5 (2023): 3093–97. http://dx.doi.org/10.55248/gengpi.4.523.40694.

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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 fur

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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 i

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Ramchandani, Monica, Hrishikesh Khandare, Priyanshi Singh, et al. "Survey: Tensorflow in Machine Learning." Journal of Physics: Conference Series 2273, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2273/1/012008.

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Abstract TensorFlow(TF) is a large-scale ML system that can be used in many different situations. Data-flow graphs are used by TF to define operations, shared state, and computations that change that state. It distributes the nodes of a Data-flow graph across multiple machines in a cluster, as well as diverse processing units within a single system, such as multicore CPUs, general-purpose GPUs and Tensor Processing Units, which are custom-designed ASICs (TPUs). Developers may use TF to try out new training techniques and optimizations. It covers a broad applications range, which focus on infer

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Ramchandani, Monica, Hrishikesh Khandare, Priyanshi Singh, et al. "Survey: Tensorflow in Machine Learning." Journal of Physics: Conference Series 2273, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2273/1/012008.

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Abstract TensorFlow(TF) is a large-scale ML system that can be used in many different situations. Data-flow graphs are used by TF to define operations, shared state, and computations that change that state. It distributes the nodes of a Data-flow graph across multiple machines in a cluster, as well as diverse processing units within a single system, such as multicore CPUs, general-purpose GPUs and Tensor Processing Units, which are custom-designed ASICs (TPUs). Developers may use TF to try out new training techniques and optimizations. It covers a broad applications range, which focus on infer

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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. Keyw

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Ihsan, Mohamad, Ratih Kumalasari Niswatin, and Daniel Swanjaya. "DETEKSI EKSPRESI WAJAH MENGGUNAKAN TENSORFLOW." Joutica 6, no. 1 (2021): 428. http://dx.doi.org/10.30736/jti.v6i1.554.

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Ekspresi wajah adalah merupakan perubahan bentuk raut muka wajah dalam menanggapi keadaan perasaan, niat dan komunikasi sosial seseorang. Ekspresi wajah ini sangat bagus untukk di teliti karena merupakan alat komunikasi non verball yang biasa digunakan oleh manusia ‘untuk menggambarkan keadaan emosi atau perasaan dan untuk menyampaikan pesan sosial di kehidupan sehari-hari. Penelitian ini menggunakan machine learning open source library Tensorflow dengan mengguanakan metode Convolutional Neural Network (CNN) yang dirancang khusus untuk pengenalan dan menentukan klasifikasi terhadap 7 ekspresi

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K.V.N., RAJESH, RAMESH K.V.N, HYMAVATHI M, and REDDY K. SYAM SUNDAR. "DIGIT RECOGNITION USING TENSORFLOW TOOL." i-manager’s Journal on Pattern Recognition 4, no. 3 (2017): 27. http://dx.doi.org/10.26634/jpr.4.3.13887.

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Patil, Om. "Classification of Vegetables using TensorFlow." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 2926–34. http://dx.doi.org/10.22214/ijraset.2018.4488.

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Abadi, Martín. "TensorFlow: learning functions at scale." ACM SIGPLAN Notices 51, no. 9 (2016): 1. http://dx.doi.org/10.1145/3022670.2976746.

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Mahavarkar, Avinash, Ritika Kadwadkar, Sneha Maurya, and Smitha Raveendran. "Underwater Object Detection using Tensorflow." ITM Web of Conferences 32 (2020): 03037. http://dx.doi.org/10.1051/itmconf/20203203037.

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Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created

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Nisarga, N. "Human Activity Recognition Using Tensorflow." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 901–5. https://doi.org/10.22214/ijraset.2024.65920.

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Human Activity Recognition (HAR) has become a key focus in healthcare and machine learning, aiming to improve personal well-being and lifestyle management through sensor data. As individuals lead increasingly busy lives, continuous monitoring of their activities can provide valuable insights for health management. Despite advancements, identifying patterns in human activity remains a challenging task, especially with diverse sensor data sources. This paper explores various technical approaches for human activity recognition, focusing on the use of TensorFlow and Long Short-Term Memory (LSTM) n

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P, Darshan. "Satellite Image Classification using TensorFlow." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 614–18. http://dx.doi.org/10.22214/ijraset.2024.63096.

