Papers by ABDULLAH ALSAEEDI
Applied Sciences
In this era of digital transformation, when the amount of scholarly literature is rapidly growing... more In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an ...
Multimedia Tools and Applications
Silicon and Nano-silicon in Environmental Stress Management and Crop Quality Improvement
Applied Sciences, 2022
The advent of social networks and micro-blogging sites online has led to an abundance of user-gen... more The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ impli...
Social Science Research Network, 2020
Background: The transfusion of COVID-19 convalescent plasma (CCP) is being considered to improve ... more Background: The transfusion of COVID-19 convalescent plasma (CCP) is being considered to improve clinical outcomes in patients with COVID-19 disease. We investigated the effectiveness of CCP therapy in patients withmoderate and severe COVID-19 disease. Methods: This non-randomized prospective cohort study was conducted from May 21, 2020, to June 30, 2020, at four major tertiary hospitals in Kuwait. CCP was administered to 135 patients. The control group comprised 233 patients who received standard treatment. All patients (N=368, median age 54 [range 15-82] years) had laboratory-confirmed SARS-CoV-2 infection and either moderate or severe COVID-19 disease. Findings: CCP treatment was significantly associated with a higher rate of clinical improvement in patients with moderate or severe disease. Among those with moderate COVID-19 disease, time to clinical improvement was 7 daysin the CCP group versus 8 days in the control group (p=0·006). For severe COVID-19 disease, time to clinical...
International Journal of Infectious Diseases, 2021
To study the effectiveness of COVID-19 convalescent plasma (CCP) therapy for patients with modera... more To study the effectiveness of COVID-19 convalescent plasma (CCP) therapy for patients with moderate and severe COVID-19 disease. Methods: This non-randomized prospective cohort study was conducted from May 21 to June 30, 2020, at four major tertiary hospitals in Kuwait. CCP was administered to 135 patients. The control group comprised 233 patients who received standard treatment. All patients (N = 368, median age 54 [range 15-82]) had laboratory-confirmed SARS-CoV-2 infection and either moderate or severe COVID-19 disease. Results: CCP treatment was associated with a higher rate of clinical improvement in patients with moderate or severe disease. Among those with moderate COVID-19 disease, time to clinical improvement was 7 days in the CCP group, versus 8 days in the control group (p = 0Á006). For severe COVID-19 disease, time to clinical improvement was 7 days in the CCP group, versus 15.5 days in the control group (p = 0Á003). In the adjusted analysis, patients with moderate disease treated with CCP had a significantly lower 30-day mortality rate. Compared to the control group, oxygen saturation improved within 3 days of CCP transfusion, and lymphocyte counts improved from day 7 in patients with moderate COVID-19 disease and day 11 in patients with severe disease. C-reactive protein levels declined throughout the first 14 days after CCP transfusion. None of the CCP patients developed a serious transfusion reaction. Conclusions: The data show that administration of CCP is a safe treatment option for patients with COVID-19 disease with a favorable outcome in the rate of, and time to, clinical improvement.
Accurate prediction of the leaching requirements (Lr) of crops and striving to attain them is ess... more Accurate prediction of the leaching requirements (Lr) of crops and striving to attain them is essential for efficient irrigation water use. Solute modeling was extended to develop four Lr conceptual models that do not neglect solute reactions in the root-zone, surface evaporation, and the influence of immobile wetted pore space. The models were based on: (i) the water movement equation
European Neurology, 1991
Restless legs syndrome is a frequent neurological disorder with potentially serious and highly di... more Restless legs syndrome is a frequent neurological disorder with potentially serious and highly distressing treatment complications. The role and potential implications of periodic leg movements during sleep range from being a genetic risk marker for restless legs syndrome to being a cardiovascular risk factor. The diagnosis of restless legs syndrome in patients with daytime movement disorders is challenging and restless legs syndrome needs to be differentiated from other sleep-related movement disorders. This article provides an update on the diagnosis of restless legs syndrome as an independent disorder and the role of periodic leg movements and reviews the association of restless legs syndrome with Parkinson's disease and other movement disorders. V
Archives of General Internal Medicine, 2018
Background: Allergic rhinitis is prevalent co-morbidity among asthmatic patients. The literature ... more Background: Allergic rhinitis is prevalent co-morbidity among asthmatic patients. The literature has suggested that it is responsible for poor asthma control. Also, it was found to be an important risk factor for developing of asthma in general population. In Kuwait there are no previous studies that estimated the prevalence of such an association. This study conducted to evaluate the prevalence of allergic rhinitis on adult bronchial asthma in a single center in Kuwait and to examine other common associations. Material and methods: This is a cross sectional study in a single center to evaluate the prevalence and allergic rhinitis and its effect on asthma management among adults. The data was collected through a distribution of an online questionnaire the score for allergic rhinitis (SFAR) to known asthmatic patients who were diagnosed previously with bronchial asthma in a period of six weeks between January and February 2018. Analyzed, discussed and compared to other relevant study. Results: 494 asthmatic patients participated in the questionnaire in a period of 6 weeks. 74.7% were females and 25.3% was males. 39.7% of patients were obese and 13% were smokers. The vast majority of asthmatic patients experienced symptoms of allergic rhinitis in the past 12 months. The most common symptoms experienced were; sneezing (48%), nose itching (45.5%), and cough (35.6%), moreover, 34.4% had runny nose. and 20.9% have had their eyes itching. Interestingly sneezing showed a decreasing prevalence as getting older among males but not females, also runny nose showed biphasic course among age groups. Two thirds (69.4%) of the patients were not using any rhinitis medications. Conclusion: Allergic rhinitis is a common co-morbidity among asthmatic adult patients in our study in Kuwait. The vast majority of patients were not using their rhinitis treatment. Better patient education and adherence to treatment is warranted. Further studies needed to assess the impact of rhinitis treatment on asthma control in Kuwait.
