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Adil H. Khan

4 papers indexed

openalexPeerJ Computer Science2025-10-07Cited by 1

BrainNet: a custom-designed CNN and transfer learning-based models for diagnosing brain tumors from MRI images

Adil H. Khan, Asad Khan, D. N. F. Awang Iskandar, Hiren Mewada, Saqib Saeed, Fahad Algarni, et al.

Cancer remains the second leading cause of death globally, with brain tumors exhibiting some of the lowest survival rates among all cancer types. Accurate diagnosis, guided by the tumor’s structure and location, is essential for selecting appropriate treatment strategies and impr…

Also available via: PeerJ, Inc.

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openalexTransdisciplinary Journal of Engineering & Science2024-07-02Cited by 6

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

Hiren Mewada, L. Syam Sundar, Bimal Patel, Miral Desai, Adil H. Khan

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators…

Also available via: Transdisciplinary Journal of Engineering & Science

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openalexSensors2023-07-24Cited by 14

Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification

Hiren Mewada, Jawad F. Al‐Asad, Faris A. Almalki, Adil H. Khan, Nouf Abdullah Almujally, Samir El-Nakla, et al.

Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to as "speech spoofing". The alg…

Also available via: Multidisciplinary Digital Publishing Institute

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openalexJournal of Electrical Engineering2021-08-01Cited by 4

Frequency domain despeckling technique for medical ultrasound images

Jawad F. Al‐Asad, Hiren Mewada, Adil H. Khan, Nidal Abu-Libdeh, Jamal Nayfeh

Abstract This work proposes a novel frequency domain despeckling technique pertaining to the enhancement of the quality of medical ultrasound images. The results of the proposed method have been validated in comparison to both the time-domain and the frequency-domain projections…

Also available via: De Gruyter

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