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Journal articles on the topic 'Machine and deep learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Liu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.

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Gadri, Said. "Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 9–17. http://dx.doi.org/10.5373/jardcs/v12sp7/20202076.

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3

Poomka, Pumrapee, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 103–9. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1021.

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At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

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Fischer, Andreas M., Basel Yacoub, Rock H. Savage, John D. Martinez, Julian L. Wichmann, Pooyan Sahbaee, Sasa Grbic, Akos Varga-Szemes, and U. Joseph Schoepf. "Machine Learning/Deep Neuronal Network." Journal of Thoracic Imaging 35 (May 2020): S21—S27. http://dx.doi.org/10.1097/rti.0000000000000498.

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Wang, Tianlei, Jiuwen Cao, Xiaoping Lai, and Badong Chen. "Deep Weighted Extreme Learning Machine." Cognitive Computation 10, no. 6 (October 1, 2018): 890–907. http://dx.doi.org/10.1007/s12559-018-9602-9.

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Mishra, Chandrahas, and D. L. Gupta. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (June 1, 2017): 66. http://dx.doi.org/10.11591/ijai.v6.i2.pp66-73.

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Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.

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Rajendra Kumar, P., and E. B. K. Manash. "Deep learning: a branch of machine learning." Journal of Physics: Conference Series 1228 (May 2019): 012045. http://dx.doi.org/10.1088/1742-6596/1228/1/012045.

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Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.

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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

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Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.

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Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

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Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (September 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.

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Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

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Khan, U., K. Khan, F. Hassan, A. Siddiqui, and M. Afaq. "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines." Engineering, Technology & Applied Science Research 9, no. 4 (August 10, 2019): 4423–27. http://dx.doi.org/10.48084/etasr.2734.

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Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines.

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Nagata, Takeshi, and Daiki Hashimoto. "Visual Inspection by Deep Learning and Machine Learning." Journal of The Japan Institute of Electronics Packaging 23, no. 4 (July 1, 2020): 271–74. http://dx.doi.org/10.5104/jiep.23.271.

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NAKASHIMA, Tomoharu. "Machine Learning and Deep Learning: Introduction and Applications." Journal of the Society of Materials Science, Japan 69, no. 9 (September 15, 2020): 633–39. http://dx.doi.org/10.2472/jsms.69.633.

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Jamalpur, Bhavana, Seena Naik Korra, Vijaya Prakash Rajanala, E. Sudarshan, and Bonthala Prabhanjan Yadav. "Machine learning intersections and challenges in deep learning." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 022072. http://dx.doi.org/10.1088/1757-899x/981/2/022072.

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Xin, Yang, Lingshuang Kong, Zhi Liu, Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao, Haixia Hou, and Chunhua Wang. "Machine Learning and Deep Learning Methods for Cybersecurity." IEEE Access 6 (2018): 35365–81. http://dx.doi.org/10.1109/access.2018.2836950.

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17

Pehrson, Lea, Carsten Lauridsen, and Michael Nielsen. "Machine learning and deep learning applied in ultrasound." Ultraschall in der Medizin - European Journal of Ultrasound 39, no. 04 (August 2018): 379–81. http://dx.doi.org/10.1055/a-0642-9545.

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Sharma, Neha, Reecha Sharma, and Neeru Jindal. "Machine Learning and Deep Learning Applications-A Vision." Global Transitions Proceedings 2, no. 1 (June 2021): 24–28. http://dx.doi.org/10.1016/j.gltp.2021.01.004.

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Zainudin, Z., S. Hasan, S. M. Shamsuddin, and S. Argawal. "Stress Detection using Machine Learning and Deep Learning." Journal of Physics: Conference Series 1997, no. 1 (August 1, 2021): 012019. http://dx.doi.org/10.1088/1742-6596/1997/1/012019.

