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Majed Alhusseni
College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia

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Journal article
Published: 01 July 2021 in Arabian Journal for Science and Engineering
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In the area of computer vision (CV), action recognition is a hot topic of research nowadays due to famous applications, which include human–machine interaction, robotics, visual surveillance, video analysis, etc. Many techniques are presented in the literature by researchers of CV, but still they faced a lot of challenges such as complexity in the background, variation in the camera view point and movement of humans. A new method is proposed in this work for action recognition. The proposed method is based on the shape and deep learning features fusion. Two-steps-based method is executed— human extraction to action recognition. In the first step, first, humans are extracted by simple learning process. In this process, HOG features are extracted from few selected datasets such as INRIA, CAVIAR, Weizmann and KTH. Then, we need to select the robust features using entropy-controlled LSVM maximization and performed detection. Second, geometric features are extracted from detected regions and parallel deep learning features are extracted from original video frame. However, the extracted deep learning features are high in dimension and some are not relevant, so it is essential to remove irrelevant features before fusion. For this purpose, a new feature reduction technique is presented named as entropy-controlled geometric mean . Through this technique, we can select the robust deep learning features and remove the irrelevant of them. Finally, both types of features (selected deep learning and original geometric) are fused by proposed parallel conditional entropy approach. The obtained feature vector is classified by a cubic multi-class SVM. Six datasets (i.e., IXMAS, KTH, Weizmann, UCF Sports, UT Interaction and WVU) are used for the experimental process and achieved an average accuracy of above 98.00%. The detailed statistical analysis and comparison with existing techniques show the the effectiveness of proposed method .

ACS Style

Muhammad Attique Khan; Yu-Dong Zhang; Majed Alhusseni; Seifedine Kadry; Shui-Hua Wang; Tanzila Saba; Tassawar Iqbal. A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition. Arabian Journal for Science and Engineering 2021, 1 -16.

AMA Style

Muhammad Attique Khan, Yu-Dong Zhang, Majed Alhusseni, Seifedine Kadry, Shui-Hua Wang, Tanzila Saba, Tassawar Iqbal. A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition. Arabian Journal for Science and Engineering. 2021; ():1-16.

Chicago/Turabian Style

Muhammad Attique Khan; Yu-Dong Zhang; Majed Alhusseni; Seifedine Kadry; Shui-Hua Wang; Tanzila Saba; Tassawar Iqbal. 2021. "A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition." Arabian Journal for Science and Engineering , no. : 1-16.

Journal article
Published: 06 July 2020 in Sensors
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Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

ACS Style

Sumair Aziz; Muhammad Umar Khan; Majed Alhaisoni; Tallha Akram; Muhammad Altaf. Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features. Sensors 2020, 20, 3790 .

AMA Style

Sumair Aziz, Muhammad Umar Khan, Majed Alhaisoni, Tallha Akram, Muhammad Altaf. Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features. Sensors. 2020; 20 (13):3790.

Chicago/Turabian Style

Sumair Aziz; Muhammad Umar Khan; Majed Alhaisoni; Tallha Akram; Muhammad Altaf. 2020. "Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features." Sensors 20, no. 13: 3790.

Journal article
Published: 19 June 2020 in Sustainability
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With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.

ACS Style

Muhammad Rashid; Muhammad Attique Khan; Majed Alhaisoni; Shui-Hua Wang; Syed Rameez Naqvi; Amjad Rehman; Tanzila Saba. A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection. Sustainability 2020, 12, 5037 .

AMA Style

Muhammad Rashid, Muhammad Attique Khan, Majed Alhaisoni, Shui-Hua Wang, Syed Rameez Naqvi, Amjad Rehman, Tanzila Saba. A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection. Sustainability. 2020; 12 (12):5037.

Chicago/Turabian Style

Muhammad Rashid; Muhammad Attique Khan; Majed Alhaisoni; Shui-Hua Wang; Syed Rameez Naqvi; Amjad Rehman; Tanzila Saba. 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection." Sustainability 12, no. 12: 5037.

Journal article
Published: 27 December 2019 in Electronics
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In centralized cache management for SDN-based ICN, it is an optimization problem to compute the location of cache servers and takes a longer time. We solve this problem by proposing to use singular-value-decomposition (SVD) and QR-factorization with column pivoting methods of linear algebra as follows. The traffic matrix of the network is lower-rank. Therefore, we compute the most important switches in the network by using SVD and QR-factorization with column pivoting methods. By using real network traces, the results show that our proposed approach reduces the computation time significantly, and also decreases the traffic overhead and energy consumption as compared to the existing approach.

ACS Style

Jan Badshah; Majed Mohaia Alhaisoni; Nadir Shah; Muhammad Kamran. Cache Servers Placement Based on Important Switches for SDN-Based ICN. Electronics 2019, 9, 39 .

AMA Style

Jan Badshah, Majed Mohaia Alhaisoni, Nadir Shah, Muhammad Kamran. Cache Servers Placement Based on Important Switches for SDN-Based ICN. Electronics. 2019; 9 (1):39.

Chicago/Turabian Style

Jan Badshah; Majed Mohaia Alhaisoni; Nadir Shah; Muhammad Kamran. 2019. "Cache Servers Placement Based on Important Switches for SDN-Based ICN." Electronics 9, no. 1: 39.

Journal article
Published: 04 December 2019 in Applied Sciences
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The configuration is essential to diagnose the status of the grounding grid, but the orientation of the unknown grounding grid is ultimately required to diagnose its configuration explicitly. This paper presents a transient electromagnetic method (TEM) to determine grounding grid orientation without excavation. Unlike the existing pathological solutions, TEM does not enhance the surrounding electromagnetic environment. A secondary magnetic field as a consequence of induced eddy currents is subjected to inversion calculation. The orientation of the grounding grid is diagnosed from the equivalent resistivity distribution against the circle perimeter. High equivalent resistivity at a point on the circle implies the grounding grid conductor and vice versa. Furthermore, various mesh configurations including the presence of a diagonal branch and unequal mesh spacing are taken into account. Simulations are performed using COMSOL Multiphysics and MATLAB to verify the usefulness of the proposed method.

ACS Style

Aamir Qamar; Inzamam Ul Haq; Majed Alhaisoni; Nadia Nawaz Qadri. Detecting Grounding Grid Orientation: Transient Electromagnetic Approach. Applied Sciences 2019, 9, 5270 .

AMA Style

Aamir Qamar, Inzamam Ul Haq, Majed Alhaisoni, Nadia Nawaz Qadri. Detecting Grounding Grid Orientation: Transient Electromagnetic Approach. Applied Sciences. 2019; 9 (24):5270.

Chicago/Turabian Style

Aamir Qamar; Inzamam Ul Haq; Majed Alhaisoni; Nadia Nawaz Qadri. 2019. "Detecting Grounding Grid Orientation: Transient Electromagnetic Approach." Applied Sciences 9, no. 24: 5270.