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Abstract: Satellite image classification plays a crucial role in various fields such as agriculture, urban planning, disaster management, and environmental monitoring. This paper presents a novel approach utilizing TensorFlow, a popular open-source machine learning framework, for satellite image classification. The proposed methodology leverages deep learning techniques to extract meaningful features from satellite images, enabling accurate classification into predefined categories. By harnessing the power of convolutional neural networks (CNNs) implemented in TensorFlow, this research aims to

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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 i

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Favour Uche Ojika, Wilfred Oseremen Owobu, Olumese Anthony Abieba, Oluwafunmilayo Janet Esan, Bright Chibunna Ubamadu, and Andrew Ifesinachi Daraojimba. "AI-Enhanced Knowledge Management Systems: A Framework for Improving Enterprise Search and Workflow Automation through NLP and TensorFlow." Computer Science & IT Research Journal 6, no. 3 (2025): 201–30. https://doi.org/10.51594/csitrj.v6i3.1884.

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In the era of digital transformation, organizations are increasingly adopting artificial intelligence (AI) to enhance knowledge management systems (KMS) and gain a competitive edge. This paper proposes a novel framework for AI-enhanced knowledge management that leverages Natural Language Processing (NLP) and TensorFlow to improve enterprise search capabilities and workflow automation. Traditional KMS often struggle with unstructured data, inefficient information retrieval, and fragmented workflows, leading to reduced productivity and decision-making inefficiencies. By integrating advanced NLP

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Druzhynin, О. О., V. V. Nekhai, and O. A. Prila. "Facebook text posts classification with TensorFlow." Mathematical machines and systems 3 (2019): 47–54. http://dx.doi.org/10.34121/1028-9763-2019-3-47-54.

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Kirana, Kartika Candra, and Salah Abdullah Khalil Abdulrahman. "Random Multi-Augmentation to Improve TensorFlow-Based Vehicle Plate Detection." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 2 (2024): 113–25. https://doi.org/10.12928/biste.v6i2.10542.

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In the development of the "Machine Learning" education kit, vehicle plate recognition was created using TensorFlow with SSD MobileNetV2. The detection failure rate in the training process with varying distances and lighting from the camera is high if the training data is insufficient. Addressing that notable gap in research, we proposed Random Multi-Augmentation to Improve TensorFlow-Based Vehicle Plate Detection. Augmentation techniques are expected to train data that is manipulated at varying lighting and distance. The proposed method consists of two combining augmentation approaches, namely

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Das, Rajat Suvra. "TensorFlow: Revolutionizing Large-Scale Machine Learning in Complex Semiconductor Design." International Journal of Computing and Engineering 5, no. 3 (2024): 1–9. http://dx.doi.org/10.47941/ijce.1812.

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The development of semiconductor manufacturing processes is becoming more intricate in order to meet the constantly growing need for affordable and speedy computing devices with greater memory capacity. This calls for the inclusion of innovative manufacturing techniques hardware components, advanced intricate assemblies and. Tensorflow emerges as a powerful technology that comprehensively addresses these aspects of ML systems. With its rapid growth, TensorFlow finds application in various domains, including the design of intricate semiconductors. While TensorFlow is primarily known for ML, it

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Vogelsang, David C., and Bradley J. Erickson. "Magician’s Corner: 6. TensorFlow and TensorBoard." Radiology: Artificial Intelligence 2, no. 3 (2020): e200012. http://dx.doi.org/10.1148/ryai.2020200012.

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Rampasek, Ladislav, and Anna Goldenberg. "TensorFlow: Biology’s Gateway to Deep Learning?" Cell Systems 2, no. 1 (2016): 12–14. http://dx.doi.org/10.1016/j.cels.2016.01.009.

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Xolabyev, Anvar Muhiddinovich. "TENSORFLOW-SUNI'Y INTELLEKT PROYEKTLARI UCHUN PLATFORMA." Zamonaviy dunyoda innovatsion tadqiqotlar 2, no. 17 (2023): 94–96. https://doi.org/10.5281/zenodo.7972639.

