NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Machine Learning")'

Nonlinear dispersive waves : asymptotic analysis and solitons asymptotic analysis and solitons

By Ablowitz, Mark J.. Cambridge, UK ; | New York : Cambridge University Press, 2011 . xiv, 348 p. : 23 cm.. 9781107012547 (hardback) | 1107012546 (hardback) | 9781107664104 (pbk.) | 1107664101 (pbk.)

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E-learning and the science of instruction : proven guidelines for consumers and designers of multimedia learning proven guidelines for consumers and designers of multimedia learning

By Clark, Ruth Colvin.. San Francisco, CA : Pfeiffer, 2011 . Pfeiffer, 22 cm.. 9780470874301 (hardback)

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Universal design for learning in the classroom :practical applications practical applications /

New York : Guilford Press, 2012 . xix, 156 p. : 26 cm.. 9781462506316 (pbk.) | 9781462506354 (hardcover)

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Introduction to game design, prototyping, and development : from concept to playable game-with Unity® and C# /

By Gibson, Jeremy,. . xxxi, 908 pages : 23 cm. 9780321933164 (paperback) | 0321933168 (paperback)

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Brain Tumor Segmentation from Multi-modality MRI Scans Using Machine Learning

By Khalid Usman. Islamabad NUST-SEECS 2015 . 47p. , Supervisors: Dr. Kashif Mahmood Rajpoot, Dr. Muhammad Muneeb ullah, Dr. Rafia Mumtaz, Dr. Moazam Faraz

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Intelligent Engineering Systems and Computational Cybernetics /

By Machado, J. A. Tenreiro.. 9781402086779

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Handbook of ambient intelligence and smart environments \

By Nakashima, Hideyuki.. New York : Springer, 2010 9780387938073 (hbk.)

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Modelling and Control for Intelligent Industrial Systems : Adaptive Algorithms in Robotics and Industrial Engineering /

By Rigatos, Gerasimos G.. 9783642178740

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Power equipment engine technology /

By Abdo, Edward.. Clifton Park, NY : Delmar/Cengage Learning, 2011 . 1 online resource (xii, 398 pages) : , Includes index. 9781418053888

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Image processing for remote sensing-(E-BOOK)

Boca Raton, FL : CRC Press/Taylor & Francis Group, 2008 . 380 s. 1-4200-6664-1 | 978-1-4200-6664-7

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ARTIFICIAL INTELLIGENCE and SOFTWARE ENGINEERING(EBOOK)

By Partridge, Derek. New Delhi Glenlake Publishing 1998 . xi,274p 0814404413

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Application of Machine Learning (E-Book)

By Yagang Zhang. India In-Teh 2010 . viii,280p;

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Principles of water treatment /

Hoboken : Wiley, 2012 . xvii, 654 pages : , Includes index. 25 cm.. 9780470405383 (hardback)

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New shop floor management : empowering people for continuous improvement /

By Suzaki, Kiyoshi.. [S.l.] : Free Press, 1993 . 462 p. ; , Hardcover. 24 cm.. 0029322650 | 9780029322659

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Wealth building strategies in energy, metals and other markets /

By Waltzek, Chris.. Hoboken, N.J. : Wiley, 2010 . xvii, 265 p. : , Includes index. 24 cm.. 9780470638309 (hardback) | 0470638303 (hardback)

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Object-Oriented Analysis, Design and Implementation An Integrated Approach

By Dathan, Brahma.. New York Springer 2015 9783319242804 | 9783319242781 (print)

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Fluid mechanics SI Version

By Munson, Bruce Roy,. Delhi: John Wiley & Sons, Inc., 2013 . xix, 747, 6, 10 pages : 28 cm. 9781118116135 | 9781118399712 (binder ready version)

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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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Foundations of deep reinforcement learning : theory and practice in Python /

By Graesser, Laura Harding,. Boston Addison-Wesely 2020 . 379p 9780135172384

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Mathematics for machine learning

By Deisenroth, Marc Peter,. UK Cambridge University Press 2020 . 371p 9781108679930

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TH-SOF-1676- Biometric Identification via ECG Signal using Machine Learning based approach

By UBAID UR REHMAN . ISLAMABAD NUST COLLEGE OF EME 2019

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Contract law

By Andrews, Neil. UK Cambridge University Press 2015 . lii, 653 pages ; 25 cm. 9781107660649 (Paperback)

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Machine Learning: Algorithms and Applications / Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier

2017 , Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied. 9781498705387

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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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Teaching English for tourism : bridging research and praxis

London ;New York, NY : Routledge, 2019 . 249p 9780367144555 (hbk)

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Machine Learning /

By Dutt, Saikat . Pearson India 2019 . 456 p.: ‎ 978-9353066697

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Enhancing Tumor Segmentation in 3D MRI Scans with YOLOv8 And Volumetric segmentation: A Computational Efficient Approach /

By Abdullah. . 71p. ; 30cm..