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20

Visweswaran, Shyam, Jason B. Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B. Taneja, Joel Welling, Kar-Hai Chu, Jaime E. Sidani, and Brian A. Primack. "Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study." Journal of Medical Internet Research 22, no. 8 (August 12, 2020): e17478. http://dx.doi.org/10.2196/17478.

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Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

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Benuwa, Ben Bright, Yong Zhao Zhan, Benjamin Ghansah, Dickson Keddy Wornyo, and Frank Banaseka Kataka. "A Review of Deep Machine Learning." International Journal of Engineering Research in Africa 24 (June 2016): 124–36. http://dx.doi.org/10.4028/www.scientific.net/jera.24.124.

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The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

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Eberhard, Matthias, and Hatem Alkadhi. "Machine Learning and Deep Neural Networks." Journal of Thoracic Imaging 35 (May 2020): S17—S20. http://dx.doi.org/10.1097/rti.0000000000000482.

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23

Ulyanov, Sergey, Andrey Filipyev, and Kirill Koshelev. "Recognition recipes with deep machine learning." System Analysis in Science and Education, no. 2 (2020) (June 30, 2020): 177–86. http://dx.doi.org/10.37005/2071-9612-2020-2-177-186.

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This article aims to reveal that deep machine learning algorithms can be applied in a variety of commercial companies in order to improve developing intelligent systems. The major task which would be discussedin the application of convolutional neural networks for recognizing recipes of products and providing the possibility of maintenance decision making in business processes. Besides algorithms, the problems of real projects like gathering and preprocessing data would be considered and possible solutions suggested.

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Jinqiang, Wang, Prabhat Basnet, and Shakil Mahtab. "Review of machine learning and deep learning application in mine microseismic event classification." Mining of Mineral Deposits 15, no. 1 (2021): 19–26. http://dx.doi.org/10.33271/mining15.01.019.

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Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst. Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning

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Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (October 27, 2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

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Mushtaq, Shiza, M. M. Manjurul Islam, and Muhammad Sohaib. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review." Energies 14, no. 16 (August 20, 2021): 5150. http://dx.doi.org/10.3390/en14165150.

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This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

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SEEGER, MATTHIAS. "GAUSSIAN PROCESSES FOR MACHINE LEARNING." International Journal of Neural Systems 14, no. 02 (April 2004): 69–106. http://dx.doi.org/10.1142/s0129065704001899.

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Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.

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Hu, Kai, Yaogen Li, Min Xia, Jiasheng Wu, Meixia Lu, Shuai Zhang, and Liguo Weng. "Federated Learning: A Distributed Shared Machine Learning Method." Complexity 2021 (August 30, 2021): 1–20. http://dx.doi.org/10.1155/2021/8261663.

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Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning.

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Serre, Thomas. "Deep Learning: The Good, the Bad, and the Ugly." Annual Review of Vision Science 5, no. 1 (September 15, 2019): 399–426. http://dx.doi.org/10.1146/annurev-vision-091718-014951.

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Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

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Sami, Khan Nasik, Zian Md Afique Amin, and Raini Hassan. "Waste Management Using Machine Learning and Deep Learning Algorithms." International Journal on Perceptive and Cognitive Computing 6, no. 2 (December 14, 2020): 97–106. http://dx.doi.org/10.31436/ijpcc.v6i2.165.

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Waste Management is one of the essential issues that the world is currently facing does not matter if the country is developed or under developing. The key issue in this waste segregation is that the trash bin at open spots gets flooded well ahead of time before the beginning of the following cleaning process. The isolation of waste is done by unskilled workers which is less effective, time-consuming, and not plausible because of a lot of waste. So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. The goal of this task is to gather a dataset and arrange it into six classes consisting of glass, paper, and metal, plastic, cardboard, and waste. The model that we have used are classification models. For our research we did comparisons between four algorithms, those are CNN, SVM, Random Forest, and Decision Tree. As our concern is a classification problem, we have used several machine learning and deep learning algorithm that best fits for classification solutions. For our model, CNN accomplished high characterization on accuracy around 90%, while SVM additionally indicated an excellent transformation to various kinds of waste which were 85%, and Random Forest and Decision Tree have accomplished 55% and 65% respectively

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Currie, Geoff. "Intelligent Imaging: Anatomy of Machine Learning and Deep Learning." Journal of Nuclear Medicine Technology 47, no. 4 (August 10, 2019): 273–81. http://dx.doi.org/10.2967/jnmt.119.232470.