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Sun’iy intelekt va raqamlashtirish hozirgi kunda jadal rivojlanib bormoqda, va bu texnologiyalarda o’z tasdig’ini topmoqda. Ushbu maqola sun’iy intelektning qanchalik dasturlash jarayonlarini yengillashtirish mumkinligi yoritilgan va bunda foydalanilgan kutubxona keltirilgan.  

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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 th

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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 transf

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Setiaji, Arif Pami. "Emotional Classification Based on Facial Expression Recognition Using Convolutional Neural Network Method." Asian Journal of Social and Humanities 1, no. 12 (2023): 1224–40. http://dx.doi.org/10.59888/ajosh.v1i12.139.

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In recent years, the development of human-computer interaction technology has reached remarkable levels, particularly in the field of facial expression recognition. This technology utilizes human facial images to identify and classify emotional expressions such as happiness, sadness, fear, and more through computer image processing. Active research in facial expression recognition yields substantial benefits for individual and societal advancement, especially in the context of its application within Smart City environments. This study demonstrates that well- configured Convolutional Neural Net

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Theeracheep, Siraphob, and Jaruloj Chongstitvatana. "Multiplication of medium-density matrices using TensorFlow on multicore CPUs." Tehnički glasnik 13, no. 4 (2019): 286–90. http://dx.doi.org/10.31803/tg-20191104183930.

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Matrix multiplication is an essential part of many applications, such as linear algebra, image processing and machine learning. One platform used in such applications is TensorFlow, which is a machine learning library whose structure is based on dataflow programming paradigm. In this work, a method for multiplication of medium-density matrices on multicore CPUs using TensorFlow platform is proposed. This method, called tbt_matmul, utilizes TensorFlow built-in methods tf.matmul and tf.sparse_matmul. By partitioning each input matrix into four smaller sub-matrices, called tiles, and applying an

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Aysha, Ms. "Vehicle Detection and Traffic Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1791–94. http://dx.doi.org/10.22214/ijraset.2021.38269.

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Abstract: On the road, vehicle detection processes are utilized for vehicle tracking, vehicle counting, vehicle speed, and traffic analysis. For vehicle detection, the Tensorflow object detection API method is employed. The Object Detection API in Tensorflow is a powerful tool that allows anyone to easily design and deploy effective picture recognition applications. Another way to control traffic is to use a traffic control system. Multiple linear regression is utilized to regulate the traffic system, while the OpenCV approach is used to identify vehicle speed. A system for fine payment is als

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Nugroho, Mursyid Sinung, and Eddy Nurraharjo. "Klasifikasi Hama Tanaman Padi berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network." BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains 6, no. 2 (2023): 672–82. http://dx.doi.org/10.31539/bioedusains.v6i2.8080.

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This research aims to classify the types of pests on rice leaves using Tensorflow with the CNN method to make it easier for the public to know the types of pests that exist on rice plants. The research results showed that initially the accuracy was only 61% but was successfully increased to 99% through various variation tests. Accuracy is affected by the background of the object and the distance of the device. Results show this model provides accurate predictions, with an average accuracy of around 90%. In conclusion, the results of rice plant pest classification using the CNN model for detect

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Bhide, Abhishek, Dnyaneshvar Ghodake, Ashish Jamle, Salman Shaikh, and Prof S. R. Bhujbal. "Predictive Machine Maintenance Using Tiny ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4252–55. http://dx.doi.org/10.22214/ijraset.2023.51254.

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Abstract: Anomaly detection (AD) is detection of pattern in data in expected behaviour. In an industrial environment, any equipment and system that breaks down are affecting productivity. Therefore, Tiny Machine Learning (Tiny ML) is introduced to address this problem. Tiny ML undergo anomaly detection to detect if any equipment did not act expected behaviour and notify the user if an anomaly detection has been detected. Anomaly detection is an unsupervised learning algorithm. It has aim to identify the patterns of data that do not follow the expected behaviour. Tiny Machine Learning (Tiny), a

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Abirami, K. Rama, M. ManojKumar, Mohammed Insaf, and Naveen Sakthivel. "Deep learning based Food Recognition using Tensorflow." Journal of Physics: Conference Series 1916, no. 1 (2021): 012149. http://dx.doi.org/10.1088/1742-6596/1916/1/012149.