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Diagnosing and localizing Covid-19 in High resolution CT(HRCT) scans using Deep learning /

By Munir, Zonaira. . 62p. ; 30cm..

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Perception of Emotion in Human-Robot Interaction /

By Zia, Muhammad Faisal. . 59p. , Perception of emotion is an intuitive replication of a person’s internal state without the need for verbal communication. Visual emotion recognition has been broadly studied and several end-toend deep neural networks (DNNs)-based and Machine learning-based models have been proposed but they lack the ability to be implemented in low-specification devices like robots, and vehicles. The drawbacks of conventional handcrafted feature-based Facial Emotion Recognition (FER) methods are eliminated by DNNs-based FER approaches. In spite of that, Deep Neural Network based FER techniques suffer from high processing costs and exorbitant memory requirements, their application is constrained in fields like Human-Robot Interaction (HRI) and HumanComputer Interaction (HCI) and relies on hardware requirements. In aforementioned study, we presented a computationally inexpensive and robust FER system for the perception of six basic emotions (i.e., disgust, surprise, fear, anger, happy, and sad) that is capable of running on embedded devices with constrained specifications. In the first step after pre-processing input images, geometric features are extracted from detected facial landmarks, considering the facial spatial position among influential landmarks. The extracted features are given as input to trainthe SVM classifier. Our proposed FER system was trained and evaluated experimentally using two databases, Karolinska Directed Emotional Faces (KDEF) and Extended Cohn-Kanade (CK+) database. Fusion of KDEF and CK+ datasets at the training level were also employed in order to generalize the FER system’s response to the variations of ethnicity, race, national and provincial backgrounds. The results show that our proposed FER system is optimized for real-time embedded applications with constrained specifications and yields an accuracy of 96.8%, 86.7% and 86.4% for CK+, KDEF and fusion of CK+ and KDEF databases respectively. As a part of our future research objectives, the developed system will make a robotic agent capable of perceiving emotion and interacting naturally without the need for additional hardware during HRI. 30cm.

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Automatic Detection and Recognition of Citrus Fruits Diseases Using Deep Learning Model /

By Faisal, Shah . . 59p. , In a country's economy, agriculture plays a very vital role. Agriculture's yield and production are reduced by plant diseases, resulting in significant economic losses and instability in the food market. In plants, the citrus fruit crop is one of the most important agricultural products in the world, produced and grown in around 140 countries. It has a lot of nutrients, such as vitamin C. However, due to pests and diseases, citrus cultivation is widely affected and suffers significant losses in both yield and quality. The majority of plant diseases exhibit visible symptoms, and the accepted method used today is for a skilled plant pathologist to detect the diseases by examining affected plant leaves under a microscope, which is a costly and time-consuming method. During the last decade, computer vision and machine learning have been widely adopted to detect and classify plant diseases, providing opportunities for early disease detection and bringing improvements to agricultural production. The early detection and accurate diagnosis of plant diseases are essential for reducing their spread and damage to crops. In this work, we presented an automatic system for early detection and recognition of citrus plant diseases based on a deep learning (DL) model to improve accuracy and reduce computational complexity. The most recent transfer learning-based models were applied to our dataset in order to increase classification accuracy. In this work, we successfully proposed a CNN-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, (InceptionV3) for the identification and classification of citrus plant diseases using transfer learning. In order to assess the performance of the model, we found that the transfer of an EfficientNetb3 model led to the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. The proposed CNN model exceeds other cutting-edge CNN network architectures developed in earlier literature in the identification and categorization of citrus plant diseases. 30cm.

