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Articles 451 - 480 of 677
Full-Text Articles in Computer Sciences
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
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This study investigated whether mobile payment services could drive post-COVID-19 pandemic recovery in the ‘experience goods’ sector (e.g., tourism) utilising Bandura’s self-efficacy or social cognitive theory. It explored the factors influencing the intention to continue using mobile payment services and the intention to recommend these to others. An empirical survey was conducted to assess the study variables, and the data obtained therefrom were analysed using the industry-standard Cross-Industry Standard Process for Data Mining method. The study results suggest that personal innovativeness and perceived trust influence consumers’ intention to continue using mobile payment services and that perceived trust, personal innovativeness and …
Hybrid Approach For Resource Allocation In Cloud Infrastructure Using Random Forest And Genetic Algorithm, Madhusudhan H S, Satish Kumar T, S.M.F D Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi
Hybrid Approach For Resource Allocation In Cloud Infrastructure Using Random Forest And Genetic Algorithm, Madhusudhan H S, Satish Kumar T, S.M.F D Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi
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In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning …
D2gen: A Decentralized Device Genome Based Integrity Verification Mechanism For Collaborative Intrusion Detection Systems, Imran Makhdoom, Kadhim Hayawi, Mohammed Kaosar, Sujith Samuel Mathew, Pin-Han Ho
D2gen: A Decentralized Device Genome Based Integrity Verification Mechanism For Collaborative Intrusion Detection Systems, Imran Makhdoom, Kadhim Hayawi, Mohammed Kaosar, Sujith Samuel Mathew, Pin-Han Ho
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Collaborative Intrusion Detection Systems are considered an effective defense mechanism for large, intricate, and multilayered Industrial Internet of Things against many cyberattacks. However, while a Collaborative Intrusion Detection System successfully detects and prevents various attacks, it is possible that an inside attacker performs a malicious act and compromises an Intrusion Detection System node. A compromised node can inflict considerable damage on the whole collaborative network. For instance, when a malicious node gives a false alert of an attack, the other nodes will unnecessarily increase their security and close all of their services, thus, degrading the system’s performance. On the contrary, …
Sustainable Maritime Crude Oil Transportation: A Split Pickup And Split Delivery Problem With Time Windows, Hiba Yahyaoui, Nadia Dahmani, Saoussen Krichen
Sustainable Maritime Crude Oil Transportation: A Split Pickup And Split Delivery Problem With Time Windows, Hiba Yahyaoui, Nadia Dahmani, Saoussen Krichen
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This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method.
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography …
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
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The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific …
Boolean Logic Algebra Driven Similarity Measure For Text Based Applications, Hassan I. Abdalla, Ali A. Amer
Boolean Logic Algebra Driven Similarity Measure For Text Based Applications, Hassan I. Abdalla, Ali A. Amer
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In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean …
Classifier Performance Evaluation For Lightweight Ids Using Fog Computing In Iot Security, Belal Sudqi Khater, Ainuddin Wahid Abdul Wahab, Mohd Yamaniidna Idris, Mohammed Abdulla Hussain, Ashraf Ahmed Ibrahim, Mohammad Arif Amin, Hisham A. Shehadeh
Classifier Performance Evaluation For Lightweight Ids Using Fog Computing In Iot Security, Belal Sudqi Khater, Ainuddin Wahid Abdul Wahab, Mohd Yamaniidna Idris, Mohammed Abdulla Hussain, Ashraf Ahmed Ibrahim, Mohammad Arif Amin, Hisham A. Shehadeh
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In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In …
Pedestrian Attribute Recognition Using Trainable Gabor Wavelets, Imran N Junejo, Naveed Ahmed, Mohammad Lataifeh
Pedestrian Attribute Recognition Using Trainable Gabor Wavelets, Imran N Junejo, Naveed Ahmed, Mohammad Lataifeh
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Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers …
Swarm Differential Privacy For Purpose-Driven Data-Information-Knowledge-Wisdom Architecture, Yingbo Li, Yucong Duan, Zakaria Maamar, Haoyang Che, Anamaria-Beatrice Spulber, Stelios Fuentes
Swarm Differential Privacy For Purpose-Driven Data-Information-Knowledge-Wisdom Architecture, Yingbo Li, Yucong Duan, Zakaria Maamar, Haoyang Che, Anamaria-Beatrice Spulber, Stelios Fuentes
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Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will …
Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
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The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android …
Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad
Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad
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The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative …
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
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Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. …
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
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The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or …