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Xia, Xiao-Ling, Cui Xu, and Bing Nan. "Facial Expression Recognition Based on TensorFlow Platform." ITM Web of Conferences 12 (2017): 01005. http://dx.doi.org/10.1051/itmconf/20171201005.

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Cabañas, Rafael, Antonio Salmerón, and Andrés R. Masegosa. "InferPy: Probabilistic modeling with Tensorflow made easy." Knowledge-Based Systems 168 (March 2019): 25–27. http://dx.doi.org/10.1016/j.knosys.2018.12.030.

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Janardhanan, PS. "Project repositories for machine learning with TensorFlow." Procedia Computer Science 171 (2020): 188–96. http://dx.doi.org/10.1016/j.procs.2020.04.020.

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Hao, Liyang, Siqi Liang, Jinmian Ye, and Zenglin Xu. "TensorD: A tensor decomposition library in TensorFlow." Neurocomputing 318 (November 2018): 196–200. http://dx.doi.org/10.1016/j.neucom.2018.08.055.

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Li, Bryan, Alexander Cowen-Rivers, Piotr Kozakowski, et al. "RL: Generic reinforcement learning codebase in TensorFlow." Journal of Open Source Software 4, no. 42 (2019): 1524. http://dx.doi.org/10.21105/joss.01524.

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Saxena, Abhineet. "Convolutional neural networks: an illustration in TensorFlow." XRDS: Crossroads, The ACM Magazine for Students 22, no. 4 (2016): 56–58. http://dx.doi.org/10.1145/2951024.

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Zhang, Zhongheng, Lei Mo, Chen Huang, and Ping Xu. "Binary logistic regression modeling with TensorFlow™." Annals of Translational Medicine 7, no. 20 (2019): 591. http://dx.doi.org/10.21037/atm.2019.09.125.

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AlMo’men Bellah Alawnah and Ola Hayajnah. "Coins multi-class classification using vision TensorFlow." International Journal of Science and Research Archive 14, no. 3 (2025): 1017–25. https://doi.org/10.30574/ijsra.2025.14.3.0777.

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Coin classification is challenging but crucial for various applications such as vending machines, cash registers, and self-service kiosks. Coins are prevalent daily in banks, grocery stores, malls, supermarkets, and ATMs. Therefore, it is essential to have the capability to recognize coins with high accuracy automatically. Deep learning image processing models have recently shown promise in resolving the coin classification problem. These models can learn to identify and classify coins based on visual features such as shape, size, and texture. However, it is not easy as many coins appear simil

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Li, Yifan. "Handwritten Digit Recognition Based on TensorFlow Framework." Applied and Computational Engineering 157, no. 1 (2025): 172–78. https://doi.org/10.54254/2755-2721/2025.po24694.

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This study investigates the widespread application of convolutional neural networks (CNNs) in the field of handwritten digit recognition. To address the challenge of style variability in handwritten digit datasets, an efficient recognition model is proposed. The model is developed using the TensorFlow framework and enhances feature extraction and classification performance through the integration of convolutional layers, pooling layers, ReLU activation functions, batch normalization, and dropout techniques. The MNIST dataset is employed for experimentation. The model is trained using the Adam

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Et.al, Vijay Shanker Pandey. "Judgement Prediction of Government Employee’s Retirement Benefits Cases using TensorFlow Decision Forests." Journal of Electrical Systems 20, no. 10s (2024): 6055–67. http://dx.doi.org/10.52783/jes.6549.

