This page has only limited features, please log in for full access.

Unclaimed
Mohammed Hamdi
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 11 August 2021 in Applied Sciences
Reads 0
Downloads 0

Web readers usually skim through the text to keep up with the amount of available content. The effectiveness of skim reading is ensured by keeping the focus on the meaningful part of the text rather than the less meaningful part. To assure if the skim reading shows efficient results for a particular screen resolution, this research presents variations in the memory of significant data when a text is read on a mobile screen or a desktop screen. Moreover, the study aims to understand the knowledge gained from the text at a given time. In total, sixty participants contributed to the study and it is found that, relative to reading the text on a mobile screen, skimming showed improved memory for the ideas defined in the text on a computer screen. A software prototype is developed in this research study to analyze the impact of skim reading on a desktop computer screen versus a mobile device screen. The findings of the study have been interpreted as evidence in support of a skimming process called satisficing.

ACS Style

Mesfer Alrizq; Sara Mehmood; Naeem Ahmed Mahoto; Ali Alqahtani; Mohammed Hamdi; Abdullah Alghamdi; Asadullah Shaikh. Analysis of Skim Reading on Desktop versus Mobile Screen. Applied Sciences 2021, 11, 7398 .

AMA Style

Mesfer Alrizq, Sara Mehmood, Naeem Ahmed Mahoto, Ali Alqahtani, Mohammed Hamdi, Abdullah Alghamdi, Asadullah Shaikh. Analysis of Skim Reading on Desktop versus Mobile Screen. Applied Sciences. 2021; 11 (16):7398.

Chicago/Turabian Style

Mesfer Alrizq; Sara Mehmood; Naeem Ahmed Mahoto; Ali Alqahtani; Mohammed Hamdi; Abdullah Alghamdi; Asadullah Shaikh. 2021. "Analysis of Skim Reading on Desktop versus Mobile Screen." Applied Sciences 11, no. 16: 7398.

Article
Published: 29 June 2021 in Wireless Personal Communications
Reads 0
Downloads 0

This paper introduces a novel Energy Efficient Mobility-Based Watchman Algorithm (E2-MBWA) to intensification packet delivery ratio of mitigating the Hotspot issue in Wireless Sensor and Actor Networks (WSAN). Hotspot issue mostly causes of network breakdown and decrease of data packet delivery. Therefore, it is required to design a new technique for data packet forwarding that can resolve these issues in the network. In this study, E2-MBWA has introduced, that cope with the layer-by-layer mechanism for data packet forwarding. The proposed algorithm works with the help of the Data Packet Forwarding Algorithm (DFPA) and Watchman Layer Update Mechanism (WLUM). Furthermore, it also rescues the data storage issues, for this, used secondary nods as substitutes. Moreover, proposed technique is compared with some latest baseline’s approaches, for example, Efficient Traffic Load Reduction Algorithm (ETLRA). The analytical energy model is also described for the best health of the network to measure the accuracy level of the Hotspot issue.

ACS Style

Umar Draz; Tariq Ali; Sana Yasin; Sarah Bukhari; Muhammad Salman Khan; Mohammed Hamdi; Saifur Rahman; Low Tang Jung; Amjad Ali. An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes. Wireless Personal Communications 2021, 1 -26.

AMA Style

Umar Draz, Tariq Ali, Sana Yasin, Sarah Bukhari, Muhammad Salman Khan, Mohammed Hamdi, Saifur Rahman, Low Tang Jung, Amjad Ali. An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes. Wireless Personal Communications. 2021; ():1-26.

Chicago/Turabian Style

Umar Draz; Tariq Ali; Sana Yasin; Sarah Bukhari; Muhammad Salman Khan; Mohammed Hamdi; Saifur Rahman; Low Tang Jung; Amjad Ali. 2021. "An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes." Wireless Personal Communications , no. : 1-26.