Pedestrian Attribute Recognition Using Two-Branch Trainable Gabor Wavelets Network, Imran N. Junejo
Pedestrian Attribute Recognition Using Two-Branch Trainable Gabor Wavelets Network, Imran N. Junejo
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Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed …
Niching Grey Wolf Optimizer For Multimodal Optimization Problems, Rasel Ahmed, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, Jahedul Islam
Niching Grey Wolf Optimizer For Multimodal Optimization Problems, Rasel Ahmed, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, Jahedul Islam
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Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to …
Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah
Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah
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The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk …
Big Data Quality Framework: A Holistic Approach To Continuous Quality Management, Ikbal Taleb, Mohamed Adel Serhani, Chafik Bouhaddioui, Rachida Dssouli
Big Data Quality Framework: A Holistic Approach To Continuous Quality Management, Ikbal Taleb, Mohamed Adel Serhani, Chafik Bouhaddioui, Rachida Dssouli
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Big Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is …
Gaming Disorder And Well-Being Among Emirati College Women, Marina Verlinden, Justin Thomas, Mahra Hasan Abdulla Ahamed Almansoori, Shamil Wanigaratne
Gaming Disorder And Well-Being Among Emirati College Women, Marina Verlinden, Justin Thomas, Mahra Hasan Abdulla Ahamed Almansoori, Shamil Wanigaratne
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Background: The present study examined Internet Gaming Disorder (IGD) and depressive symptom levels among a predominantly female sample of college students from the United Arab Emirates (UAE). Methods: IGD was assessed among two successive cohorts of students at the beginning of the academic year in 2016 and 2019, respectively. All participants (n = 412) completed the Internet Gaming Disorder Scale – Short-Form (IGDS9-SF) and the WHO-5 Well-being Index (WHO-5), a tool widely used for the screening and assessment of depressive symptomatology. Results: Mean IGDS9-SF scores (15.85, SD = 6.40) were fairly similar to those observed in other nations. The prevalence …
Factors That Affect E-Learning Platforms After The Spread Of Covid-19: Post Acceptance Study, Rana Saeed Al-Maroof, Khadija Alhumaid, Iman Akour, Said Salloum
Factors That Affect E-Learning Platforms After The Spread Of Covid-19: Post Acceptance Study, Rana Saeed Al-Maroof, Khadija Alhumaid, Iman Akour, Said Salloum
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The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students' perception, hence, acceptance of e-learning platforms. Therefore, this research attempts to explore the post-acceptance of e-learning platforms based on a conceptual model that has various variables. Each variable contributes differently to the post-acceptance of the e-learning platform. The research investigates the moderating role of vaccination fear on the post-acceptance of e-learning platforms among students. Thus, the study aims at exploring students' perceptions about their post-acceptance …
A Comprehensive Review Of Retinal Vascular And Optical Nerve Diseases Based On Optical Coherence Tomography Angiography, Fatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Talal Alhalabi, Harpal Singh Sandhu, Ayman El-Baz
A Comprehensive Review Of Retinal Vascular And Optical Nerve Diseases Based On Optical Coherence Tomography Angiography, Fatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Talal Alhalabi, Harpal Singh Sandhu, Ayman El-Baz
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The optical coherence tomography angiography (OCTA) is a noninvasive imaging technology which aims at imaging blood vessels in retina by studying decorrelation signals between multiple sequential OCT B-scans captured in the same cross section. Obtaining various vascular plexuses including deep and superficial choriocapillaris, is possible, which helps in understanding the ischemic processes that affect different retina layers. OCTA is a safe imaging modality that does not use dye. OCTA is also fast as it can capture high-resolution images in just seconds. Additionally, it is used in the assessment of structure and blood flow. OCTA provides anatomic details in addition to …
A Novel Mra-Based Framework For Segmenting The Cerebrovascular System And Correlating Cerebral Vascular Changes To Mean Arterial Pressure, Fatma Taher, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El‐Mashad, Ayman El‐Baz
A Novel Mra-Based Framework For Segmenting The Cerebrovascular System And Correlating Cerebral Vascular Changes To Mean Arterial Pressure, Fatma Taher, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El‐Mashad, Ayman El‐Baz
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Blood pressure (BP) changes with age are widespread, and systemic high blood pressure (HBP) is a serious factor in developing strokes and cognitive impairment. A non‐invasive methodology to detect changes in human brain’s vasculature using Magnetic Resonance Angiography (MRA) data and correlation of cerebrovascular changes to mean arterial pressure (MAP) is pre-sented. MRA data and systemic blood pressure measurements were gathered from patients (n = 15, M = 8, F = 7, Age = 49.2 ± 7.3 years) over 700 days (an initial visit and then a follow‐up period of 2 years with a final visit.). A novel segmentation algorithm …
Hybrid Deep Learning Architecture To Forecast Maximum Load Duration Using Time-Of-Use Pricing Plans, Jinseok Kim, Babar Shah, Ki Il Kim
Hybrid Deep Learning Architecture To Forecast Maximum Load Duration Using Time-Of-Use Pricing Plans, Jinseok Kim, Babar Shah, Ki Il Kim
