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Articles 1 - 30 of 436
Full-Text Articles in Physical Sciences and Mathematics
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
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In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
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Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
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This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding …
Enhanced Route Navigation Control System For Turtlebot Using Human-Assisted Mobility And 3-D Slam Optimization, Ankit Kumar, Kamred Udham Singh, Pankaj Dadheech, Aditi Sharma, Ahmed I. Alutaibi, Ahed Abugabah, Arwa Mohsen Alawajy
Enhanced Route Navigation Control System For Turtlebot Using Human-Assisted Mobility And 3-D Slam Optimization, Ankit Kumar, Kamred Udham Singh, Pankaj Dadheech, Aditi Sharma, Ahmed I. Alutaibi, Ahed Abugabah, Arwa Mohsen Alawajy
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An autonomous, power-assisted Turtlebot is presented in this paper in order to enhance human mobility. The turtlebot moves from its initial position to its final position at a predetermined speed and acceleration. We propose an intelligent navigation system that relies solely on individual instructions. When there is no individual present, the Turtlebot remains stationary. Turtlebot utilizes a rotating Kinect sensor in order to perceive its path. Various angles were examined in order to demonstrate the effectiveness of the system in experiments conducted on a U-shaped experimental pathway. The Turtlebot was used as an experimental device during these trials. Based on …
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
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Rain attenuation poses a significant challenge for high-throughput communication systems. In response, this paper introduces an artificial intelligence (AI) model designed for predicting and mitigating rain-induced impairments in high-throughput satellite (HTS) to land channels. The model is based on three AI algorithms developed using 3D antenna design to characterize, analyze, and mitigate rain-induced attenuation, optimizing channel quality specifically in the United Arab Emirates (UAE). The study evaluates various parameters, including rain-specific attenuation, effective slant path through rain, rain-induced attenuation, signal carrier-to-noise ratio, and symbol error rate, for five conventional modulation schemes: Quadrature Phase-Shift Keying (QPSK), 8-Phase Shift Keying (8-PSK), 16-Quadrature …
Blood Cell Image Segmentation And Classification: A Systematic Review, Muhammad Shahzad, Farman Ali, Syed Hamad Shirazi, Assad Rasheed, Awais Ahmad, Babar Shah, Daehan Kwak
Blood Cell Image Segmentation And Classification: A Systematic Review, Muhammad Shahzad, Farman Ali, Syed Hamad Shirazi, Assad Rasheed, Awais Ahmad, Babar Shah, Daehan Kwak
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Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking …
Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno
Dataset Of Arabic Spam And Ham Tweets, Sanaa Kaddoura, Safaa Henno
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This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for …
Developing A Success Model Of A Social Student Relationship Management System, Wasef Mater, Monther Aldwairi, Nasim Matar, Waleed Al-Rahmi
Developing A Success Model Of A Social Student Relationship Management System, Wasef Mater, Monther Aldwairi, Nasim Matar, Waleed Al-Rahmi
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Social media's significance in higher education has increased due to its capacity to enhance participation, communication, teamwork, and information sharing. Important notifications, updates, and reminders can be promptly received by all members of the university community, assuring that information is shared with everyone. The objective of this study is to develop a model for a Customer Relationship Management (CRM) system in higher education that is based on social media and intends to increase student satisfaction, loyalty, and profitability. It blends the idea of trust with Delone Mclean success model. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the …
Advancing Cognitive Accessibility: The Role Of Artificial Intelligence In Enhancing Inclusivity, Rukiya Deetjen-Ruiz, Marjorie P Daniel, Jennie Telus, Lodz Deetjen
Advancing Cognitive Accessibility: The Role Of Artificial Intelligence In Enhancing Inclusivity, Rukiya Deetjen-Ruiz, Marjorie P Daniel, Jennie Telus, Lodz Deetjen
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This editorial examines the transformative role of Artificial Intelligence (AI) in enhancing cognitive accessibility for neurodiverse individuals. It explores the evolution from conventional assistive technologies to sophisticated AI-driven solutions, highlighting how these advancements are reshaping inclusivity in education and the workplace. The piece critically analyzes the benefits and challenges of AI in this context, considering ethical implications, user-centered design, and the need for equitable access. It concludes with a call to action for continued innovation and collaboration in developing AI technologies that truly cater to the diverse needs of neurodiverse individuals.
