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Journal articles 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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1

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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Et.al, Anuraag Velamati. "Traffic Sign Classification Using Convolutional Neural Networks and Computer Vision." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 4244–50. http://dx.doi.org/10.17762/turcomat.v12i3.1715.

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The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Ne

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Anusha, Palagati, Biruduraju Naganjali, B. Adikeshava Reddy, A. Varun Reddy, and Emuka Sreenija. "Customer Churn Prediction in Telecom: A Deep Learning Approach Using Keras and TensorFlow." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 126–33. https://doi.org/10.47001/irjiet/2025.inspire21.

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For telecom companies, customer attrition is a major problem that has a direct impact on retention and revenue. In order to predict churn and take quick retention actions based on customer history, a deep learning model built on Keras and TensorFlow is used for this project. Deep learning enhances the ability to identify intricate data associations when compared to more conventional techniques like logistic regression and decision trees. Data collection, preprocessing, training, and model evaluation are all part of the project. Tenure, charges, and demographics of customers are included in a p

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Abha. "Performance Analysis of Sign Language Detection Using Deep Neural Networks and Computer Vision." AlQalam Journal of Medical and Applied Sciences 3, no. 2 (2020): 78–81. https://doi.org/10.5281/zenodo.4059765.

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This paper related to the Method of Training a Deep Learning Model and how we have used it for the American Sign Language detection. We have trained a Convolution Neural Nets (CNN) using Keras and TensorFlow as a backend. There is multiple image manipulation done in between using Computer Vision like resizing, thresholding, RGB2GRAY and the most important is histogram analysis which helps to identify the difference in background and image. The main aim of this project is to track the gestures made by the hand in American Sign Language and translate it into English. The entire project has been

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Eliackim, MUHOZA, and Musoni Wilson Dr. "Efficacy of Algorithms in Deep Learning on Brain Tumor Cancer Detection (Topic Area: Deep Learning)." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 1887–94. https://doi.org/10.5281/zenodo.7716372.

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In today's world, manually examining a large number of MRI (magnetic resonance imaging)images and detecting a brain tumor is a time-consuming and incorrect task. It may have an impact on the patient's medical therapy. It might be a time-consuming task because to the large amount of image data sets involved. Because normal tissue and brain tumor cells have a lot in common in terms of appearance, segmenting tumor regions can be difficult. As a result, a highly accurate automatic tumor detection approach is required. In this study, I useda convolutional neural network to segregate brain t

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Talekar, Publisher: P. R. "Fake Instagram Profile Detection Using Feedforward Neural Network." International Journal of Advance and Applied Research 5, no. 8 (2024): 24–28. https://doi.org/10.5281/zenodo.11161740.

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<strong>Abstract:<em> </em></strong> The project aimed to develop a robust fake account detection system for social media platforms, particularly Instagram, utilising deep learning techniques. Leveraging a dataset consisting of various features such as profile picture presence, username characteristics, and other relevant attributes, the model was trained to discern between genuine and fake accounts. The dataset underwent thorough exploratory data analysis, including visualisations to gain insights into feature distributions and correlations. The preprocessing phase involved standardisation of

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Benbrahim, Houssam, Hanaâ Hachimi, and Aouatif Amine. "Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images." Scalable Computing: Practice and Experience 21, no. 3 (2020): 379–90. http://dx.doi.org/10.12694/scpe.v21i3.1725.

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Skin cancer is a dangerous disease causing a high proportion of deaths around the world. Any diagnosis of cancer begins with a careful clinical examination, followed by a blood test and medical imaging examinations. Medical imaging is today one of the main tools for diagnosing cancers. It allows us to obtain precise images, internal organs and thus to visualize the possible tumours that they present. These images provide information on the location, size and evolutionary stage of tumour lesions. Automatic classification of skin tumours using images is an important task that can help doctors, l

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Kim, Jong-Min, Jihun Kim, and Il Do Ha. "Application of Deep Learning and Neural Network to Speeding Ticket and Insurance Claim Count Data." Axioms 11, no. 6 (2022): 280. http://dx.doi.org/10.3390/axioms11060280.

