Relevant bibliographies by topics / Neural data processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural data processing'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Neural data processing"

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Blank, T. B., and S. D. Brown. "Data processing using neural networks." Analytica Chimica Acta 277, no. 2 (1993): 273–87. http://dx.doi.org/10.1016/0003-2670(93)80440-v.

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Majoros, Tamás, Balázs Ujvári, and Stefan Oniga. "EEG data processing with neural network." Carpathian Journal of Electronic and Computer Engineering 12, no. 2 (2019): 33–36. http://dx.doi.org/10.2478/cjece-2019-0014.

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Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.
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Worswick, Steven G., James A. Spencer, Gunnar Jeschke, and Ilya Kuprov. "Deep neural network processing of DEER data." Science Advances 4, no. 8 (2018): eaat5218. http://dx.doi.org/10.1126/sciadv.aat5218.

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Prodan, Roman, Denys Shutka, and Vasyl Tataryn. "PREVENTING POTENTIAL ROBBERY CRIMES USING DEEP LEARNING ALGORITHM OF DATA PROCESSING." Measuring Equipment and Metrology 84, no. 3 (2023): 16–22. http://dx.doi.org/10.23939/istcmtm2023.03.016.

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Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics. With the increasing number of robbed apartments and houses ev
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Xu, Yingying, Zhi Liu, Yujun Li, et al. "Feature data processing: Making medical data fit deep neural networks." Future Generation Computer Systems 109 (August 2020): 149–57. http://dx.doi.org/10.1016/j.future.2020.02.034.

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Apiecionek, Łukasz. "Fully Scalable Fuzzy Neural Network for Data Processing." Sensors 24, no. 16 (2024): 5169. http://dx.doi.org/10.3390/s24165169.

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The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks
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Hrusha, Volodymyr. "Intelligent Processing of Data From Chlorophyll Fluorometric Sensors." Cybernetics and Computer Technologies, no. 1 (June 30, 2022): 42–48. http://dx.doi.org/10.34229/2707-451x.22.1.5.

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Introduction. Chlorophyll fluorescence induction (CFI) is a monitoring method of plant objects. CFI is a radiation of chlorophyll in red spectrum during a chlorophyll lighting of alive plant in blue spectrum. Chlorophyll fluorometers – the special devices that are used for measurement of CFI. Series of such devices were developed in V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine. In particular, fluorometer «Floratest» and a network of wireless sensors were developed for CFI measurement. An accumulation of massive amount of measurements resulted into possibility to use intellectua
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Vladislav, Goncharenko, Durdymyradov Kerven, and Parrondo Isaac. "EEG DATA PROCESSING FOR BRAIN COMPUTER INTERFACE." International Journal of Multidisciplinary Research Transactions 5, no. 4 (2023): 79–80. https://doi.org/10.5281/zenodo.7779282.

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Stauder, Ralf, Daniel Ostler, Thomas Vogel, et al. "Surgical data processing for smart intraoperative assistance systems." Innovative Surgical Sciences 2, no. 3 (2017): 145–52. http://dx.doi.org/10.1515/iss-2017-0035.

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AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional ne
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Ashida, Yuzuru. "Data processing of reflection seismic data by use of neural network." Journal of Applied Geophysics 35, no. 2-3 (1996): 89–98. http://dx.doi.org/10.1016/0926-9851(96)00010-9.

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Dissertations / Theses on the topic "Neural data processing"

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Begum, Rushna. "Neural network processing of impact echo NDT data." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340456.

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Kirkin, S., and K. V. Melnyk. "Intelligent Data Processing in Creating Targeted Advertising." Thesis, National Technical University "Kharkiv Polytechnic Institute", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/44710.

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Hoya, Tetsuya. "Graph theoretic methods for data partitioning." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286542.

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Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Alahakoon, Lakpriya Damminda 1968. "Data mining with structure adapting neural networks." Monash University, School of Computer Science and Software Engineering, 2000. http://arrow.monash.edu.au/hdl/1959.1/7987.

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Olivier, Hannelore. "Musical networks : the case for a neural network methodology in advertisement music research." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/16618.

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Thesis (M.Mus.)--University of Stellenbosch, 2005.<br>ENGLISH ABSTRACT: Countless scientists had been struggling for centuries to find a significant connection between cognition, emotion and reasoning – resulting in today’s rather embarrassingly imperfect understanding of even the most basic human cognition. We should apprehend that it is unlikely that major breakthroughs in the Cognitive Sciences, Psychology, Sociology or the Medical Sciences will elucidate everything about the human brain and -behaviour in the very near future. Realizing this, it is realistic that we should transfer our
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Kurniawan, Budi. "Offline writer identification system using multiple neural networks." Phd thesis, Department of Electrical Engineering, 1998. http://hdl.handle.net/2123/9392.

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Bakhary, Norhisham. "Structural condition monitoring and damage identification with artificial neural network." University of Western Australia. School of Civil and Resource Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0102.

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Many methods have been developed and studied to detect damage through the change of dynamic response of a structure. Due to its capability to recognize pattern and to correlate non-linear and non-unique problem, Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. Most successful works reported in the application of ANN for damage detection are limited to numerical examples and small controlled experimental examples only. This is because of the two main constraints for its practical application in det
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Trigueiros, Duarte. "Neural network based methods in the extraction of knowledge from accounting and financial data." Thesis, University of East Anglia, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292217.

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Phillips, Peter. "A novel pre-processing method for the classification of data by a neural network." Thesis, University of Sussex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398348.