Understanding Trust Drivers Of S-Commerce, Mousa Al-Kfairy, Ahmed Shuhaiber, Ayman Wael Al-Khatib, Saed Alrabaee, Souheil Khaddaj
Understanding Trust Drivers Of S-Commerce, Mousa Al-Kfairy, Ahmed Shuhaiber, Ayman Wael Al-Khatib, Saed Alrabaee, Souheil Khaddaj
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Trust has emerged as a pillar in the acceptance and use of new technologies in the ever-changing digital landscape, notably in the booming field of social commerce. The importance of this study lies in the fact that it explores in-depth the aspects of customer trust in Instashopping using new constructs that have yet to be explored in s-commerce literature. Focusing on Instashopping, the research proposed a multi-dimensional model of trust to examine the dynamics of user trust in social commerce platforms and analyses the effects of various factors, including institution-based trust, disposition to trust, personal inventiveness, perceived page quality, and …
Advancing The Understanding Of Clinical Sepsis Using Gene Expression–Driven Machine Learning To Improve Patient Outcomes, Asrar Rashid, Feras Al-Obeidat, Wael Hafez, Govind Benakatti, Rayaz A. Malik, Christos Koutentis, Javed Sharief, Joe Brierley, Nasir Quraishi, Zainab A. Malik, Arif Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, M. Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
Advancing The Understanding Of Clinical Sepsis Using Gene Expression–Driven Machine Learning To Improve Patient Outcomes, Asrar Rashid, Feras Al-Obeidat, Wael Hafez, Govind Benakatti, Rayaz A. Malik, Christos Koutentis, Javed Sharief, Joe Brierley, Nasir Quraishi, Zainab A. Malik, Arif Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, M. Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information …
Enhancedbert: A Feature-Rich Ensemble Model For Arabic Word Sense Disambiguation With Statistical Analysis And Optimized Data Collection, Sanaa Kaddoura, Reem Nassar
Enhancedbert: A Feature-Rich Ensemble Model For Arabic Word Sense Disambiguation With Statistical Analysis And Optimized Data Collection, Sanaa Kaddoura, Reem Nassar
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Accurate assignment of meaning to a word based on its context, known as Word Sense Disambiguation (WSD), remains challenging across languages. Extensive research aims to develop automated methods for determining word senses in different contexts. However, the literature lacks the presence of datasets generated for the Arabic language WSD. This paper presents a dataset comprising a hundred polysemous Arabic words. Each word in the dataset encompasses 3–8 distinct senses, with ten example sentences per sense. Some statistical operations are conducted to gain insights into the dataset, enlightening its characteristics and properties. Subsequently, a novel WSD approach is proposed to utilize …
Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim
Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim
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Real-time systems mostly interact with the external world and each input operation must meet predetermined deadlines to be useful. However, in many real-time applications, a partial result is also acceptable. We developed a reward-based mixed criticality system based on the resource reservation approach to address the problem of ensuring the effective execution of low- and high-criticality tasks in both low- and high modes, even under heavy workloads. Using dedicated servers with pessimistic resource allocation for each high criticality task ensured their execution in both modes unaffected by low criticality tasks. The surplus resources are reclaimed and assigned to low critical …
Advancing Temporal Sepsis Biomarking: Covariate Vascular Endothelial Growth Factor A And B Gene Expression Profiling In A Murine Model Of Sars-Cov Infection, Asrar Rashid, Feras Al-Obeidat, Kesava Ramakrishnan, Wael Hafez, Nouran Hamza, Zainab A. Malik, Raziya Kadwa, Muneir Gador, Govind Benakatti, Rayaz A. Malik, Ibrahim Elbialy, Hekmieh Manad, Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
Advancing Temporal Sepsis Biomarking: Covariate Vascular Endothelial Growth Factor A And B Gene Expression Profiling In A Murine Model Of Sars-Cov Infection, Asrar Rashid, Feras Al-Obeidat, Kesava Ramakrishnan, Wael Hafez, Nouran Hamza, Zainab A. Malik, Raziya Kadwa, Muneir Gador, Govind Benakatti, Rayaz A. Malik, Ibrahim Elbialy, Hekmieh Manad, Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain
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The limited specificity of standard inflammatory biomarkers poses a challenge for the diagnosis and monitoring of sepsis. The differential gene expression patterns of Vascular Endothelial Growth Factor A and B (VEGF-A and B) are promising candidates. This study aimed to elucidate variations in VEGF-A/B gene expression following SARS-CoV MA15 disease initiation. Biomarker tracking was examined in a murine C57BL wild-type (WT) genotype MA15 (SARS-CoV) nasal instillation model. In [GSE40824], the expression of TNF and VEGF-A significantly differed between the groups (p = 1.53e-07, and 0.0043) and over time. In [GSE40827], [GSE51386], [GSE51387], and [GSE40840], the expression of TNF, VEGF-A, and …
