Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (15)
- Deep learning (9)
- COVID-19 (6)
- Artificial intelligence (5)
- Clustering (3)
-
- Edge computing (3)
- IoT (3)
- Optimization (3)
- Virtual emotion (3)
- AI (2)
- Augmented reality (2)
- Autonomous driving (2)
- Blockchain (2)
- Bot detection (2)
- ChatGPT (2)
- Convolutional neural networks (2)
- Cyberattacks (2)
- Cybersecurity (2)
- Deep Learning (2)
- Federated Learning (2)
- Human factors (2)
- Intrusion detection (2)
- LSTM (2)
- Localization (2)
- Machine Learning (2)
- Malware (2)
- NLP (2)
- Plant disease detection (2)
- Resource management (2)
- Robustness (2)
Articles 1 - 30 of 88
Full-Text Articles in Physical Sciences and Mathematics
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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
All Works
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 …
Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha
Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha
All Works
The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient …
An Improved Dandelion Optimizer Algorithm For Spam Detection: Next-Generation Email Filtering System, Mohammad Tubishat, Feras Al-Obeidat, Ali Safaa Sadiq, Seyedali Mirjalili
An Improved Dandelion Optimizer Algorithm For Spam Detection: Next-Generation Email Filtering System, Mohammad Tubishat, Feras Al-Obeidat, Ali Safaa Sadiq, Seyedali Mirjalili
All Works
Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, …
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa
All Works
BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …
Efficient Power Management Optimization Based On Whale Optimization Algorithm And Enhanced Differential Evolution, Khalid Zaman, Sun Zhaoyun, Babar Shah, Altaf Hussain, Tariq Hussain, Umer Sadiq Khan, Farman Ali, Boukansous Sarra
Efficient Power Management Optimization Based On Whale Optimization Algorithm And Enhanced Differential Evolution, Khalid Zaman, Sun Zhaoyun, Babar Shah, Altaf Hussain, Tariq Hussain, Umer Sadiq Khan, Farman Ali, Boukansous Sarra
All Works
Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required …
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
All Works
Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) …
Benewind: An Adaptive Benefit Win–Win Platform With Distributed Virtual Emotion Foundation, Hyunbum Kim, Jalel Ben-Othman
Benewind: An Adaptive Benefit Win–Win Platform With Distributed Virtual Emotion Foundation, Hyunbum Kim, Jalel Ben-Othman
All Works
In recent decades, online platforms that use Web 3.0 have tremendously expanded their goods, services, and values to numerous applications thanks to its inherent advantages of convenience, service speed, connectivity, etc. Although online commerce and other relevant platforms have clear merits, offline-based commerce and payments are indispensable and should be activated continuously, because offline systems have intrinsic value for people. With the theme of benefiting all humankind, we propose a new adaptive benefit platform, called BeneWinD, which is endowed with strengths of online and offline platforms. Furthermore, a new currency for integrated benefits, the win–win digital currency, is used in …
A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari
A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari
All Works
There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of …
Digital Literacies As Policy Catalysts Of Social Innovation And Socioeconomic Transformation: Interpretive Analysis From Singapore And The Uae, Ravi S. Sharma, Intan Azura Mokhtar, Dhanjoo N. Ghista, Amril Nazir, Sana Z. Khan
Digital Literacies As Policy Catalysts Of Social Innovation And Socioeconomic Transformation: Interpretive Analysis From Singapore And The Uae, Ravi S. Sharma, Intan Azura Mokhtar, Dhanjoo N. Ghista, Amril Nazir, Sana Z. Khan
All Works
Even before the COVID-19 global pandemic, the world saw the adoption and proliferation of numerous digital tools and technologies, or a global digital transformation. This paved the way for digital inclusion, particularly through e-commerce and shared services platforms which helped to reduce barriers to entry and created abundant socio-economic opportunities across income groups. As a result, digital literacy becomes a vital aspect of modern life due to the rapid global shift toward this digital transformation. Numerous scholars have investigated the benefits of digital literacies since 1995. The primary objective of this paper is to investigate good practices and lessons learned …
A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee
A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee
All Works
There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and …
Enabling Affordances Of Blockchain In Agri-Food Supply Chains: A Value-Driver Framework Using The Q-Methodology, Pouyan Jahanbin, Stephen C. Wingreen, Ravishankar Sharma, Behrang Ijadi, Marlon M. Reis
Enabling Affordances Of Blockchain In Agri-Food Supply Chains: A Value-Driver Framework Using The Q-Methodology, Pouyan Jahanbin, Stephen C. Wingreen, Ravishankar Sharma, Behrang Ijadi, Marlon M. Reis
All Works
The application of blockchain beyond cryptocurrencies has received increasing attention from industry and scholars alike. Given predicted looming food crises, some of the most impactful deployments of blockchains are likely to concern food supply chains. This study outlined how blockchain adoption can result in positive affordances in the food supply chain. Using Q methodology, this study explored the current status of the agri-food supply chain and how blockchain technology could be useful in addressing existing challenges. This theorization leads to the proposition of the 3TIC value-driver framework for determining the enabling affordances of blockchain that would increase shared value for …
Towards Crisp‐Bc: 3tic Specification Framework For Blockchain Use‐Cases, Pouyan Jahanbin, Ravi S. Sharma, Stephen T. Wingreen, Nir Kshetri, Kim‐Kwang Raymond Choo
Towards Crisp‐Bc: 3tic Specification Framework For Blockchain Use‐Cases, Pouyan Jahanbin, Ravi S. Sharma, Stephen T. Wingreen, Nir Kshetri, Kim‐Kwang Raymond Choo
All Works
The application of Blockchain and augmented technologies such as IoT, AI, and Big Data platforms present a feasible approach for resolving the implementation challenges of trusted, decentralized platforms. This article proposes a DevOps framework for the specification of Blockchain use‐cases that enables evaluation, replication, and benchmarking. Specifically, it could be applied to specify the requirements and design characteristics of Blockchain applications in terms of key attributes such as: (i) transparency; (ii) traceability; (iii) tamper‐resistance; (iv) immutability; and (v) compliance. The article first introduces the design characteristics of Blockchain as a Platform and then examines successful use‐cases for its implementation using …
Forensic Investigation Of Small-Scale Digital Devices: A Futuristic View, Farkhund Iqbal, Aasia Jaffri, Zainab Khalid, Aine Macdermott, Qazi Ejaz Ali, Patrick C. K. Hung
Forensic Investigation Of Small-Scale Digital Devices: A Futuristic View, Farkhund Iqbal, Aasia Jaffri, Zainab Khalid, Aine Macdermott, Qazi Ejaz Ali, Patrick C. K. Hung
All Works
Small-scale digital devices like smartphones, smart toys, drones, gaming consoles, tablets, and other personal data assistants have now become ingrained constituents in our daily lives. These devices store massive amounts of data related to individual traits of users, their routine operations, medical histories, and financial information. At the same time, with continuously evolving technology, the diversity in operating systems, client storage localities, remote/cloud storages and backups, and encryption practices renders the forensic analysis task multi-faceted. This makes forensic investigators having to deal with an array of novel challenges. This study reviews the forensic frameworks and procedures used in investigating small-scale …
Survey Of Personalized Learning Software Systems: A Taxonomy Of Environments, Learning Content, And User Models, Heba Ismail, Nada Hussein, Saad Harous, Ashraf Khalil
Survey Of Personalized Learning Software Systems: A Taxonomy Of Environments, Learning Content, And User Models, Heba Ismail, Nada Hussein, Saad Harous, Ashraf Khalil
All Works
This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the …