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Prabhu, Sanjay P. "Ethical challenges of machine learning and deep learning algorithms." Lancet Oncology 20, no. 5 (May 2019): 621–22. http://dx.doi.org/10.1016/s1470-2045(19)30230-x.

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Majaj, Najib J., and Denis G. Pelli. "Deep learning—Using machine learning to study biological vision." Journal of Vision 18, no. 13 (December 3, 2018): 2. http://dx.doi.org/10.1167/18.13.2.

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Shukla, Rati, Vinod Kumar, Vikash Yadav, and Mayur Rahul. "Input Data Characterization Using Machine Learning and Deep Learning." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012012. http://dx.doi.org/10.1088/1757-899x/1022/1/012012.

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Anjum, Uzma. "Artificial Intelligence, Machine Learning and Deep Learning In Healthcare." Bioscience Biotechnology Research Communications 14, no. 7 (June 25, 2021): 144–48. http://dx.doi.org/10.21786/bbrc/14.7.36.

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G K, Krithika, Karthik S, Kowsalya R, Alfred Daniel J, and Sangeetha K. "Driver Alert System Using Deep Learning and Machine Learning." International Research Journal on Advanced Science Hub 3, Special Issue ICARD 3S (March 23, 2021): 120–23. http://dx.doi.org/10.47392/irjash.2021.078.

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Xu, Yayin, Ying Zhou, Przemyslaw Sekula, and Lieyun Ding. "Machine learning in construction: From shallow to deep learning." Developments in the Built Environment 6 (May 2021): 100045. http://dx.doi.org/10.1016/j.dibe.2021.100045.

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Liu, Yicen, Yu Lu, Xi Li, Wenxin Qiao, Zhiwei Li, and Donghao Zhao. "SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches." IEEE Communications Letters 25, no. 6 (June 2021): 1926–30. http://dx.doi.org/10.1109/lcomm.2021.3061991.

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Salman, Sara, and Jamila H. Soud. "Deep Learning Machine using Hierarchical Cluster Features." Al-Mustansiriyah Journal of Science 29, no. 3 (March 10, 2019): 82. http://dx.doi.org/10.23851/mjs.v29i3.625.

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Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.

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Eldin Mustafa Abdelaziz, Ala Adin Baha, Ka Fei Thang, and Jacqueline Lukose. "Electric Load Forecasting with Deep Machine Learning." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3404–9. http://dx.doi.org/10.1166/jctn.2019.8300.

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The most commonly used form of energy in houses, factories, buildings and agriculture is the electrical energy, however, in recent years, there has been an increase in electrical energy demand due to technology advancements and rise in population, therefore an appropriated forecasting system must be developed to predict these demands as accurately as possible. For this purpose, five models were selected, they are Bidirectional-Long Short Term Memory (Bi-LSTM), Feed Forward Neural Network (FFNN), Long Short Term Memory (LSTM), Nonlinear Auto Regressive network with eXogenous inputs (NARX) and Multiple Linear Regression (MLR). This paper will demonstrate the development of these selected models using MATLAB and an android mobile application, which is used to visualize and interact with the data. The performance of the selected models was evaluated by performing the Mean Absolute Percent Error (MAPE), the selected historical data used to perform the MAPE was obtained from Toronto, Canada and Tasmania, Australia, where the year 2006 until 2016 was used as training data and the year 2017 was used to test the MAPE of the historical data with the models’ data. It is observed that the NARX model had the least MAPE for both the regions resulting in 1.9% for Toronto, Canada and 2.9% for Tasmania, Australia. Google cloud is used as the IoT (Internet of Things) platform for NARX data model, the 2017 datasets is converted to JavaScript Object Notation (JSON) file using JavaScript programming language, for data visualization and analysis for the android mobile application.