Journal article
Published: 08 March 2021 in Electronics
Reads 0
Downloads 0

Bioacoustics plays an important role in the conservation of bird species. Bio-acoustic surveys based on autonomous audio recording are both cost-effective and time-efficient. However, there are many bird species with different patterns of vocalization, and it is a challenging task to deal with them. Previous studies have revealed that many authors focus on the segmentation of bird audio without considering specific patterns of bird vocalization. Based on the existing literature, currently there is no work on the segmentation of monosyllabic and multisyllabic birds, separately. Therefore, this research addresses the aforementioned concern and also proposes a collection of audio features named ‘Perceptual, Descriptive, and Harmonic Features (PDHFs)’ that gives promising results in the classification of bird vocalization. Moreover, the classification results improved when monosyllabic and multisyllabic birds were classified separately. To analyze the performance of PDHFs, different classifiers were used in which Artificial neural network (ANN) outperformed other classifiers and demonstrated an accuracy of 98%.

ACS Style

Abdullah Alghamdi; Tooba Mehtab; Rizwan Iqbal; Mona Leeza; Noman Islam; Mohammed Hamdi; Asadullah Shaikh. Automatic Classification of Monosyllabic and Multisyllabic Birds Using PDHF. Electronics 2021, 10, 624 .

AMA Style

Abdullah Alghamdi, Tooba Mehtab, Rizwan Iqbal, Mona Leeza, Noman Islam, Mohammed Hamdi, Asadullah Shaikh. Automatic Classification of Monosyllabic and Multisyllabic Birds Using PDHF. Electronics. 2021; 10 (5):624.

Chicago/Turabian Style

Abdullah Alghamdi; Tooba Mehtab; Rizwan Iqbal; Mona Leeza; Noman Islam; Mohammed Hamdi; Asadullah Shaikh. 2021. "Automatic Classification of Monosyllabic and Multisyllabic Birds Using PDHF." Electronics 10, no. 5: 624.

Journal article
Published: 05 October 2020 in Energies
Reads 0
Downloads 0

Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.

ACS Style

Nasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies 2020, 13, 5193 .

AMA Style

Nasir Ayub, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies. 2020; 13 (19):5193.

Chicago/Turabian Style

Nasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19: 5193.

Journal article
Published: 21 September 2020 in IEEE Access
Reads 0
Downloads 0

Due to the availability of fingerprint verification technology on mobile devices fingerprint based human identification has become the most widely used biometric technology in everyday life. The elderly find digital payment applications difficult to operate and unsecure. This work has exploited fingerprint verification availability on mobile devices to provide a user friendly and secure digital wallet payment facility to elderly who are unable to avail this facility due to the complex infrastructure of traditional authentication mechanisms. A novel digital payment mechanism is presented which uses Bluetooth technology of mobile devices for billing at the point of sale and fingerprint verification for user authentication. A proof of the concept study is presented to validate the proposed model using state of the art usability questionnaires and attributes with the target group. The result indicates that the proposed methodology provides satisfaction and ease of use to the user.

ACS Style

Sarwat Iqbal; Muhammad Irfan; Kamran Ahsan; Muhammad Azhar Hussain; Muhammad Awais; Muhammad Shiraz; Mohammed Hamdi; Abdullah Alghamdi. A Novel Mobile Wallet Model for Elderly Using Fingerprint as Authentication Factor. IEEE Access 2020, 8, 177405 -177423.

AMA Style

Sarwat Iqbal, Muhammad Irfan, Kamran Ahsan, Muhammad Azhar Hussain, Muhammad Awais, Muhammad Shiraz, Mohammed Hamdi, Abdullah Alghamdi. A Novel Mobile Wallet Model for Elderly Using Fingerprint as Authentication Factor. IEEE Access. 2020; 8 (99):177405-177423.

Chicago/Turabian Style

Sarwat Iqbal; Muhammad Irfan; Kamran Ahsan; Muhammad Azhar Hussain; Muhammad Awais; Muhammad Shiraz; Mohammed Hamdi; Abdullah Alghamdi. 2020. "A Novel Mobile Wallet Model for Elderly Using Fingerprint as Authentication Factor." IEEE Access 8, no. 99: 177405-177423.