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Assessment of Stress Biomarkers in the Saliva of Smokers and Nonsmokers via UV Photospectrometry and POMS /

By Fahim, Maria . . 99p. , Smoking is one of the major health catastrophes. Smoking is believed to be the major cause of chronic diseases like Cardiovascular complications, stroke, pregnancy issues, respiratory failure, etc. There are three important transdiagnostic emotional factors that make the population vulnerable to initiation of smoking i.e. anhedonia anxiety sensitivity, distress tolerance. Research studies for the past five decades have proven the adverse effect of stress on brain physiology and functioning. The human body responds to trauma (physical or non-physical stress) in a definite manner. This response of the body can be qualitatively and quantitatively monitored through several chemicals in the bloodstream, saliva, or urine; responding to the stress, called stress biomarkers i.e. Brain-derived neurotrophic factor, cortisol, cytokines etc. Saliva delivers an efficient specimen for various diagnostic procedures due to the presence of different biological products and secondary metabolites of xenobiotics and helps in determining the disease progressions as well as therapy outcomes depending on the variations in the markers/triggers. The nature of mindset and mood states are evaluated by a scales designed to rate the behavior of an individual towards the environmental stimuli that may be physical or psychological in nature. This psychological rating scale is known as the profile of mood state (POMS). This scale was initially originated by McNair, Droppleman, and Lorr in 1971. This scale is presented in the form a questionnaire including different questions regarding the mode and feelings of a subject.This research work aims to further elucidate the utilization of UV Photospectrometry for quantitatively assessing POMS and its relation to the stress biomarker. The samples were collected form the vicinity of the university campus H-12 Islamabad. The samples were processed and stored at the biomedical laboratory of School of Mechanical and Manufacturing Engineering (SMME), NUST. A total of twenty-four (24) male subjects were analyzed. A total of two groups were considered. Group 1 included the non-smoking participants, while group 2 included smoking participants. Simple spitting technique was used for the collection of unstimulated saliva. About 4 ml unstimulated saliva was collected in the sterile falcon tube. Saliva was temporarily stored in cool boxes at 4°C and immediately II shifted to the facility. Centrifugation of the salivary sample was done at 4°C for 5 minutes and 10,000 rpm. Saliva sample was frozen at -80°C until sample collection span was completed. The mood state of the participants was also evaluated using the profile of mood state technique used initially by McNair, Lorr, and Droppleman in 1971. The total mood disturbance (TMD) score was calculated that ranges from -32 to 200. The questionnaire was accessed from ―Mackenzie, B. (2001) Profile of Mood States (POMS) [WWW] Available from: https://www.brianmac.co.uk/poms.htm [Accessed 26/6/2022]‖. Simulated neural networking (SNN) was applied to the collected data from smokers and non-smokers for accuracy scoring. The required statistical analysis was performed and the data was statistically analyzed through a software ―GraphPad Prism 8.0‖ and the respective graphs were plotted. UV spectrophotometry studies provided peak plasma concentration peaks at the lower UV range of 190 to 210 nm, but with no significant difference, representing the presence of biological stress markers. The profile of mood state evaluation studies concluded that the smoking participants were presented with a significantly higher level of individual mood profile scores i.e. anger (****, P<0.0001), confusion (**, P<0.0014), fatigue (*, P<0.0354), tension (*, P<0.0422) and stress as compared to nonsmoking participants. The vigorous score was significantly high in the nonsmoking individuals (****, P<0.0001). Similarly, total mood disturbance score was also significantly high in the smoking participants. The application of artificial neural networking through artificial machine learning scored the accuracy of the results 84% which is a reliable outcome. The current research work concludes that different stress stimuli including physiological stress and psychological stress tends to initiate/increase the smoking behavior among the community. Likewise, it is also concluded that smoking initiation may not be always triggered in response to stress. Numerous factors i.e. lack of education, negative inspiration, or behavior to impress are also involved. Furthermore, the adaptation of smoking behavior as a result of stressful stimuli is not a valid approach to reduce the noxious/stressful stimuli. The stress may further be exaggerated by smoking. 30cm.

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Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) /

By Asgher Umer. . 148,p; 30,cm..

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Python data science handbook : essential tools for working with data /

By Vanderplas, Jacob T.,. Sebastopol, CA : O'Reilly Media, 2022 . xxiv, 563 pages : , Previous edition: 2016. 24 cm. 9781098121228 | 1098121228

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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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Segmentation and classification of brain tumor MR images through advance machine learning methods/

By Farhi Lubna . . 124,p; 30,cm..

