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Your search keyword '"Al-Zahrani, Fahad Ahmed"' showing total 145 results
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145 results on '"Al-Zahrani, Fahad Ahmed"'

38. An Optimal Method for Speech Recognition Based on Neural Network

Author

Ishak, Mohamad Khairi, Madsen, Dag Øivind, and Al-Zahrani, Fahad Ahmed

Subjects
Computational Theory and Mathematics, Artificial Intelligence, Software, Theoretical Computer Science
Abstract

Natural language processing technologies have become more widely available in recent years, making them more useful in everyday situations. Machine learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising breakthroughs. These methods were particularly advantageous for regional languages, as they were provided with cutting-edge language processing tools as soon as the requisite corporate information was generated. The bulk of modern people are unconcerned about the importance of reading. Reading aloud, on the other hand, is an effective technique for nourishing feelings as well as a necessary skill in the learning process. This paper proposed a novel approach for speech recognition based on neural networks. The attention mechanism is first utilized to determine the speech accuracy and fluency assessments, with the spectrum map as the feature extraction input. To increase phoneme identification accuracy, reading precision, for example, employs a new type of deep speech. It makes use of the exportchapter tool, which provides a corpus, as well as the TensorFlow framework in the experimental setting. The experimental findings reveal that the suggested model can more effectively assess spoken speech accuracy and reading fluency than the old model, and its evaluation model’s score outcomes are more accurate.

Published
2023
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45. Detection of Different Stages of Alzheimer's Disease Using CNN Classifier.

Author

Mahmud, S. M. Hasan, Ali, Md Mamun, Shahriar, Mohammad Fahim, Al-Zahrani, Fahad Ahmed, Ahmed, Kawsar, Nandi, Dip, and Bui, Francis M.

Subjects
DEEP learning, ALZHEIMER'S disease, MILD cognitive impairment, CONVOLUTIONAL neural networks, SUPPORT vector machines, K-nearest neighbor classification
Abstract

Alzheimer's disease (AD) is a neurodevelopmental impairment that results in a person's behavior, thinking, and memory loss. Themost common symptoms of ADare losingmemory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stagedetection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build a predictive model, open-source data was collected where five stages of images of AD were available as Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and AD. Every stage of AD is considered as a class, andthenthe datasetwas dividedintothree partsbinary class, three class, andfive class. In this research, we applied different preprocessing steps with augmentation techniques to efficiently identifyAD. It integrates a random oversampling technique to handle the imbalance problem from target classes, mitigating the model overfitting and biases. Then three machine learning classifiers, such as random forest (RF), K-Nearest neighbor (KNN), and support vector machine (SVM), and two deep learning methods, such as convolutional neuronal network (CNN) and artificial neural network (ANN) were applied on these datasets. After analyzing the performance of the used models and the datasets, it is found that CNN with binary class outperformed 88.20% accuracy. The result of the study indicates that the model is highly potential to detect AD in the initial phase. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Exploration of LSPR-based Refractive Index Sensor Coated with Silver-MgF2 Layer.

Author

Basak, Nilanjana, Sultana, Naznin, Mitu, Sumaiya Akhtar, R, Vinod Kumar, Al-Zahrani, Fahad Ahmed, Patel, Shobhit K., and Ahmed, Kawsar

Subjects
REFRACTIVE index, SURFACE plasmon resonance, FINITE element method, DETECTORS
Abstract

A refractive index (RI) sensor has been demonstrated and numerically analyzed by the finite element method (FEM) to gain supreme sensitivity on the basis of localized surface plasmon resonance (LSPR). Plasmonic material, such as silver, has been used as a coating layer outside of the dielectric core. Various structural parameters have been analyzed for the proposed sensors, which have a significant influence on the sensing field. To make the sensing strength much stronger, a thin MgF2 layer has been added to the silver layer. The wavelength and amplitude interrogation method achieves extremely high wavelength sensitivity (WS) responses of 948.67 μm/RIU and amplitude sensitivity (AS) responses of − 1602.82 RIU−1 in the analyte RI range of 1.35 to 1.38. The operating wavelength range of 5.5–7.5 μm shows efficient results in this structure. The proposed sensor is used for identifying analytes in the biological and biochemical fields and is also applicable in bio-photonics. Owing to its low confinement losses and simply designed structure, the proposed RI sensor will be extensively appropriate for bio-sensing because of its outstanding sensitivity response. [ABSTRACT FROM AUTHOR]

Published
2023
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