Background: Tocilizumab, through the blocking of interleukin-6 receptors, is hypothesized to be e... more Background: Tocilizumab, through the blocking of interleukin-6 receptors, is hypothesized to be effective in the treatment of severe coronavirus disease 2019 (COVID-19). However, data on tocilizumab use in this setting is conflicting. Objective: To evaluate the effect of tocilizumab treatment on the outcomes of patients with severe COVID-19 pneumonia. Methods: A retrospective case-control study including 168 patients hospitalized with severe COVID-19 pneumonia at Al-Jahra hospital, Kuwait, from April 1st to June 30th. All patients received standard of care treatment. Patients in the tocilizumab group received tocilizumab infusion. The primary outcomes were death, need for mechanical ventilation and the proportion of patients with clinical improvement. The incidence of adverse events was monitored. Results: Need for mechanical ventilation was higher in the tocilizumab group (48% vs. 24.3%, p=0.002). Clinical improvement was lower (54.1% vs. 71.4%) and number of deaths were higher (29.6% vs. 10%) in the tocilizumab group (p=0.009). There was no difference in the mean survival time between tocilizumab and control group (17.7 days vs. 19.2 days). There was no significant difference between the two groups regarding the incidence of infections (6.1% vs. 2.9%, p=0.471). Conclusion: Our results suggest that use of tocilizumab in severe COVID-19 pneumonia does not provide clinical or mortality benefit. However, further research is needed to determine the ideal utilization of tocilizumab in the setting of severe COVID-19 disease.
2019 International Conference on Computer and Information Sciences (ICCIS), 2019
Internet of Things (IoT), Wireless sensor and actuator networks (WSANs) share a great mashup rela... more Internet of Things (IoT), Wireless sensor and actuator networks (WSANs) share a great mashup relation in the current communication technology trend. The number of such deployments has grown exponentially in the past few years. This growth leads the researchers to analyze, design and deploy such setups for numerous application areas ranging from simple daily life scenarios to large-scale scientific application areas. The present pace of IoT and WSAN mashup predict the interconnectivity of tens of billions of devices in the near future. Researchers are presently focused on the various aspects of IoT and WSAN mashup including basic infrastructures, heterogeneity of WSAN nodes, security aspects, data fusion etc. This paper presents a work in which an adhoc testbed has been designed to analyze various performance metrics by real experiments rather than simulations. The testbed begins with the design of basic infrastructure of WSAN nodes to gateways and its integration with the Internet, treating each WSAN node as a thing in the IoT framework. This paper describes a practical implementation of an IoT testbed as a complete framework. The testbed construction includes the design and implementation of hardware and software components of the system. Experiments have been conducted to show the results of the web performance of the testbed using different communication technologies and hardware platforms.
2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), 2021
Software testing is a time-consuming and costly task, as it involves testing all software modules... more Software testing is a time-consuming and costly task, as it involves testing all software modules. To minimize the cost and effort of software testing, automatic defect detection can be used to identify the defective modules during the early stages. These aid software testers in detecting the modules that require intensive testing. Therefore, automatically predicting software defects has become a critical factor in software engineering. This paper explores the existing methods and techniques on software defect prediction (SDP) and lists the most popular datasets that are used as benchmarks in SDP. In addition, it discusses the approaches to overcome the class imbalance problem, which usually occurs in the benchmark datasets for SDP problems. This paper can be helpful for researchers in software engineering and other related areas.