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NAKATANI, Masayuki, Zeyuan SUN, and Yutaka UCHIMURA. "Intelligent Construction Machine by Deep Reinforcement Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—G03. http://dx.doi.org/10.1299/jsmermd.2017.2p2-g03.

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Van Ginneken, B. "ES01.03 Deep Machine Learning for Screening LDCT." Journal of Thoracic Oncology 13, no. 10 (October 2018): S190. http://dx.doi.org/10.1016/j.jtho.2018.08.020.

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Bozhkov, Lachezar, and Petia Georgieva. "Deep learning models for brain machine interfaces." Annals of Mathematics and Artificial Intelligence 88, no. 11-12 (October 2, 2019): 1175–90. http://dx.doi.org/10.1007/s10472-019-09668-0.

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Jiang, X. W., T. H. Yan, J. J. Zhu, B. He, W. H. Li, H. P. Du, and S. S. Sun. "Densely Connected Deep Extreme Learning Machine Algorithm." Cognitive Computation 12, no. 5 (August 8, 2020): 979–90. http://dx.doi.org/10.1007/s12559-020-09752-2.

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Tiwari, Tanya, Tanuj Tiwari, and Sanjay Tiwari. "How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?" International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (March 6, 2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i2.569.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

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Marks, Paul. "Deep learning speeds MRI scans." Communications of the ACM 64, no. 4 (April 2021): 12–14. http://dx.doi.org/10.1145/3449060.

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Espadoto, Mateus, Nina Sumiko Tomita Hirata, and Alexandru C. Telea. "Deep learning multidimensional projections." Information Visualization 19, no. 3 (May 18, 2020): 247–69. http://dx.doi.org/10.1177/1473871620909485.

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Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.

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Bilokon, O. S. "Application of deep learning technology for creating intellectual autonomous machines." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2020): 407–18. http://dx.doi.org/10.15407/pp2020.02-03.407.

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One of the most common tasks that arise in building intelligent machine vision systems for intellectually autonomous machines is the problems of classification and regression. Classification problems are used for the reflexive action of autonomous machines. Prediction tasks can be used to build machine vision systems to provide intelligent autonomous machines with environmental knowledge, which in turn is important for planned predictable movements. Defining a class of task instances is an important procedure for the effective design of deep learning systems. In this context, the possibility of using a multilayered neural network as a regressor to construct elementary functional mappings is explored for further prediction. The study outlines the peculiarities of functioning and configuration of a specialized robotics system, considered in this paper as an intelligent autonomous machine or physical agent, generates a set of data points for elementary functions, analytical modeling and modeling of training systems. Input graph was constructed, neural network architecture was defined, gradient descent algorithm was implemented, and output schedules were finally constructed: learning process, results prediction and comparative graph of predicted results superimposed on the input graph. As a result of the study, an assessment of the machine's intellectual ability to predict was made.

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Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

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Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy

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Bhattacharyya, Debnath. "COMPREHENSIVE ANALYSIS ON COMPARISON OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS ON CARDIAC ARREST." Journal of Medical pharmaceutical and allied sciences 10, no. 4 (August 15, 2021): 3125–31. http://dx.doi.org/10.22270/jmpas.v10i4.1395.

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Machine Learning is the technology of having machines to understand and behave as humans do. Refining their learning in supervised manner over time, by feeding them information and data in the form of experiences in the real world. Heart disease has a wide variety of consequences, varying from asymptomatically to extreme arrhythmias, and even premature cardiac failure. A comparative computational analysis was conducted on open-source datasets among the most frequently used classification algorithms in Machine Learning and Neural Networks by randomly splitting data in to test and training and an in-depth survey of feature selection is addressed. Our study further concentrates on working with massive datasets from prospective study.

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