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Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use. …
Quantitative Analysis And Performance Evaluation Of Target-Oriented Replication Strategies In Cloud Computing, Quadri Waseem, Wan Isni Sofiah Wan Din, Sultan S. Alshamrani, Abdullah Alharbi, Amril Nazir
Quantitative Analysis And Performance Evaluation Of Target-Oriented Replication Strategies In Cloud Computing, Quadri Waseem, Wan Isni Sofiah Wan Din, Sultan S. Alshamrani, Abdullah Alharbi, Amril Nazir
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Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. …
Smart Dynamic Traffic Monitoring And Enforcement System, Youssef El-Hansali, Fatma Outay, Ansar Yasar, Siham Farrag, Muhammad Shoaib, Muhammad Imran, Hammad Hussain Awan
Smart Dynamic Traffic Monitoring And Enforcement System, Youssef El-Hansali, Fatma Outay, Ansar Yasar, Siham Farrag, Muhammad Shoaib, Muhammad Imran, Hammad Hussain Awan
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Enforcement of traffic rules and regulations involves a wide range of complex tasks, many of which demand the use of modern technologies. variable speed limits (VSL) control is to change the current speed limit according to the current traffic situation based on the observed traffic conditions. The aim of this study is to provide a simulation-based methodological framework to evaluate (VSL) as an effective Intelligent Transportation System (ITS) enforcement system. The focus of the study is on measuring the effectiveness of the dynamic traffic control strategy on traffic performance and safety considering various performance indicators such as total travel time, …
Information, Communications And Media Technologies For Sustainability: Constructing Data-Driven Policy Narratives, Ravishankar Sharma, Aijaz A. Shaikh, Stephen Bekoe, Gautam Ramasubramanian
Information, Communications And Media Technologies For Sustainability: Constructing Data-Driven Policy Narratives, Ravishankar Sharma, Aijaz A. Shaikh, Stephen Bekoe, Gautam Ramasubramanian
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This paper introduces the idea of data-driven narratives to examine how the use of infor-mation, communications, and media technologies (ICMTs) impacts the sustainable growth of econ-omies. While ICMTs have regularly been advocated as a policy tool for growth and development, there is a research gap in empirical studies validating how such policies may be effective. This analysis is based on historical panel data from 39 economies across the developed North (19) and developing South (20). The industry-standard Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied to construct narratives that weave extant theories with empirical data. The art of …
Noise Annoyance In The Uae: A Twitter Case Study Via A Data-Mining Approach, Andrew Peplow, Justin Thomas, Aamna Alshehhi
Noise Annoyance In The Uae: A Twitter Case Study Via A Data-Mining Approach, Andrew Peplow, Justin Thomas, Aamna Alshehhi
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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Noise pollution is a growing global public health concern. Among other issues, it has been linked with sleep disturbance, hearing functionality, increased blood pressure and heart disease. Individuals are increasingly using social media to express complaints and concerns about problematic noise sources. This behavior—using social media to post noise-related concerns—might help us better identify troublesome noise pollution hotspots, thereby enabling us to take corrective action. The present work is a concept case study exploring the use of social media data as a means of identifying and monitoring noise annoyance across the …
Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem
Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem
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Plant hormones are small regulatory molecules that exert pharmacological actions in mammalian cells such as anti-oxidative and pro-metabolic effects. Kinetin belongs to the group of plant hormones cytokinin and has been associated with modulatory functions in mammalian cells. The mammalian adenosine receptor (A2a-R) is known to modulate multiple physiological responses in animal cells. Here, we describe that kinetin binds to the adenosine receptor (A2a-R) through the Asn253 residue in an adenosine dependent manner. To harness the beneficial effects of kinetin for future human use, we assess its acute toxicity by analyzing different biochemical and histological markers in rats. Kinetin at …
Designing A Relational Model To Identify Relationships Between Suspicious Customers In Anti-Money Laundering (Aml) Using Social Network Analysis (Sna), Abdul Khalique Shaikh, Malik Al-Shamli, Amril Nazir
Designing A Relational Model To Identify Relationships Between Suspicious Customers In Anti-Money Laundering (Aml) Using Social Network Analysis (Sna), Abdul Khalique Shaikh, Malik Al-Shamli, Amril Nazir
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The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge …
Detection Of Freezing Of Gait Using Unsupervised Convolutional Denoising Autoencoder, Mohd Halim Mohd Noor, Amril Nazir, Mohd Nadhir Ab Wahab, Jodene Ooi Yen Ling
Detection Of Freezing Of Gait Using Unsupervised Convolutional Denoising Autoencoder, Mohd Halim Mohd Noor, Amril Nazir, Mohd Nadhir Ab Wahab, Jodene Ooi Yen Ling
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At the advanced stage of Parkinson’s disease, patients may suffer from ‘freezing of gait’ episodes: a debilitating condition wherein a patient’s “feet feel as though they are glued to the floor”. The objective, continuous monitoring of the gait of Parkinson’s disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when …