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With the popularity of big data analysis with insurance claim count data, diverse regression models for count response variable have been developed. However, there is a multicollinearlity issue with multivariate input variables to the count response regression models. Recently, deep learning and neural network models for count response have been proposed, and a Keras and Tensorflow-based deep learning model has been also proposed. To apply the deep learning and neural network models to non-normal insurance claim count data, we perform the root mean square error accuracy comparison of gradient

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Vélez, Sergio, José Antonio Rubio, Rubén Vacas, and Enrique Barajas. "Digital ampelography: deep learning (CNN) using Keras to identify grapevine cultivars." Acta Horticulturae, no. 1390 (March 7, 2024): 311–20. https://doi.org/10.17660/ActaHortic.2024.1390.38.

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Ampelography is the science that studies the identification and classification of grapevines (Vitis). It is a laborious science, and it is carried out manually through visual surveys usually performed by agronomists, which requires a huge amount of time. Image processing and computer vision based on machine learning methods can enable agronomists to minimise the time spent on cultivar identification. Convolutional neural networks (CNN) could be employed for this task since they can efficiently learn increasingly complex visual concepts by identifying spatial hierarchies of patterns and reproce

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Kholil, Moch, Heri Priya Waspada, and Rafika Akhsani. "Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network." SINTECH (Science and Information Technology) Journal 5, no. 2 (2022): 198–204. http://dx.doi.org/10.31598/sintechjournal.v5i2.1179.

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Convolutional Neural Network (CNN) is one of the Deep Learning methods that is able to carry out an independent learning process that is popular and appropriate in classifying. The development of technology in the field of Deep Learning, this study aims to assist farmers in identifying the types of infectious diseases that attack chickens based on faecal images using Convolutional Neural Network (CNN) so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bact

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Pokhrel, Nawa Raj, Keshab Raj Dahal, Ramchandra Rimal, Hum Nath Bhandari, and Binod Rimal. "Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models." Software 3, no. 1 (2024): 47–61. http://dx.doi.org/10.3390/software3010003.

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Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in ingPython 3.12. The framework aligns with the modular engineering principles for the design and development strategy. Transparency, reproducibility, and recombinability are the framework’s primary design criteria. The platform can extract valuable insights from numerical and text data and utilize them to predict future values by implementing long short-term memory (LSTM), gated recurrent unit (GRU), and convolution neural network (CNN). Its end-to-end machine learning pipeline involves a sequence of tasks, including

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Klęsk, Przemysław. "Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch." Applied Sciences 14, no. 21 (2024): 9972. http://dx.doi.org/10.3390/app14219972.

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Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scr

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Jaiswal, Aryaman. "Cricket Score Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1149–57. https://doi.org/10.22214/ijraset.2025.73175.

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Cricket is a sport characterized by its wealth of data and intricacies, making outcome prediction a fascinating challenge for analysts, broadcasters, and fans alike. The emergence of T20 leagues, particularly the Indian Premier League (IPL), has significantly increased the demand for advanced, real-time analytical tools. Traditional score prediction methods in cricket often depend on fixed metrics like average run rates, which do not adequately reflect the game's dynamic nature. This project introduces a deep learning approach to forecast the final score of a team batting first in a T20 match,

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Surenthiran, Krishnan, Magalingam Pritheega, and Ibrahim Roslina. "Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5467–76. https://doi.org/10.11591/ijece.v11i6.pp5467-5476.

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This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accur

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Singari, Ranganath, Karun Singla, and Gangesh Chawla. "Deep Learning Framework for Steel Surface Defects Classification." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 4, no. 1 (2019): 25–32. http://dx.doi.org/10.35121/ijapie201901135.