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BreachBuster AI Pentesting Assistant /

By Usman, M. Umar . MCS, NUST Rawalpindi 2024 . xi, 79 p

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Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) /

By Asgher,Umer . . 172p. , Humans in the modern systems not only interact with other humans but also have to interact with intelligent machines, robots in form of cyber physical systems to collaborate in order to carry out different tasks in real working environment. The modern Industrial system comprises of humans, machines, and cyber systems with a collective aim of optimized industrial manufacturing objectives, human factors, and ergonomics goals. Different macrohuman factors are considered while designing and formulating human work safety of such systems and one of the important neuroergonomic factors in is the Cognitive and Mental Workload (C-MWL). The mental workload (MWL) in the human’s brain is measured with difference non-invasive neuroimaging techniques. Most of the cognitive load measuring methods either require massive system protocols like fMRI (functional magnetic resonance imagining), positron-emission tomography (PET) or strict human anatomical movements restrictions like electroencephalogram (EEG) and magnetoencephalography (MEG). To address these limitations, fNIRS (functional Near infrared Spectroscopy) is used in this research to measure the hemodynamic changes in the human brain’s tissues as a measure of the brain activity. The brain’s hemodynamic signals are measured using a light weight portable fNIRS system (P-fNIRSSyst) that is designed to measure relative change in concentration of chromophores (oxy and deoxy hemoglobin) in brain tissues. In this study a novel variant of MBLL (Modified Beer-lambert Law) is designed by keeping the previous intensity value as a reference by taking the average from initial four seconds activity stimuli in optical density. The four second stimuli average in novel and important in calculation the changes in concentration of chromophores. This novel variant of MBLL is Fixed Value - Modified Beer-lambert Law (FV-MBLL). In this research, MWL is measured and classified in different real time working environments. The two-state cognitive load is measured with fNIRS system and classified using FV-MBLL using machine learning techniques like k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN). The classification accuracies of FV-MBLL are better than MBLL. The research further explores the classification capabilities of deep neural networks (DNN) such as convolutional neural network s (CNN) and Long short-term memory (LSTM) for the first time in assessment and classification of four- iv state MWL. The classification accuracies of LSTM outperform not only ML algorithms (SVM, KNN and ANN) but CNN as well in classification of multi-state MWL. The research experimental validation is performed using the accuracies with MWL that are further utilized in neurorehabilitation as neurofeedback to operate bionic systems using Brain Machine Interface (BMI). 30cm.

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Brain-Computer Interface for Mental State Detection of Drivers /

By Arif, Saad . . 169p. , Background: Each year millions of vehicles suffer crashes on the roads globally due to deteriorated mental state of the drivers during driving tasks which result in higher casualties. Drowsy driving is the leading cause of high fatality rate which is instigated due to sleep deprivation, fatigue, and anxiety, etc. Vehicular, and driver’s behavioral data-dependent systems detect the drowsiness after its onset when an accident is more likely, and they are also subject to false identification. The drowsy mental state of drivers must be detected earlier for in-time warning to avoid fatal losses, and also the system must be less intrusive, and adaptable for normal driving tasks. Detection systems using physiological signals from human organs are comparatively underexplored in which bodily states of the subject can be identified earlier and with more reliability. It is postulated that all the bodily states are primarily originated from the human brain which could be a promising region to detect drowsiness at an earlier stage. Among many physiological signals, electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) are chosen for this study because they are proved to be more portable, easy to use, non-invasive, and reliable brain modalities for human mental state detection. Aim and Objectives: This study aimed to design a passive brain-computer interface (pBCI) scheme for the earlier detection of driver drowsiness with minimum intrusion into the driving task. Design objectives for the pBCI scheme were to select the channel of interest (COI), online detection with a shorter time window, minimum recalibration and setup time, and development of a widely applicable standard as an inter-subject transfer framework (iSTF) to cater to the inter-subject variability. All these objectives lead to such a pBCI system that is readily available for any subject, anytime, easy to use with minimal design, and yet detecting the drowsiness correctly at an earlier stage to avoid life losses. Methodology: Multichannel EEG and fNIRS brain signals from anterior, posterior, and lateral brain regions of sleep-deprived drowsy subjects were acquired during the simulated driving task for post hoc analysis. Initial pBCIschemes used labeled EEG data acquired from prefrontal (PFC), frontal, and occipital cortices for extracting the eight spectral, and eight temporal xvi features of EEG signals. Seven supervised machine learning classifiers were used to do the cross-validated binary classification of drowsy, and alert brain states. Initial design only achieved a few objectives and generated the need to use different modalities to meet all the requirements. The final pBCI scheme used labeled fNIRS data acquired from PFC and dorsolateral prefrontal cortices (DLPFC) for extracting the six cerebral oxygen regulation (CORE) and three hemodynamic signal features. CORE states of wakefulness and non-rapid eye movement (NREM) sleep stages were used to design a novel standard framework for wide applicability, and sleep stage classification using the vector phase analysis (VPA) approach. VPA was used for classifying microsleep/lapse, and drowsiness detection was done using the proposed brain hemodynamic patterns. Results: 𝛿, 𝜃, 𝛼, 𝛽 band powers as EEG spectral features achieved 82% accuracy in 10 s detection window. Signal skewness, variance, mean, peak as EEG temporal features achieved 87.2% accuracy in 1 s detection window. Ensemble classifier declared F8 as COI for earlier drowsiness detection using both the EEG pBCI schemes. Only the objectives of the short detection window and COI were achieved with the initial designs. In the final design with fNIRS, the novel VPA features: CORE vector gradients, achieved 94.1% accuracy in 5 s detection window for NREM sleep stage classification using ensemble classifier with the least computation time of 44 ms. Precise spatial localization of fNIRS declared AF8 position in right DLPFC as COI. The novel sleep stages-based threshold criteria along with VPA were crossvalidated as a standard iSTF for online microsleep detection with the least recalibration and setup time. Feature selection and achieved results were validated with various statistical significance tests. All the design objectives were attained with the fNIRS-based pBCI scheme. Conclusion: The aim to detect the driver's drowsiness earlier with minimum intrusion into the driving task, is accomplished. The presented fNIRS-based adaptive pBCI scheme is readily available for any subject, anytime, easy to wear with minimal ergonomic design, capable of real-time, correct, and early detection of the driver drowsiness to lessen the life losses in vehicular driving scenarios. The recommended research directions will surely justify, improve, and broaden the application horizons of the presented design 30cm..

