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Articles 241 - 270 of 677
Full-Text Articles in Computer Sciences
Assessing The Impact Of Chatbot-Human Personality Congruence On User Behavior: A Chatbot-Based Advising System Case, Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, Joao Negreiros
Assessing The Impact Of Chatbot-Human Personality Congruence On User Behavior: A Chatbot-Based Advising System Case, Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, Joao Negreiros
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Chatbot personality has been demonstrated to influence user behavior, such as trust and intended engagement. However, previous research on chatbot-user personality congruence’s influence on user behavior is scant despite its significance in human-human conversations. This study explores the effect of chatbot-human personality trait congruence on user behavior in the context of a chatbot-based advising system. In this study, 54 college students interacted with chatbots with three different personalities (extraversion, agreeableness, and conscientiousness) and rated their trust, usage intention, and intended engagement with the chatbots. Additionally, 18 participants were interviewed to gain further insights into their perceptions and evaluations of the …
A Reputation-Based Aodv Protocol For Blackhole And Malfunction Nodes Detection And Avoidance, Qussai M. Yaseen, Monther Aldwairi, Ahmad Manasrah
A Reputation-Based Aodv Protocol For Blackhole And Malfunction Nodes Detection And Avoidance, Qussai M. Yaseen, Monther Aldwairi, Ahmad Manasrah
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Enhancing the security of Wireless Sensor Networks (WSNs) improves the usability of their applications. Therefore, finding solutions to various attacks, such as the blackhole attack, is crucial for the success of WSN applications. This paper proposes an enhanced version of the AODV (Ad Hoc On-Demand Distance Vector) protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs, thereby avoiding them when delivering packets. The proposed version employs a network-based reputation system to select the best and most secure path to a destination. To achieve this goal, the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast …
A Multi-Objective Grey Wolf Optimizer For Energy Planning Problem In Smart Home Using Renewable Energy Systems, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Feras Al-Obeidat, Osama Ahmad Alomari, Ammar Kamal Abasi, Mohammad Tubishat, Zenab Elgamal, Waleed Alomoush
A Multi-Objective Grey Wolf Optimizer For Energy Planning Problem In Smart Home Using Renewable Energy Systems, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Feras Al-Obeidat, Osama Ahmad Alomari, Ammar Kamal Abasi, Mohammad Tubishat, Zenab Elgamal, Waleed Alomoush
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This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar …
Digraph Enabled Digital Twin And Label-Encoding Machine Learning For Scada Network's Cyber Attack Analysis In Industry 5.0, Nabeel Al-Qirim, Anoud Bani-Hani, Munir Majdalawieh, Hussam Al Hamadi, Mohammad Kamrul Hasan
Digraph Enabled Digital Twin And Label-Encoding Machine Learning For Scada Network's Cyber Attack Analysis In Industry 5.0, Nabeel Al-Qirim, Anoud Bani-Hani, Munir Majdalawieh, Hussam Al Hamadi, Mohammad Kamrul Hasan
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False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) on SCADA (Supervisory Control and Data Acquisition) networks impact industry 5.0 enabled smart grid components such as intelligent-electronic-device (IED), circuit-breaker, network-switch, and power transmission lines. Since the SCADA-network-based cyber-attacking flow is not in digital-twin form, it is impossible to simulate the effects of the attack. Furthermore, the string nature of these affected components' data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel "Digital Twin and Machine …
Generative Ai And Large Language Models: A New Frontier In Reverse Vaccinology, Kadhim Hayawi, Sakib Shahriar, Hany Alashwal, Mohamed Adel Serhani
Generative Ai And Large Language Models: A New Frontier In Reverse Vaccinology, Kadhim Hayawi, Sakib Shahriar, Hany Alashwal, Mohamed Adel Serhani
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Reverse vaccinology is an emerging concept in the field of vaccine development as it facilitates the identification of potential vaccine candidates. Biomedical research has been revolutionized with the recent innovations in Generative Artificial Intelligence (AI) and Large Language Models (LLMs). The intersection of these two technologies is explored in this study. In this study, the impact of Generative AI and LLMs in the field of vaccinology is explored. Through a comprehensive analysis of existing research, prospective use cases, and an experimental case study, this research highlights that LLMs and Generative AI have the potential to enhance the efficiency and accuracy …
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 …
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 …
Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani
Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani
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This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features …
Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma
Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma
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This paper addresses the critical challenge of fraud detection in medical insurance claims, a pervasive issue causing significant financial losses in healthcare. The primary goal is to develop an advanced fraud detection approach by integrating multiple unsupervised machines learning algorithms, leveraging their collective strengths through a majority voting mechanism, where labelling of data is unavailable. Central to this approach is the ensemble of 18 novel unsupervised algorithms, specifically, anomaly detection models. The novelty lies in the majority voting system employed to aggregate the decisions from these diverse algorithms, enhancing the reliability and accuracy of fraud detection. To validate the effectiveness …
Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex
Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex
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This paper proposes a novel IoT-based Medication Adherence System to combat the pervasive issue of non-adherence among chronic disease patients. This system leverages real-time monitoring and timely reminders to improve medication intake and, consequently, patient outcomes. We delve into the factors behind non-adherence and explore how IoT technology can empower patient education and alleviate medication anxieties. The study emphasizes the significance of proactive interventions in fostering adherence and ultimately improving health for those with chronic conditions. Advocating for a holistic approach that merges patient education, behavioral modifications, and technological advancements, this research proposes a transformative model for chronic disease management. …
Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui
Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui
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No abstract provided.
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 …
Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed
Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed
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Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
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The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …
A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak
A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak
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One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ …
Structure Estimation Of Adversarial Distributions For Enhancing Model Robustness: A Clustering-Based Approach, Bader Rasheed, Adil Khan, Asad Masood Khattak
Structure Estimation Of Adversarial Distributions For Enhancing Model Robustness: A Clustering-Based Approach, Bader Rasheed, Adil Khan, Asad Masood Khattak
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In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in isolation, our approach employs clustering algorithms in conjunction with dimensionality reduction techniques to group adversarial perturbations, effectively constructing a more intricate and structured feature space for model training. Our method incorporates density and boundary-aware clustering mechanisms to capture the inherent spatial relationships among adversarial examples. Furthermore, we introduce a strategy for utilizing adversarial perturbations to enhance the delineation between clusters, leading to the formation of more robust and …