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Deep learning has offered new avenues in the field of industrial management. Traditional methods of quality inspection such as Acceptance Sampling relies on a probabilistic measure derived from inspecting a sample of finished products. Evaluating a fixed number of products to derive the quality level for the complete batch is not a robust approach. Visual inspection solutions based on deep learning can be employed in the large manufacturing units to improve the quality inspection units for steel surface defect detection. This leads to optimization of the human capital due to reduction in manua

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Amsaveni, Avinashiappan, Thiagarajan Harshavarthan, Coimbatore Mahesh Harshwarth, and Suresh Rohith. "Smart surveillance using deep learning." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (2023): 423–32. https://doi.org/10.11591/ijres.v12.i3pp423-432.

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Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The Ke

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R, Ms Likitha, Ms Harshitha P, and Ms Rashmi M. "Classification and Detection of Chicken Disease Using CNN with Image Classification Technique." International Journal of Engineering Research and Applications 14, no. 6 (2024): 126–29. http://dx.doi.org/10.9790/9622-1406126129.

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Artificial intelligence technology in deep learning is one of the popular classification methods. The development of deep learning technology is expected to assist farmers in identifying the types of infectious diseases that attack chickens based on feces images so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chick

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Avinashiappan, Amsaveni, Harshavarthan Thiagarajan, Harshwarth Coimbatore Mahesh, and Rohith Suresh. "Smart surveillance using deep learning." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (2023): 423. http://dx.doi.org/10.11591/ijres.v12.i3.pp423-432.

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<span>Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others.

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398–405. https://doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human-computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preproce

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Zhou, Yongzhang, Jun Wang, Renguang Zuo, Fan Xiao, Wenjie Shen, and Shugong Wang. "Machine Learning, Deep Learning and Implementation Language in Geological Field." Journal of Autonomous Intelligence 4, no. 1 (2021): 6. http://dx.doi.org/10.32629/jai.v4i1.479.

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<p class="15" align="justify">Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot of geological big data research. It will make geological big data winged and change geology. Machine learning is a training process of model derived from data, and it eventually gives a decision oriented to a certain performance measurement. De

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Pandav, Khushali, and Neramaye N. Deshpande. "Train Your Own Neural Network for Facial Expression Recognition Using TensorFlow, CNN and Keras." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem37411.

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Facial expression recognition is one of the most important aspects of human-computer interaction, based on which the system needs to perceive and respond to the emotions expressed by a human. This paper presents a facial expression recognition system that makes use of the convolutional neural network, using TensorFlow and Keras for its implementation. It categories emotions included in the FER2013 dataset consisting of 48x48 pixel grayscale images labeled with seven emotional categories: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. It basically comprises steps like image preprocess

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Sawwashere, Dr Supriya S., and Mr Aman Jambhulkara. "Detection of Breast Cancer Cells Using Deep Learning with Convolution Neural Network." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1970–73. http://dx.doi.org/10.22214/ijraset.2024.60193.

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Abstract: Breast cancer, a pervasive global health concern, necessitates timely and precise diagnosis to ensure effective treatment and enhance patient outcomes. This comprehensive review critically examines a Convolutional Neural Network (CNN) model specifically crafted for the detection of breast cancer utilizing histopathological images. The Python code, developed with the powerful TensorFlow and Keras libraries, strategically incorporates advanced methodologies such as transfer learning and data augmentation to optimize the model's diagnostic performance. The study meticulously delves into

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Patria, Harry. "Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning)." Data Science: Journal of Computing and Applied Informatics 6, no. 2 (2022): 67–80. http://dx.doi.org/10.32734/jocai.v6.i2-8309.

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The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers with a low frequency of previous fraud. With the data imbalance, traditional machine learningis likely t

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Jamshed, Muhammad Ammar. "Analyze Soil Fertility using Deep Learning Convolutional Neural Networks." Shanlax International Journal of Arts, Science and Humanities 10, no. 3 (2023): 1–5. http://dx.doi.org/10.34293/sijash.v10i3.5281.

