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Full-Text Articles in Computer Sciences

Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha Sep 2023

Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha

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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 …

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An Improved Dandelion Optimizer Algorithm For Spam Detection: Next-Generation Email Filtering System, Mohammad Tubishat, Feras Al-Obeidat, Ali Safaa Sadiq, Seyedali Mirjalili Sep 2023

An Improved Dandelion Optimizer Algorithm For Spam Detection: Next-Generation Email Filtering System, Mohammad Tubishat, Feras Al-Obeidat, Ali Safaa Sadiq, Seyedali Mirjalili

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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, …

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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 Sep 2023

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

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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 …

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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 Sep 2023

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

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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 …

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Benewind: An Adaptive Benefit Win–Win Platform With Distributed Virtual Emotion Foundation, Hyunbum Kim, Jalel Ben-Othman Sep 2023

Benewind: An Adaptive Benefit Win–Win Platform With Distributed Virtual Emotion Foundation, Hyunbum Kim, Jalel Ben-Othman

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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 …

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Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira Sep 2023

Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira

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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) …

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A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari Sep 2023

A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari

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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 …

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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 Aug 2023

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

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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 …

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A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee Aug 2023

A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee

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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 …

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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 Aug 2023

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

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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 …

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Towards Crisp‐Bc: 3tic Specification Framework For Blockchain Use‐Cases, Pouyan Jahanbin, Ravi S. Sharma, Stephen T. Wingreen, Nir Kshetri, Kim‐Kwang Raymond Choo Jul 2023

Towards Crisp‐Bc: 3tic Specification Framework For Blockchain Use‐Cases, Pouyan Jahanbin, Ravi S. Sharma, Stephen T. Wingreen, Nir Kshetri, Kim‐Kwang Raymond Choo

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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 …

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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 Jul 2023

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

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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 …

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Survey Of Personalized Learning Software Systems: A Taxonomy Of Environments, Learning Content, And User Models, Heba Ismail, Nada Hussein, Saad Harous, Ashraf Khalil Jul 2023

Survey Of Personalized Learning Software Systems: A Taxonomy Of Environments, Learning Content, And User Models, Heba Ismail, Nada Hussein, Saad Harous, Ashraf Khalil

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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 …

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Fault Aware Task Scheduling In Cloud Using Min-Min And Dbscan, S. M.F.D.Syed Mustapha, Punit Gupta Jul 2023

Fault Aware Task Scheduling In Cloud Using Min-Min And Dbscan, S. M.F.D.Syed Mustapha, Punit Gupta

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Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and …

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Dbscan Inspired Task Scheduling Algorithm For Cloud Infrastructure, S. M.F.D.Syed Mustapha, Punit Gupta Jul 2023

Dbscan Inspired Task Scheduling Algorithm For Cloud Infrastructure, S. M.F.D.Syed Mustapha, Punit Gupta

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Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task …

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Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb Jul 2023

Empowering Patient Similarity Networks Through Innovative Data-Quality-Aware Federated Profiling, Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb

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Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the …

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Artificial Intelligence Tool For The Study Of Covid-19 Microdroplet Spread Across The Human Diameter And Airborne Space, Hesham H. Alsaadi, Monther Aldwairi, Faten Yasin, Sandra C.P. Cachinho, Abdullah Hussein Jul 2023

Artificial Intelligence Tool For The Study Of Covid-19 Microdroplet Spread Across The Human Diameter And Airborne Space, Hesham H. Alsaadi, Monther Aldwairi, Faten Yasin, Sandra C.P. Cachinho, Abdullah Hussein

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The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance …

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Maximum Activation 3d Cube Transition System For Virtual Emotion Surveillance, Taewoo Lee, Jalel Ben-Othman, Hyunbum Kim Jul 2023

Maximum Activation 3d Cube Transition System For Virtual Emotion Surveillance, Taewoo Lee, Jalel Ben-Othman, Hyunbum Kim

