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AlDahoul N, Karim HA, Momo MA, Escobar FIF, Magallanes VA, Tan MJT. Author Correction: Parasitic egg recognition using convolution and attention network. Sci Rep 2023; 13:15818. [PMID: 37740108 PMCID: PMC10516876 DOI: 10.1038/s41598-023-43068-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023] Open
Affiliation(s)
- Nouar AlDahoul
- Computer Science, New York University, Abu Dhabi, United Arab Emirates.
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
| | | | - Mhd Adel Momo
- Fleet Management Systems and Technologies, Istanbul, Turkey
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AlDahoul N, Momo MA, Chong KL, Ahmed AN, Huang YF, Sherif M, El-Shafie A. Streamflow classification by employing various machine learning models for peninsular Malaysia. Sci Rep 2023; 13:14574. [PMID: 37666880 PMCID: PMC10477249 DOI: 10.1038/s41598-023-41735-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 08/30/2023] [Indexed: 09/06/2023] Open
Abstract
Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting.
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Affiliation(s)
- Nouar AlDahoul
- Computer Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Mhd Adel Momo
- Fleet Management Systems & Technologies, Istanbul, Turkey
| | - K L Chong
- Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia
| | - Ali Najah Ahmed
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia.
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia.
| | - Yuk Feng Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sg. Long, Bandar Sg. Long, 43000, Kajang, Selangor, Malaysia
| | - Mohsen Sherif
- National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- Civil and Environmental Eng. Dept, College of Engineering, United Arab Emirates University, 15551, Al Ain, United Arab Emirates
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
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AlDahoul N, Karim HA, Momo MA, Escobar FIF, Magallanes VA, Tan MJT. Parasitic egg recognition using convolution and attention network. Sci Rep 2023; 13:14475. [PMID: 37660120 PMCID: PMC10475085 DOI: 10.1038/s41598-023-41711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 08/30/2023] [Indexed: 09/04/2023] Open
Abstract
Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.
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Affiliation(s)
- Nouar AlDahoul
- Computer Science, New York University, Abu Dhabi, United Arab Emirates.
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
| | | | - Mhd Adel Momo
- Fleet Management Systems and Technologies, Istanbul, Turkey
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Ibrahim H, AlDahoul N, Lee S, Rahwan T, Zaki Y. YouTube's recommendation algorithm is left-leaning in the United States. PNAS Nexus 2023; 2:pgad264. [PMID: 37601308 PMCID: PMC10433241 DOI: 10.1093/pnasnexus/pgad264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/11/2023] [Accepted: 07/31/2023] [Indexed: 08/22/2023]
Abstract
With over two billion monthly active users, YouTube currently shapes the landscape of online political video consumption, with 25% of adults in the United States regularly consuming political content via the platform. Considering that nearly three-quarters of the videos watched on YouTube are delivered via its recommendation algorithm, the propensity of this algorithm to create echo chambers and deliver extremist content has been an active area of research. However, it is unclear whether the algorithm may exhibit political leanings toward either the Left or Right. To fill this gap, we constructed archetypal users across six personas in the US political context, ranging from Far Left to Far Right. Utilizing these users, we performed a controlled experiment in which they consumed over eight months worth of videos and were recommended over 120,000 unique videos. We find that while the algorithm pulls users away from political extremes, this pull is asymmetric, with users being pulled away from Far Right content stronger than from Far Left. Furthermore, we show that the recommendations made by the algorithm skew left even when the user does not have a watch history. Our results raise questions on whether the recommendation algorithms of social media platforms in general, and YouTube, in particular, should exhibit political biases, and the wide-reaching societal and political implications that such biases could entail.