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This research revolves around how plant soil potential can be further discovered and used for farming through detection of relevant nutrients and chemicals within the soil landscapes within areas and even desert climates and how we can improve land soil fertility of the purpose of farming both using Convolutional neural networks which process of imagery in layers and predictive detections of objects within image backgrounds and frontal lobes. When we view layers for farming beneath the surface to understand suitability of farming done on top. The general model applied can be summarized as foll

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Hussein Hadi, Teeb. "Deep Learning-based DDoS Detection in Network Traffic Data." International journal of electrical and computer engineering systems 15, no. 5 (2024): 407–14. http://dx.doi.org/10.32985/ijeces.15.5.3.

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In today's society, the cloud is essential for communication since it allows access to important information anytime and anywhere. However, cloud services also attract hackers who want to exploit online details. This has caused significant changes in the cyber-attack landscape. Distributed Denial of Service (DDoS) is the most common attack. Traditional tools like firewalls and encryption can mitigate these risks, but new models are needed to cope with the changing nature of cyber-attacks. Detecting DDoS attacks is particularly challenging since network traffic data is complex and often contain

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Omankwu, Obinnaya Chinecherem &. Ubah Valetine Ifeanyi. "Hybrid Deep Learning Model for Heart Disease Prediction Using Recurrent Neural Network (RNN)." NIPES Journal of Science and Technology Research 5, no. 2 (2023): 184–94. https://doi.org/10.5281/zenodo.8014330.

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<em>In</em><em> this paper, we use a recurrent neural network (RNN) that combines multiple gated recurrent units (GRUs), long short-term memory (LSTM), and the Adam optimizer to develop a new hybrid deep learning model for heart disease prediction. This proposed model yielded an excellent accuracy of 98.6876%. This proposed model is a hybrid of GRUs and RNNs model. The model was developed in Python 3.7 by integrating multiple GRUs and RNNs working with Keras and Tensorflow as backends for the deep learning process and is supported by various Python libraries. A recent existing model using RNN

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Krishnan, Surenthiran, Pritheega Magalingam, and Roslina Ibrahim. "Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5467. http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476.

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<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reac

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Khan, Md, Ishrat Khan, Md Bag, Machbah Uddin, Md Hassan, and Jayedul Hassan. "Deep learning-based bacterial genus identification." Journal of Advanced Veterinary and Animal Research 9, no. 4 (2022): 573. http://dx.doi.org/10.5455/javar.2022.i626.

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Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Cory

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Hu, Zicheng, Alice Tang, Jaiveer Singh, Sanchita Bhattacharya, and Atul J. Butte. "A robust and interpretable end-to-end deep learning model for cytometry data." Proceedings of the National Academy of Sciences 117, no. 35 (2020): 21373–80. http://dx.doi.org/10.1073/pnas.2003026117.

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Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, all

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Raja, Ronish. "Plant Disease Detection System Using Convolutional Neural Networks and TensorFlow Lite." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 725–33. https://doi.org/10.22214/ijraset.2025.69515.

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This research presents an intelligent system for the classification of plant diseases using a convolutional neural network (CNN) trained on a large dataset of diseased and healthy plant leaves. The model was developed using Python and deep learning libraries such as TensorFlow and Keras, achieving high accuracy in classifying various plant diseases. The trained model is integrated into a user-friendly web application using Streamlit, enabling real-time predictions from uploaded images. The system provides an accessible interface for farmers, researchers, and agricultural workers to detect plan

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Avani Fatesinhbhai Chaudhari, Avani Fatesinhbhai Chaudhari, and Priyanka Sharma Priyanka Sharma. "Real-Time Facial Expression Detection System." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 137–41. https://doi.org/10.35629/5252-0706137141.

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Real-time Facial Expression Detection system using Artificial Intelligence and Deep Learning. The project combined theoretical concepts with practical implementation, focusing on deep learning. A Convolutional Neural Network (CNN) was trained on the FER2013 dataset to classify seven emotions: Happy, Sad, Angry, Disgusted, Fearful, Surprised, and Neutral. Both a custom CNN and VGG16 (via transfer learning) were used to compare performance and training efficiency.[9] Technologies like Python, TensorFlow, Keras, and OpenCV were used. Key features included real-time webcam integration, face detect

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Oise, Godfrey, and Susan Konyeha. "E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH." FUDMA JOURNAL OF SCIENCES 8, no. 3 (2024): 17–24. http://dx.doi.org/10.33003/fjs-2024-0804-2579.

