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184 results on '"Alqahtani, Fayez"'

1. Collaborative Team Recognition: A Core Plus Extension Structure

Author

Yu, Shuo, Alqahtani, Fayez, Tolba, Amr, Lee, Ivan, Jia, Tao, and Xia, Feng

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods.

Published
2024

26. Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0.

Author

Munshi, Manushi, Gupta, Rajesh, Jadav, Nilesh Kumar, Polkowski, Zdzislaw, Tanwar, Sudeep, Alqahtani, Fayez, and Said, Wael

Subjects
MACHINE learning, HEART failure, DRUG discovery, QUANTUM computing, IMAGE recognition (Computer vision)
Abstract

Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Investigating UAV-Based Applications in Indoor–Outdoor Sports Stadiums and Open-Air Gatherings for Different Interference Conditions beyond 5G Networks

Author

Gupta, Akhil, primary, Saini, Prakhar, additional, Teja, Banala Sharath, additional, Durgesh, Giddaluru Shiva, additional, Mishra, Shourabh Kumar, additional, Yadav, Anjani Kumar, additional, Tanwar, Sudeep, additional, Alqahtani, Fayez, additional, Raboaca, Maria Simona, additional, and Said, Wael, additional

Published
2023
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37. Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture.

Author

Patel, Jigna, Ruparelia, Anand, Tanwar, Sudeep, Alqahtani, Fayez, Tolba, Amr, Sharma, Ravi, Raboaca, Maria Simona, and Neagu, Bogdan Constantin

Subjects
CONVOLUTIONAL neural networks, PRECISION farming, OBJECT recognition (Computer vision), EGGPLANT, WEEDS, HERBICIDE application, HERBICIDES
Abstract

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images. With the help of object detection, the precise location of weeds from images can be achieved. The dataset is collected manually from a private farm in Gandhinagar, Gujarat, India. The combined approach of classification and object detection is applied in the proposed model. The Convolutional Neural Network (CNN) model is used to classify weed and non-weed images; further DL models are applied for object detection. We have compared DL models based on accuracy, memory usage, and Intersection over Union (IoU). ResNet-18, YOLOv3, CenterNet, and Faster RCNN are used in the proposed work. CenterNet outperforms all other models in terms of accuracy, i.e., 88%. Compared to other models, YOLOv3 is the least memory-intensive, utilizing 4.78 GB to evaluate the data. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Semantic-Rich Recommendation System for Medical Emergency Response System

Author

Karthika, R., Deborah, L., Zheng, Wenying, Alqahtani, Fayez, Tolba, Amr, Krishnan, B., and Bansal, Ritika

Abstract

The emergency response process consists of methodical and coordinated series of actions and protocols executed by individuals and organizations to proficiently address crises. When planning for medical emergencies, it is vital to work with responsive medical organizations to ensure good communication and coordination. Unlike e-government processes, emergency response processes are focused on knowledge and may frequently change as the emergency situation develops. It is important to change the emergency response plan for dynamic situations and the proposed method helps to create a flexible plan for emergency responses. The proposed approach uses a system for organizing knowledge to figure out the needs and the resources essential for an emergency. It helps to identify the organizations to be involved based on their rules for mutual aid and jurisdiction. Experimental analysis shows that the proposed method outperforms Smart-c and DCERP in suggesting a greater number of hospitals during medical emergency and achieves 0.8, 0.9 and 0.9 precision, recall, and f-measure approximately.

Published
2023
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43. F-LSTM: Federated learning-based LSTM framework for cryptocurrency price prediction.

Author

Patel, Nihar, Vasani, Nakul, Jadav, Nilesh Kumar, Gupta, Rajesh, Tanwar, Sudeep, Polkowski, Zdzislaw, Alqahtani, Fayez, and Gafar, Amr

Subjects
FEDERATED learning, CRYPTOCURRENCIES, PREDICTION models, MACHINE learning, COMPUTER simulation
Abstract

In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., F-LSTM utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of F-LSTM , we ran different empirical simulations. Our findings demonstrate that F-LSTM outperforms conventional approaches and machine learning techniques by achieving a loss minimal of 2.3 × 10 − 4 . Furthermore, the F-LSTM uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the F-LSTM requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (F-LSTM). In terms of data privacy and accuracy, F-LSTM addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Blockchain-Based Crop Recommendation System for Precision Farming in IoT Environment.

Author

Patel, Devangi Hitenkumar, Shah, Kamya Premal, Gupta, Rajesh, Jadav, Nilesh Kumar, Tanwar, Sudeep, Neagu, Bogdan Constantin, Attila, Simo, Alqahtani, Fayez, and Tolba, Amr

Subjects
RECOMMENDER systems, PRECISION farming, AGRICULTURE, INTERNET of things, PLANT growth, PLANT development
Abstract

In agriculture, soil is a vital element that decides the quality and yield of agricultural produce. Soil consists of various nutrients such as nitrogen (N), phosphorous (P), potassium (K), the potential of hydrogen (pH), and water content. Nitrogen is responsible for building chlorophyll, which helps produce proteins and thus directly contributes to plant growth and development. Phosphorous is needed to develop root systems and flowers, whereas potassium helps increase disease resistance. Each of these play a role in crop cultivation. Thus, in this research paper, considering the fact that soil health will provide farmers with the best selection of crops that are compatible with their farm's soil nutrients, we propose an algorithm for recommending a set of suitable crops based on various soil attributes. These soil nutrients can be collected in real-time using soil sensors, such as N, P, K, and pH, and humidity sensors. They can be deployed in farms where the cultivation takes place. These sensor readings would then be transferred to the blockchain layer, thereby validating the data and ensuring it is tamper-proof and evident. The crop recommendation model uses data from these sensors in real-time, increasing the results' accuracy. The last stage leads us to display these results via a user dashboard, which helps the farmers to keep in check with their farm's practices, and their sensor states from remote locations. [ABSTRACT FROM AUTHOR]

Published
2023
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