IEEE Access, 2021
It has been proven that Internet of Things (IoT) platforms can improve the performance and effici... more It has been proven that Internet of Things (IoT) platforms can improve the performance and efficiency of a wide range of processes. With the acceptance of IoT as a major part of the technology of Industry 4.0, the notion of leveraging the Internet in industries to enable automation and reconfigure existing industrial processes has greatly evolved. By introducing smart technology and intelligent processes, the Industrial Internet of Things (IIoT) is committed to bringing high operational efficiency, enhanced productivity, and effective management to industrial assets. Despite this, the reliance of IIoT on central architecture presents numerous challenges, including the security and maintenance of smart devices, privacy issues owing to third-party participation, and massive computations conducted by a central entity, all of which prevent its widespread adoption in businesses. Emerging blockchain technologies have the potential to transform IIoT platforms and applications. A distributed and decentralized approach followed by blockchain might offer interesting solutions to the challenges raised by IIoT. Furthermore, 5G networks are expected to deliver excellent solutions to meet the demands of decentralized systems, with a focus on application-specific vulnerabilities. Blockchain and IIoT, enabled by 5G, is a viable option to fully explore the potential of contemporary industry. In this context, this article analyzes and examines recent achievements to highlight the major obstacles in blockchain-IIoT convergence and presents a framework for potential solutions. Directions for the future are also provided and intend to assist researchers in understanding the full potential of these innovations.
SIL Proceedings, 1922-2010, 1978
Journal of Ambient Intelligence and Smart Environments, 2021
Due to the increase in the global aging population and its associated age-related challenges, var... more Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two data...
Labelled-transition systems (LTS) are widely used by developers and testers to model software sys... more Labelled-transition systems (LTS) are widely used by developers and testers to model software systems in terms of their sequential behaviour. They provide an overview of the behaviour of the system and their reaction to different inputs. LTS models are the foundation for various automated verification techniques such as model-checking and model-based testing. These techniques require up-to-date models to be meaningful. Unfortunately, software models are rare in practice. Due to the effort and time required to build these models manually, a software engineer would want to infer them automatically from traces (sequences of events or function calls). Many techniques have focused on inferring LTS models from given traces of system execution, where these traces are produced by running a system on a series of tests. State-merging is the foundation of some of the most successful LTS inference techniques to construct LTS models. Passive inference approaches such as k-tail and Evidence-Drive...
For text clustering task, distinctive text features selection is important due to feature space h... more For text clustering task, distinctive text features selection is important due to feature space high dimensionality. It is essential to reduce the feature space dimension to increase accuracy and decrease processing time. In this work, for text clustering task, we introduce a novel hybrid feature selection model. This method measures the term importance based on the correlation coefficient among four term weighting techniques. All terms in the feature parameter vector are ranked based on this correlation coefficient score. Then low score terms are filtered out. Clustering technique is applied on the feature parameter vectors after filtering step. The proposed method results show its superiority over the traditional feature selection approaches.
Applied Sciences, 2021
Spreading rumors in social media is considered under cybercrimes that affect people, societies, a... more Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engine...
Journal of Computer Science, 2021
Social media platforms are extensively used in exchanging and sharing information and user experi... more Social media platforms are extensively used in exchanging and sharing information and user experience, thereby resulting in massive outspread and viewing of personal experiences in many fields of life. Thus, informative health-related videos on YouTube are highly perceptible. Many users tend to procure medical treatments and health-related information from social media particularly from YouTube when searching for chronic illness treatments. Sometimes, these sources contain misinformation that cause fatal effects on the users' health. Many sentimental analyses and classifications have been conducted on social media platforms to study user post and comments on many life science fields. However, no study has been conducted on the analysis of Arabic user comments, which provide details on herbal treatments for people with diabetes. Therefore, this study proposes a model to detect and discover emotions/opinions of YouTube users on herbal treatment videos is proposed through an analysis of user comments by using machine learning classifiers. In addition, a new Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), which is based on user comments from several YouTube videos, is introduced. This study examines the impact of four representation methods on ADHTD to show the performance of machine learning classifiers. These methods remove repeating characters in Arabic dialect and character extension known as 'TATAWEEL' or 'MAD', stemming of Arabic words, Arabic stop words removal and N-grams with Arabic words. Experiments has been conducted based aforementioned methods to handle imbalanced proposed dataset and identify the best machine learning classifiers over Arabic dialect textual data. The model has achieved a higher accuracy that reached 95% when using Synthetic Minority Oversampling TEchnique (SMTOE) techniques to balanced dataset than imbalanced dataset.
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Papers by ABDULLAH ALSAEEDI