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The concept of barrier coverage has been utilized for with various applications of surveillance, object tracking in smart cities. In barrier coverage, it is desirable to have large number of active barriers to maximize lifetime of UAV-assisted application. Because existing studies primarily focused on the formation of barriers in two-dimensional area with limited applicability, it is indispensable to extend the barrier constructions in three-dimensional area. In this letter, a cube transition barrier system using smart UAVs is designed for three-dimensional space. Then, we formally define a problem whose goal is to maximize the number of cube transition barriers by applying …

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Self-Healing In Cyber–Physical Systems Using Machine Learning: A Critical Analysis Of Theories And Tools, Obinna Johnphill, Ali Safaa Sadiq, Feras Al-Obeidat, Haider Al-Khateeb, Mohammed Adam Taheir, Omprakash Kaiwartya, Mohammed Ali Jul 2023

Self-Healing In Cyber–Physical Systems Using Machine Learning: A Critical Analysis Of Theories And Tools, Obinna Johnphill, Ali Safaa Sadiq, Feras Al-Obeidat, Haider Al-Khateeb, Mohammed Adam Taheir, Omprakash Kaiwartya, Mohammed Ali

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The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures …

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Suspicious Behavior Detection With Temporal Feature Extraction And Time-Series Classification For Shoplifting Crime Prevention, Amril Nazir, Rohan Mitra, Hana Sulieman, Firuz Kamalov Jul 2023

Suspicious Behavior Detection With Temporal Feature Extraction And Time-Series Classification For Shoplifting Crime Prevention, Amril Nazir, Rohan Mitra, Hana Sulieman, Firuz Kamalov

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The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then …

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Aisha: A Custom Ai Library Chatbot Using The Chatgpt Api, Yrjo Lappalainen, Nikesh Narayanan Jun 2023

Aisha: A Custom Ai Library Chatbot Using The Chatgpt Api, Yrjo Lappalainen, Nikesh Narayanan

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This article focuses on the development of a custom chatbot for Zayed University Library (United Arab Emirates) using Python and the ChatGPT API. The chatbot, named Aisha, was designed to provide quick and efficient reference and support services to students and faculty outside the library's regular operating hours. The article also discusses the benefits of chatbots in academic libraries, and reviews the early literature on ChatGPT's applicability in this field. The article describes the development process, perceived capabilities and limitations of the bot, and plans for further development. This project represents the first fully reported attempt to explore the potential …

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A Novel Driver Emotion Recognition System Based On Deep Ensemble Classification, Khalid Zaman, Sun Zhaoyun, Babar Shah, Tariq Hussain, Sayyed Mudassar Shah, Farman Ali, Umer Sadiq Khan Jun 2023

A Novel Driver Emotion Recognition System Based On Deep Ensemble Classification, Khalid Zaman, Sun Zhaoyun, Babar Shah, Tariq Hussain, Sayyed Mudassar Shah, Farman Ali, Umer Sadiq Khan

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Driver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which …

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A System Dynamics Approach To Evaluate Advanced Persistent Threat Vectors, Mathew Nicho, Christopher D. Mcdermott, Hussein Fakhry, Shini Girija Jun 2023

A System Dynamics Approach To Evaluate Advanced Persistent Threat Vectors, Mathew Nicho, Christopher D. Mcdermott, Hussein Fakhry, Shini Girija

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Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage …

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An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista May 2023

An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista

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Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims …

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Boosting Adversarial Training Using Robust Selective Data Augmentation, Bader Rasheed, Asad Masood Khattak, Adil Khan, Stanislav Protasov, Muhammad Ahmad May 2023

Boosting Adversarial Training Using Robust Selective Data Augmentation, Bader Rasheed, Asad Masood Khattak, Adil Khan, Stanislav Protasov, Muhammad Ahmad

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Artificial neural networks are currently applied in a wide variety of fields, and they are near to achieving performance similar to humans in many tasks. Nevertheless, they are vulnerable to adversarial attacks in the form of a small intentionally designed perturbation, which could lead to misclassifications, making these models unusable, especially in applications where security is critical. The best defense against these attacks, so far, is adversarial training (AT), which improves the model’s robustness by augmenting the training data with adversarial examples. In this work, we show that the performance of AT can be further improved by employing the neighborhood …

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Personalized Health Care In A Data-Driven Era: A Post–Covid-19 Retrospective, Arnob Zahid, Ravishankar Sharma May 2023

Personalized Health Care In A Data-Driven Era: A Post–Covid-19 Retrospective, Arnob Zahid, Ravishankar Sharma

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No abstract provided.