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Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning /

By Nayyab, Rida. . 81p. , Cardiovascular diseases are considered the major cause of death worldwide surpassing cancer. However, despite the broad category of diseases, research has been limited to binary classification i.e. normal and abnormal class leaving behind the accurate classification of specific diseases that affect the ECG waveform. PTB – XL database offers a wide variety of ECG records, but little research is dedicated to extracting morphological features for multi-class classification. Therefore, the paper used the open database to filter the ECG signal records having single unique labels and pre-processed them using the Butterworth bandpass filter and DWT db8. The Bandpass filter corrected baseline wander and reduced noise however, a high signal-to-noise ratio was achieved after applying 8-level DWT. The processed signals were fed into the Pan-Tompkins algorithm to extract R peaks. These peaks served as a baseline to identify other morphological features i.e. P-QRS-T intervals and amplitudes. These extracted features were labelled into 1 normal and 4 abnormal classes. There was a class imbalance in the dataset that could cause bias while training models. Therefore, SMOTE-NC was applied to upsample the dataset. The new dataset was split into the training set and the testing set. These sets were given as inputs to CNN and DNN models for a 5-fold loop. The performance was evaluated for both models using metrics like F1 score, recall, precision and accuracy. The CNN model achieved a mean accuracy of 81% whereas the mean accuracy for DNN was 84%. It was also noted that among the 5 classes, HYP was consistently being classified accurately at 98%. 30cm.

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Real-Time Target Acquisition Test for Rehabilitation Using EMG /

By Ain ,Qurat Ul . . 109p. ; , This research investigates the use of electromyography (EMG) signals for real-time control in rehabilitation applications. Utilizing the Myo armband, we captured EMG signals corresponding to 12 distinct hand and finger movements. We compared the performance of two machine learning classifiers, Long Short-Term Memory (LSTM) networks and Vanilla Neural Networks (VNN), in accurately classifying these movements. LSTM networks demonstrated superior performance, achieving higher accuracy and robustness in signal classification compared to VNN. To address adaptability and reduce training time for new users, we employed transfer learning techniques. Our research also incorporated transfer learning techniques to enhance model performance, leveraging both a broad dataset collected from multiple subjects and a focused dataset from a single individual over an extended period. Our results show that transfer learning significantly improves the adaptability of the system, allowing for quicker and more efficient integration of new subjects into the model. The study further includes statistical analysis to validate the performance improvements, with paired t-tests and ANOVA confirming the significance of our findings. This work highlights the potential of LSTM networks and transfer learning in enhancing the usability and effectiveness of EMG-based control systems for rehabilitation, paving the way for more responsive and adaptable prosthetic devices. The integration of advanced machine learning techniques into EMG signal processing presents a promising avenue for future research and clinical applications. 30cm..

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Cyber Anomaly Detection using ML with Wazuh/ELK (CADM) /

By Ashiq, Hussain . MCS, NUST Rawalpindi 2024 . 58 p

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Speech to Speech Translation with Emotion Detection /