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The goal of this research is to improve the management of electronic trash (e-waste) by using a Sequential Neural Network (SNN) with TensorFlow and Keras as part of an advanced deep learning technique. In order to address the growing problem of e-waste, the research collects a large amount of data from images of e-waste and then carefully preprocesses and augments those images. With precision, recall, and F1 scores of 87%, 86%, and 86%, respectively, the SNN architecture—which incorporates dropout, pooling, and convolutional layers—achieved an amazing 100% classification accuracy. These outsta

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Kossman, Stephania, and Maxence Bigerelle. "Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms." Materials 14, no. 22 (2021): 7027. http://dx.doi.org/10.3390/ma14227027.

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High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves

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G, Kavyasri, Keerthana D, Keerthi Reddy B, Keerthi K, KesavaAditya J, and Prof S. Ramesh Kumar. "Deep Learning based Credit Card Fraudulency Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 477–81. http://dx.doi.org/10.22214/ijraset.2024.61553.

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Abstract: Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like ecommerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e- commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neu

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Smaida, Mahmoud, Serhii Yaroshchak, and Ahmed Y. Ben Sasi. "Learning Rate Optimization in CNN for Accurate Ophthalmic Classification." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (2021): 211–16. http://dx.doi.org/10.35940/ijitee.b8259.0210421.

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One of the most important hyper-parameters for model training and generalization is the learning rate. Recently, many research studies have shown that optimizing the learning rate schedule is very useful for training deep neural networks to get accurate and efficient results. In this paper, different learning rate schedules using some comprehensive optimization techniques have been compared in order to measure the accuracy of a convolutional neural network CNN model to classify four ophthalmic conditions. In this work, a deep learning CNN based on Keras and TensorFlow has been deployed using P

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Mahmoud, Smaida*, Yaroshchak Serhii, and Y. Ben Sasi Ahmed. "Learning Rate Optimization in CNN for Accurate Ophthalmic Classification." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 4 (2021): 211–16. https://doi.org/10.35940/ijitee.B8259.0210421.

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One of the most important hyper-parameters for model training and generalization is the learning rate. Recently, many research studies have shown that optimizing the learning rate schedule is very useful for training deep neural networks to get accurate and efficient results. In this paper, different learning rate schedules using some comprehensive optimization techniques have been compared in order to measure the accuracy of a convolutional neural network CNN model to classify four ophthalmic conditions. In this work, a deep learning CNN based on Keras and TensorFlow has been deployed using P

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Albertsson, Kim, Sitong An, Sergei Gleyzer, et al. "Machine Learning with ROOT/TMVA." EPJ Web of Conferences 245 (2020): 06019. http://dx.doi.org/10.1051/epjconf/202024506019.

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ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for c

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Liu, Nan. "Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching." Mathematical Problems in Engineering 2022 (August 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/1002105.

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As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolut

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Patil, Prof Deepika P., Girija Varma, Shweta Poojary, Shraddha Sawant, and Aditya Sharma. "Counterfeit Currency Detection based on AI." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 3022–27. http://dx.doi.org/10.22214/ijraset.2022.41980.

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Abstract: The use of technology has grown tremendously within the few years it has made it easier to have access to advanced printing equipment in the industry which resulted in color printing of currencies to produce counterfeit notes across the country. To eliminate such unethical activities of printing counterfeit currency it is mandatory to make a system that detects the fake currency, In systems such as a money exchanger for example ATMs and vending machines, counterfeit currency notes must be detected beforehand exchanging process takes place. In the past, there have been similar systems

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Abdelaziz, Ahmed, and Alia N. Mahmoud. "Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion." Fusion: Practice and Applications 8, no. 1 (2022): 08–15. http://dx.doi.org/10.54216/fpa.080201.

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Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed

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