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Using Deep Learning Model To Identify Iron Chlorosis In Plants, Munir Majdalawieh, Shafaq Khan, Md. T. Islam May 2023

Using Deep Learning Model To Identify Iron Chlorosis In Plants, Munir Majdalawieh, Shafaq Khan, Md. T. Islam

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Iron deficiency in plants causes iron chlorosis which frequently occurs in soils that are alkaline (pH greater than 7.0) and that contain lime. This deficiency turns affected plant leaves to yellow, or with brown edges in advanced stages. The goal of this research is to use the deep learning model to identify a nutrient deficiency in plant leaves and perform soil analysis to identify the cause of the deficiency. Two pre-trained deep learning models, Single Shot Detector (SSD) MobileNet v2 and EfficientDet D0, are used to complete this task via transfer learning. This research also contrasts the architecture and performance …

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Time Varied Self-Reliance Aerial Ground Traffic Monitoring System With Pre-Recognition Collision Avoidance, Seungheyon Lee, Sooeon Lee, Yumin Choi, Jalel Ben-Othman, Lynda Mokdad, Hyunbum Kim May 2023

Time Varied Self-Reliance Aerial Ground Traffic Monitoring System With Pre-Recognition Collision Avoidance, Seungheyon Lee, Sooeon Lee, Yumin Choi, Jalel Ben-Othman, Lynda Mokdad, Hyunbum Kim

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In this letter, we introduce a time varied self-reliance aerial ground traffic monitoring system which provides pre-recognition collision avoidance among mobile robots and smart UAVs for virtual emotion security. Then, with ILP (Integer Linear Programming), we make a formal definition of the problem whose objective is to minimize a total spent time by smart UAVs (Unmanned Aerial Vehicles) and mobile robots without conflicts on condition that the demanded number of self-reliance security barriers are formed. To solve the defined problem, we develop two approaches, time-differentiated pre-stop movement and approximated equal segments movement. Then, those schemes are implemented through expanded experiments …

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More Than Meets The Eye: In-Store Retail Experiences With Augmented Reality Smart Glasses, Pauline Pfeifer, Tim Hilken, Jonas Heller, Saifeddin Alimamy, Roberta Di Palma May 2023

More Than Meets The Eye: In-Store Retail Experiences With Augmented Reality Smart Glasses, Pauline Pfeifer, Tim Hilken, Jonas Heller, Saifeddin Alimamy, Roberta Di Palma

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Augmented reality smart glasses (ARSGs) promise to enhance consumer experiences and decision-making when deployed as in-store retail technologies. However, research to date has not studied in-store use cases; instead, it has focused primarily on consumers' potential adoption of these devices for everyday use. Nor have prior studies compared ARSG uses with the now-common use of AR on touchscreen devices. The current research addresses these knowledge gaps by examining whether ARSGs outperform AR on touchscreen devices in the context of in-store retail experiences. Testing with an actual retail application (n = 308) shows that ARSGs are superior to AR on touchscreen …

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Haptic Systems: Trends And Lessons Learned For Haptics In Spacesuits, Mohammad Amin Kuhail, Jose Berengueres, Fatma Taher, Mariam Alkuwaiti, Sana Z. Khan Apr 2023

Haptic Systems: Trends And Lessons Learned For Haptics In Spacesuits, Mohammad Amin Kuhail, Jose Berengueres, Fatma Taher, Mariam Alkuwaiti, Sana Z. Khan

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Haptic technology uses forces, vibrations, and movements to simulate a sense of touch. In the context of spacesuits, proposals to use haptic systems are scant despite evidence of their efficacy in other domains. Existing review studies have sought to summarize existing haptic system applications. Despite their contributions to the body of knowledge, existing studies have not assessed the applicability of existing haptic systems in spacesuit design to meet contemporary challenges. This study asks, “What can we learn from existing haptic technologies to create spacesuits?”. As such, we examine academic and commercial haptic systems to address this issue and draw insights …

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