The Metaverse, Religious Practice And Wellbeing: A Narrative Review, Justin Thomas, Mohammad Amin Kuhail, Fahad Albeyahi
The Metaverse, Religious Practice And Wellbeing: A Narrative Review, Justin Thomas, Mohammad Amin Kuhail, Fahad Albeyahi
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The metaverse is touted as the next phase in the evolution of the Internet. This emerging digital ecosystem is widely conceptualized as a persistent matrix of interconnected multiuser, massively scaled online environments optimally experienced through immersive digital technologies such as virtual reality (VR). Much of the prognostication about the social implications of the metaverse center on secular activities. For example, retail, entertainment (gaming/concerts), and social networking. Little attention has been given to how the metaverse might impact religion. This narrative review explores contemporary research into online religious practice and the use of immersive digital technologies for religious purposes. This focus …
Informing The State Of Process Modeling And Automation Of Blood Banking And Transfusion Services Through A Systematic Mapping Study, Shaima' Abdallah Elhaj, Yousra Odeh, Dina Tbaishat, Anwar Rjoop, Asem Mansour, Mohammed Odeh
Informing The State Of Process Modeling And Automation Of Blood Banking And Transfusion Services Through A Systematic Mapping Study, Shaima' Abdallah Elhaj, Yousra Odeh, Dina Tbaishat, Anwar Rjoop, Asem Mansour, Mohammed Odeh
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Purpose: The current state of the art in process modeling of blood banking and transfusion services is not well grounded; methodological reviews are lacking to bridge the gap between such blood banking and transfusion processes (and their models) and their automation. This research aims to fill this gap with a methodological review. Methods: A systematic mapping study was adopted, driven by five key research questions. Identified research studies were accepted based on fulfilling the following inclusion criteria: 1) research studies should focus on blood banking and transfusion process modeling since the late 1970s; and 2) research studies should focus on …
Rolling The Crypto Dice: The Interplay Of Legal Environments, Market Uncertainty, And Gambling Attitudes On Users’ Behavioral Intentions, Ayman Abdalmajeed Alsmadi, Ahmed Shuhaiber, Khaled Saleh Al-Omoush
Rolling The Crypto Dice: The Interplay Of Legal Environments, Market Uncertainty, And Gambling Attitudes On Users’ Behavioral Intentions, Ayman Abdalmajeed Alsmadi, Ahmed Shuhaiber, Khaled Saleh Al-Omoush
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The high volatility and inherent high-risk nature of cryptocurrency investments promote the study of the determinants of value perception and the various factors influencing individuals’ intentions regarding whether to adopt, abstain from, or continue their investments in these dynamic cryptocurrency markets. The main aim of this study is to examine the determinants of behavioral intention to continue using cryptocurrencies. In addition, it is aimed at exploring the effect of gambling attitudes on the perceived benefits and legal environment in the cryptocurrency context. An online questionnaire was developed in order to gather data from 258 respondents in the United Arab Emirates …
Role Of Authentication Factors In Fin-Tech Mobile Transaction Security, Habib Ullah Khan, Muhammad Sohail, Shah Nazir, Tariq Hussain, Babar Shah, Farman Ali
Role Of Authentication Factors In Fin-Tech Mobile Transaction Security, Habib Ullah Khan, Muhammad Sohail, Shah Nazir, Tariq Hussain, Babar Shah, Farman Ali
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Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues …
On Hierarchical Clustering-Based Approach For Rddbs Design, Hassan I. Abdalla, Ali A. Amer, Sri Devi Ravana
On Hierarchical Clustering-Based Approach For Rddbs Design, Hassan I. Abdalla, Ali A. Amer, Sri Devi Ravana
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Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to …
Boosting The Item-Based Collaborative Filtering Model With Novel Similarity Measures, Hassan I. Abdalla, Ali A. Amer, Yasmeen A. Amer, Loc Nguyen, Basheer Al-Maqaleh
Boosting The Item-Based Collaborative Filtering Model With Novel Similarity Measures, Hassan I. Abdalla, Ali A. Amer, Yasmeen A. Amer, Loc Nguyen, Basheer Al-Maqaleh
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Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior …
Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain
Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain
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In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal …
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh
Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh
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The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …
Learning Heterogeneous Subgraph Representations For Team Discovery, Radin Hamidi Rad, Hoang Nguyen, Feras Al-Obeidat, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta, Fattane Zarrinkalam