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A judgement prediction model with TensorFlow Decision Forests (DF) is a machine learning model that uses decision trees as the building blocks for classification and forecasting of writ petition. The TensorFlow library provides tools for implementing and training decision forests, which are collections of decision trees, in order to make predictions. In this paper we analyze the basic description of the retirement benefits matters like grant of pension on superannuation, family pension, commutation of pension, gratuity, group insurance saving fund, leave encashment and superannuation, providen

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Kyu Park, Yong, Kyung Shin Kim, Jang Il Kim, Sung Hee Kim, and Kil Hung Lee. "A proposals of convolution neural network system for malicious code analysis based on cloud systems." International Journal of Engineering & Technology 7, no. 2.12 (2018): 80. http://dx.doi.org/10.14419/ijet.v7i2.12.11040.

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Background/Objectives: In the information security field, artificial intelligence must be applied first. This is because the frequency of malicious code is too high and the processing method is too difficult, which is very difficult for human to handle.Methods/Statistical analysis: In this paper, we developed a program to classify malicious codes into images and a Tensorflow system to classify malicious codes. The malware used as input was the computer virus code used in the BIG 2015 Challenge. This dataset, called a Kaggle dataset, consists of 10,868 bytes of train set.Findings: We used the T

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Munjal, Rohan, Sohaib Arif, Frank Wendler, and Olfa Kanoun. "Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins." Sensors 22, no. 4 (2022): 1312. http://dx.doi.org/10.3390/s22041312.

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A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A

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Мінухін, С. В. "Дослідження моделі сегментації зображень з використанням розподілених режимів TensorFlow та згорткової нейронної мережі U-Net". Системи обробки інформації, № 1(160), (30 березня 2020): 115–22. http://dx.doi.org/10.30748/soi.2020.160.15.

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Розглянуто модель сегментації медичних зображень з використанням різних розподілених режимів бібліотеки машинного навчання TensorFlow та згорткові мережі U-Net. Проведено аналіз можливостей розподілених обчислень для їх використання в TensorFlow. Описана побудована архітектура кластера робочих станцій та послідовність кроків для проведення експериментальних досліджень. Отримано оцінки втрат при навчанні у вигляді коефіцієнта Дайса, які свідчать про переваги використання синхронного режиму розподіленого навчання та в умовах масштабованості розгорнутого кластера.

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Salunkhe, Akilesh, Manthan Raut, Shayantan Santra, and Sumedha Bhagwat. "Android-based object recognition application for visually impaired." ITM Web of Conferences 40 (2021): 03001. http://dx.doi.org/10.1051/itmconf/20214003001.

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Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision has allowed the development of various real-time object detection applications. This paper describes a simple android app that would help the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone’s camera where real-time object recognition through tensorflow’s object detection API was done. The detected objects were then converted into an audio output by using android’s text-t

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Shin, Dong-Jin, and Jeong-Joon Kim. "A Deep Learning Framework Performance Evaluation to Use YOLO in Nvidia Jetson Platform." Applied Sciences 12, no. 8 (2022): 3734. http://dx.doi.org/10.3390/app12083734.

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Deep learning-based object detection technology can efficiently infer results by utilizing graphics processing units (GPU). However, when using general deep learning frameworks in embedded systems and mobile devices, processing functionality is limited. This allows deep learning frameworks such as TensorFlow-Lite (TF-Lite) and TensorRT (TRT) to be optimized for different hardware. Therefore, this paper introduces a performance inference method that fuses the Jetson monitoring tool with TensorFlow and TRT source code on the Nvidia Jetson AGX Xavier platform. In addition, central processing unit

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Venkat, Anand, Tharindu Rusira, Raj Barik, Mary Hall, and Leonard Truong. "SWIRL: High-performance many-core CPU code generation for deep neural networks." International Journal of High Performance Computing Applications 33, no. 6 (2019): 1275–89. http://dx.doi.org/10.1177/1094342019866247.

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Deep neural networks (DNNs) have demonstrated effectiveness in many domains including object recognition, speech recognition, natural language processing, and health care. Typically, the computations involved in DNN training and inferencing are time consuming and require efficient implementations. Existing frameworks such as TensorFlow, Theano, Torch, Cognitive Tool Kit (CNTK), and Caffe enable Graphics Processing Unit (GPUs) as the status quo devices for DNN execution, leaving Central Processing Unit (CPUs) behind. Moreover, existing frameworks forgo or limit cross layer optimization opportun

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