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Affiliation(s)
- Hazem Ibrahim
- Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE
| | - Nouar AlDahoul
- Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE
| | - Sangjin Lee
- Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE
| | - Talal Rahwan
- Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE
| | - Yasir Zaki
- Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE
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Lye MH, AlDahoul N, Abdul Karim H. Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective. Sensors (Basel) 2023; 23:6804. [PMID: 37571588 PMCID: PMC10422501 DOI: 10.3390/s23156804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/12/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023]
Abstract
Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person's activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP-TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
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Affiliation(s)
- Mohd Haris Lye
- Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia;
| | - Nouar AlDahoul
- Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia;
- Computer Science, New York University, Abu Dhabi P.O. Box 1291888, United Arab Emirates
| | - Hezerul Abdul Karim
- Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia;
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AlDahoul N, Karim HA, Momo MA. RGB-D based multi-modal deep learning for spacecraft and debris recognition. Sci Rep 2022; 12:3924. [PMID: 35273245 PMCID: PMC8913695 DOI: 10.1038/s41598-022-07846-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/25/2022] [Indexed: 11/23/2022] Open
Abstract
Recognition of space objects including spacecraft and debris is one of the main components in the space situational awareness (SSA) system. Various tasks such as satellite formation, on-orbit servicing, and active debris removal require object recognition to be done perfectly. The recognition task in actual space imagery is highly complex because the sensing conditions are largely diverse. The conditions include various backgrounds affected by noise, several orbital scenarios, high contrast, low signal-to-noise ratio, and various object sizes. To address the problem of space recognition, this paper proposes a multi-modal learning solution using various deep learning models. To extract features from RGB images that have spacecraft and debris, various convolutional neural network (CNN) based models such as ResNet, EfficientNet, and DenseNet were explored. Furthermore, RGB based vision transformer was demonstrated. Additionally, End-to-End CNN was used for classification of depth images. The final decision of the proposed solution combines the two decisions from RGB based and Depth-based models. The experiments were carried out using a novel dataset called SPARK which was generated under a realistic space simulation environment. The dataset includes various images with eleven categories, and it is divided into 150 k of RGB images and 150 k of depth images. The proposed combination of RGB based vision transformer and Depth-based End-to-End CNN showed higher performance and better results in terms of accuracy (85%), precision (86%), recall (85%), and F1 score (84%). Therefore, the proposed multi-modal deep learning is a good feasible solution to be utilized in real tasks of SSA system.
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Affiliation(s)
- Nouar AlDahoul
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia. .,YO-VIVO Corporation, Bacolod, Philippines.
| | | | - Mhd Adel Momo
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.,YO-VIVO Corporation, Bacolod, Philippines
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Barbon HCV, Fermin JL, Kee SL, Tan MJT, AlDahoul N, Karim HA. Going Electronic: Venturing Into Electronic Monitoring Systems to Increase Hand Hygiene Compliance in Philippine Healthcare. Front Pharmacol 2022; 13:843683. [PMID: 35250592 PMCID: PMC8892004 DOI: 10.3389/fphar.2022.843683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/01/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
| | - Jamie Ledesma Fermin
- Department of Electronics Engineering, University of St. La Salle, Bacolod, Philippines
| | - Shaira Limson Kee
- Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines
| | - Myles Joshua Toledo Tan
- Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines
- Department of Chemical Engineering, University of St. La Salle, Bacolod, Philippines
- *Correspondence: Myles Joshua Toledo Tan, ; Hezerul Abdul Karim,
| | - Nouar AlDahoul
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia
| | - Hezerul Abdul Karim
- Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia
- *Correspondence: Myles Joshua Toledo Tan, ; Hezerul Abdul Karim,
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Macariola AD, Santarin TMC, Villaflor FJM, Villaluna LMG, Yonzon RSL, Fermin JL, Kee SL, AlDahoul N, Karim HA, Tan MJT. Breaking Barriers Amid the Pandemic: The Status of Telehealth in Southeast Asia and its Potential as a Mode of Healthcare Delivery in the Philippines. Front Pharmacol 2021; 12:754011. [PMID: 34819860 PMCID: PMC8606793 DOI: 10.3389/fphar.2021.754011] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 10/25/2021] [Indexed: 11/21/2022] Open
Affiliation(s)
- Aitana Dy Macariola
- Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines
| | | | | | | | | | - Jamie Ledesma Fermin
- Yo-Vivo Corporation, Bacolod City, Philippines.,Department of Electronics Engineering, University of St. La Salle, Bacolod, Philippines
| | - Shaira Limson Kee
- Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines.,Yo-Vivo Corporation, Bacolod City, Philippines
| | - Nouar AlDahoul
- Yo-Vivo Corporation, Bacolod City, Philippines.,Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia
| | | | - Myles Joshua Toledo Tan
- Department of Natural Sciences, University of St. La Salle, Bacolod, Philippines.,Yo-Vivo Corporation, Bacolod City, Philippines.,Department of Chemical Engineering, University of St. La Salle, Bacolod, Philippines
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AlDahoul N, Essam Y, Kumar P, Ahmed AN, Sherif M, Sefelnasr A, Elshafie A. Suspended sediment load prediction using long short-term memory neural network. Sci Rep 2021; 11:7826. [PMID: 33837236 PMCID: PMC8035216 DOI: 10.1038/s41598-021-87415-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/26/2021] [Indexed: 11/09/2022] Open
Abstract
Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.
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Affiliation(s)
- Nouar AlDahoul
- Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia
| | - Yusuf Essam
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Pavitra Kumar
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (U.M.), 50603, Kuala Lumpur, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Mohsen Sherif
- National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates. .,Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, P.O.Box:15551,, Al Ain, United Arab Emirates.
| | - Ahmed Sefelnasr
- National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates
| | - Ahmed Elshafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (U.M.), 50603, Kuala Lumpur, Malaysia.,National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates
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