Learning Heterogeneous Subgraph Representations For Team Discovery, Radin Hamidi Rad, Hoang Nguyen, Feras Al-Obeidat, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta, Fattane Zarrinkalam
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The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from a heterogeneous collaboration network where the …
Customer Churn Prediction Using Composite Deep Learning Technique, Asad Khattak, Zartashia Mehak, Hussain Ahmad, Muhammad Usama Asghar, Muhammad Zubair Asghar, Aurangzeb Khan
Customer Churn Prediction Using Composite Deep Learning Technique, Asad Khattak, Zartashia Mehak, Hussain Ahmad, Muhammad Usama Asghar, Muhammad Zubair Asghar, Aurangzeb Khan
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Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract …
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis, Martin Kaltdorf, Tim Breitenbach, Stefan Karl, Maximilian Fuchs, David Komla Kessie, Eric Psota, Martina Prelog, Edita Sarukhanyan, Regina Ebert, Franz Jakob, Gudrun Dandekar, Muhammad Naseem, Chunguang Liang, Thomas Dandekar
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis, Martin Kaltdorf, Tim Breitenbach, Stefan Karl, Maximilian Fuchs, David Komla Kessie, Eric Psota, Martina Prelog, Edita Sarukhanyan, Regina Ebert, Franz Jakob, Gudrun Dandekar, Muhammad Naseem, Chunguang Liang, Thomas Dandekar
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The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity …
Intelligent Biomedical Image Classification In A Big Data Architecture Using Metaheuristic Optimization And Gradient Approximation, Laila Almutairi, Ahed Abugabah, Hesham Alhumyani, Ahmed A. Mohamed
Intelligent Biomedical Image Classification In A Big Data Architecture Using Metaheuristic Optimization And Gradient Approximation, Laila Almutairi, Ahed Abugabah, Hesham Alhumyani, Ahmed A. Mohamed
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Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. Computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary …
Towards Designing A Knowledge Sharing System For Higher Learning Institutions In The Uae Based On The Social Feature Framework, S. M. F. D. Syed Mustapha, Edmund Evangelista, Farhi Marir
Towards Designing A Knowledge Sharing System For Higher Learning Institutions In The Uae Based On The Social Feature Framework, S. M. F. D. Syed Mustapha, Edmund Evangelista, Farhi Marir
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Numerous ICT instruments, such as communication tools, social media platforms, and collaborative software, bolster and facilitate knowledge sharing activities. Determining the vital success factors for knowledge sharing within its unique context is argued to be essential before implementing it. Therefore, it is imperative to define domain-specific critical success factors when envisioning the design of a knowledge sharing system. This research paper introduces the blueprint for an Academic Knowledge Sharing System (AKSS), rooted in an essential success framework tailored to knowledge sharing to deploy within an academic institution. In this regard, an extensive exploration of the relevant literature led to the …
Migrating 120,000 Legacy Publications From Several Systems Into A Current Research Information System Using Advanced Data Wrangling Techniques, Yrjö Lappalainen, Matti Lassila, Tanja Heikkilä, Jani Nieminen, Tapani Lehtilä
Migrating 120,000 Legacy Publications From Several Systems Into A Current Research Information System Using Advanced Data Wrangling Techniques, Yrjö Lappalainen, Matti Lassila, Tanja Heikkilä, Jani Nieminen, Tapani Lehtilä
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This article describes a complex CRIS (current research information system) implementation project involving the migration of around 120,000 legacy publication records from three different systems. The project, undertaken by Tampere University, encountered several challenges in data diversity, data quality, and resource allocation. To handle the extensive and heterogenous dataset, innovative approaches such as machine learning techniques and various data wrangling tools were used to process data, correct errors, and merge information from different sources. Despite significant delays and unforeseen obstacles, the project was ultimately successful in achieving its goals. The project served as a valuable learning experience, highlighting the importance …
Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Mohammad Tubishat, Husam Jasim Mohammed
Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Mohammad Tubishat, Husam Jasim Mohammed
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In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With …
Deep Learning For Plant Bioinformatics: An Explainable Gradient-Based Approach For Disease Detection, Muhammad Shoaib, Babar Shah, Nasir Sayed, Farman Ali, Rafi Ullah, Irfan Hussain
Deep Learning For Plant Bioinformatics: An Explainable Gradient-Based Approach For Disease Detection, Muhammad Shoaib, Babar Shah, Nasir Sayed, Farman Ali, Rafi Ullah, Irfan Hussain
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Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a …