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Books on the topic "Neural data processing"

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M, Poulton Mary, ed. Computational neural networks for geophysical data processing. Pergamon, 2001.

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Bart, Kosko, ed. Neural networks for signal processing. Prentice Hall, 1992.

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S, Govindaraju Rao, and Rao A. Ramachandra 1939-, eds. Artificial neural networks in hydrology. Kluwer Academic Publishers, 2000.

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Rzempoluck, Edward J. Neural Network Data Analysis Using SimulnetTM. Springer New York, 1998.

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W, McLarty J., ed. Neural networks and genome informatics. Elsevier, 2000.

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Geological Survey (U.S.). National Mapping Division., ed. Neural network method for sharpening Landsat thermal data from higher resolution multispectral data. U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1997.

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Geological Survey (U.S.). National Mapping Division, ed. Neural network method for sharpening Landsat thermal data from higher resolution multispectral data. U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1997.

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Zaghloul, Waleed A. Text mining using neural networks. University of Nebraska-Lincoln, 2005.

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Souc̆ek, Branko. Neural and concurrent real-time systems: The sixth generation. Wiley, 1989.

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B, Bulsari A., ed. Neural networks for chemical engineers. Elsevier, 1995.

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Book chapters on the topic "Neural data processing"

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Kee, Estelle Xin Ying, and Jer Lang Hong. "A Heuristic Data Mining Framework Towards Dynamic Data of Social Media." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_48.

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Heni, Houyem, and Faiez Gargouri. "A Methodological Approach for Big Data Security: Application for NoSQL Data Stores." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_80.

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Kohjima, Masahiro, Yuta Nambu, Yuki Kurauchi, and Ryuji Yamamoto. "General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data." In Neural Information Processing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30105-6_13.

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Eftekharian, Esmaeel, Amin Khatami, Abbas Khosravi, and Saeid Nahavandi. "Data Mining Analysis of an Urban Tunnel Pressure Drop Based on CFD Data." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_16.

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Khushi, Matloob, Usman Naseem, Jonathan Du, Anis Khan, and Simon K. Poon. "Data Mining ENCODE Data Predicts a Significant Role of SINA3 in Human Liver Cancer." In Neural Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_2.

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Minowa, Tadashi, and Takeshi Takahashi. "Secure Distributed Storage for Bulk Data." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_67.

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Satish, D. Srikrishna, and C. Chandra Sekhar. "Kernel Based Clustering for Multiclass Data." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_197.

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Zhang, Hanchao, and Jinhua Xu. "Sparse Coding on Multiple Manifold Data." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_62.

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Rogovschi, Nicoleta, and Mohamed Nadif. "Weighted Topological Clustering for Categorical Data." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_71.

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Basirat, Amir H., and Asad I. Khan. "Introducing a Novel Data Management Approach for Distributed Large Scale Data Processing in Future Computer Clouds." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_46.

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Conference papers on the topic "Neural data processing"

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Sushchenko, Olha, Yurii Bezkorovainyi, Oleksander Salyuk, and Oleksander Zhdanov. "Application of Neural Networks in Digital Data Processing." In 2024 14th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2024. http://dx.doi.org/10.1109/acit62333.2024.10712550.

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Lesage, Xavier, Cristiano Merio, Fernando Welzel, Luca Sauer de Araujo, Sylvain Engels, and Laurent Fesquet. "Data-driven Processing Element for Sparse Convolutional Neural Networks." In 2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS). IEEE, 2024. http://dx.doi.org/10.1109/newcas58973.2024.10666329.

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Chen, Junjie. "Design Optimization of Data Pipelines in Gig Economy Platforms: Improving Data Processing Efficiency." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064443.

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Gu, Hongwei, Yuxiu Duan, Zongkai Shao, Pan Xiao, Kaili Wang, and Fenhua Bai. "Data-free binary neural network with image heterogeneity." In Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), edited by Robert Minasian and Li Chai. SPIE, 2025. https://doi.org/10.1117/12.3061121.

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Chang, Simyung, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, and Kyuwoong Hwang. "Subspectral Normalization for Neural Audio Data Processing." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413522.

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Gernigon, Cédric, Silviu-Ioan Filip, Olivier Sentieys, Clément Coggiola, and Mickaël Bruno. "Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing." In 2023 European Data Handling & Data Processing Conference (EDHPC). IEEE, 2023. http://dx.doi.org/10.23919/edhpc59100.2023.10396014.

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Castillo-Muniz, E., and E. Bayro-Corrochano. "Geometric spherical networks for Visual Data processing." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252770.

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Zhang, Chonglin, Lijuan Yu, and Weibing Wu. "Study on fitness data processing based on neural network information processing." In 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223619.

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Ilieva, Roumiana Yossifova, and Yoto Plamenov Nikolov. "Neural Network Processing Model for Data Flow Optimization." In 2019 X National Conference with International Participation (ELECTRONICA). IEEE, 2019. http://dx.doi.org/10.1109/electronica.2019.8825634.

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Das, Palash, Shivam Lakhotia, Prabodh Shetty, and Hemangee K. Kapoor. "Towards Near Data Processing of Convolutional Neural Networks." In 2018 31st International Conference on VLSI Design and 2018 17th International Conference on Embedded Systems (VLSID). IEEE, 2018. http://dx.doi.org/10.1109/vlsid.2018.94.

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Reports on the topic "Neural data processing"

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f
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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada259120.

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Levitan, Herbert. Microcomputer-Based Data Acquisition, Analysis and Control of Information Processing by Neural Networks. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177170.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att
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