NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Neural Networks Computer")'

Creating Brain-like Intelligence : From Basic Principles to Complex Intelligent Systems /

By Sendhoff, Bernhard. Berlin ; | New York : Springer, 2009 . viii, 350 p. : , "International symposium Creating brain-like intelligence was held in February 2007 in Germany"--Pref. 24 cm.. 9783642006159 (pbk. : alk. paper

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Intelligent Engineering Systems and Computational Cybernetics /

By Machado, J. A. Tenreiro.. 9781402086779

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Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems /

By Géron, Aurélien,. Beijing Oreilly 2019 . xxv, 819 pages : , "2nd edition updated for TensorFlow 2"--Page 1 of cover. 24 cm. 1492032646 | 9781492032649

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Introduction to Artificial Intelligence /

By Ertel, Wolfgang,. . 1 online resource (XIV, 356 pages 130 illustrations, 46 illustrations in color.) 9783319584874

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Multi Stage Quasi-labeled Vehicle Re-Identification Using Branched Convolutional Neural Networks /

By Haider, Ali . . 48 p. ;

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Audio Phonic ( An Audiovisual Auto corrector) /

By Sadiq, Muhammed Bilal . . 67 p

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Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network /

By Nazir, Abida . . 44p. , Reaching and Grasping is most signi cant component of human life.Translation of EEG in the form of upper limb movement is of great importance for realization of natural neuroprosthesis control and restoration of hand movements of patients with motor disorders. Patients su ering from spinal cord injury (SCI)problems have lost most of voluntary motor control functions. Such type of loss can be cured using movement related cortical potentials (MRCPS) analysis. Brain computer interface with limb neuro-prosthesis is considered as a solution to such problems. This study anlyzes EEG signals in relation with natural reach and grasp actions. EEG signals have movement related cortical potentials (MRCPS) which can be used to decode upper limb movements. This experiment was performed in Graz University of Technology Austria and they o ered free access dataset for further exploration.Total 45 subjects were involved in this study, 15 subjects with every type of electrode:gel,water and dry performed the experiment. All subjects accomplished self-initiated 80 reach and grasp actions toward a spoon within the jar (lateral grasp) and toward an empty glass (palmar grasp).EEG signals are recorded using three types of electrodes: water based, Gel based and Dry electrodes. In this study signals are classi ed using Deep learning technique i.e Convulotional Neural Networks. For analysis, EEG signals were preprocessed using various lteration techniques. After ltration data is fed into classi er for classi cation of signals. Data is divided into test set and training set. Grand average peak accuracy calculated on unseen test data resulted in 54.2% classi cation accuracy i.e Gel based accuracy approached 56.8.4%, water based 52.7% and dry based 51.8%. 30cm.

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Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow /

By Ekman, Magnus,. Boston : Addison-Wesley; 2022 . 688p 9780137470358

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Multi-Task Learning using Brain Computer Interface /

By Fazal, Mariyam . . 102p. , Brain-computer interfaces (BCIs) can decode not only what users are thinking but also the intensity of their cognitive effort. However, BCIs have traditionally been constrained to single-task applications. Multitask learning (MTL) offers a promising solution by enabling BCIs to handle multiple related tasks simultaneously, enhancing both performance and usability.This study applied MTL to EEG data from N-back working memory tasks (0-back, 2-back, and 3-back) using openaccess data from 26 participants at Technische Universität Berlin. We developed a novel hybrid CNN-LSTM-tAPEformer architecture that integrates Convolutional Neural Networks for spatial feature extraction, Long Short-Term Memory networks for temporal sequence modeling, and Transformer blocks with specialized attention mechanisms for capturing long-range temporal dependencies. The proposed model performs dual functions by classifying accurate behavioral responses while simultaneously measuring cognitive workload across varying task complexity levels. Notable innovations include the development of Time Absolute Position Encoding (tAPE) that enhances temporal processing by integrating sinusoidal positional encoding with adaptive channel-specific encoding to preserve temporal relationships in EEG data. The system incorporates regional and temporal self-attention mechanisms along with global attention pooling to achieve enhanced neural pattern detection. Through leave-one-subject-out cross-validation methodology, the model was trained using data from all participants except one, then evaluated on the excluded individual to assess cross-subject generalization performance. Findings validate the hybrid CNNLSTM-tAPEformer model's efficacy for practical multi-task learning implementations, establishing its utility for BCI applications that demand concurrent cognitive state identification and workload assessment. 30cm.

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A Pragmatic Framework for Component (Source Code) Retieval /

By Bibi, Nazia . Rawalpindi, MCS (NUST), 2024 . xxii, 296 p

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