Computer Engineering Commons^™

Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif

Journal of Soft Computing and Computer Applications

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …

Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali

Journal of Soft Computing and Computer Applications

Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …

A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy

Journal of Soft Computing and Computer Applications

The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …

A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

In lightweight cryptography, the absence of an S-Box in some algorithms like speck, Tiny Encryption Algorithm, or the presence of a fixed S-Box in others like Advanced Encryption Standard can make them more vulnerable to attacks. This study introduces an innovative method for creating a dynamic 6-bit S-Box (8×8) in octal format. The generating process of S-Box passes through two phases. The first is the number initialization phase. This phase involves generating sequence numbers 1, sequence numbers 2, and sequence numbers 3 depending on X_i, Y_i, and Z_i values generated using the 3D Hindmarsh …

The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana

Journal of Soft Computing and Computer Applications

Digital data such as images, audio, and video have become widely available since the invention of the Internet. Due to the ease of access to this multimedia, challenges such as content authentication, security, copyright protection, and ownership determination arose. In this paper, an explanation of watermark techniques, embedding, and extraction methods are provided. It further discusses the utilization of artificial intelligence methods and conversion of host media from the spatial domain to the frequency domain; these methods aim to improve the quality of watermarks. This paper also included a classification of the basic characteristics of the digital watermark and the …

Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal

Journal of Soft Computing and Computer Applications

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset …

Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy

Journal of Soft Computing and Computer Applications

Cyberattacks have become one of the most significant security threats that have emerged in the last couple of years. It is imperative to comprehend such attacks; thus, analyzing various kinds of cyberattack datasets assists in constructing the precise intrusion detection models. This paper tries to analyze many of the available cyberattack datasets and compare them with many of the fields that are used to detect and predict cyberattack, like the Internet of Things (IoT) traffic-based, network traffic-based, cyber-physical system, and web traffic-based. In the present paper, an overview of each of them is provided, as well as the course of …

Electronic Properties Of Group-Iii Nitride Semiconductors And Device Structures Probed By Thz Optical Hall, Nerijus Armakavicius, Philipp Kühne, Alexis Papamichail, Hengfang Zhang, Sean Knight, Axel Persson, Vallery Stanishev, Jr-Tai Chen, Plamen Paskov, Mathias Schubert, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

Group-III nitrides have transformed solid-state lighting and are strategically positioned to revolutionize high-power and high-frequency electronics. To drive this development forward, a deep understanding of fundamental material properties, such as charge carrier behavior, is essential and can also unveil new and unforeseen applications. This underscores the necessity for novel characterization tools to study group-III nitride materials and devices. The optical Hall effect (OHE) emerges as a contactless method for exploring the transport and electronic properties of semiconductor materials, simultaneously offering insights into their dielectric function. This nondestructive technique employs spectroscopic ellipsometry at long wavelengths in the presence of a magnetic …

How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez

Journal of Computer Science Integration

This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …

Problem Solving / Javascript Programming, Sarah Zelikovitz, Orit D. Gruber

Open Educational Resources

This Lab Experiment focuses on JavaScript Programming. Upon completing the lab, you will be able to understand the following:

· The definition of Algorithmic Problem Solving.

· The role of JavaScript in web pages.

· The concept of Iteration in computer programming.

Authentic Impediments: The Influence Of Identity Threat, Cultivated Perceptions, And Personality On Robophobia, Kate K. Mays

Human-Machine Communication

Considering possible impediments to authentic interactions with machines, this study explores contributors to robophobia from the potential dual influence of technological features and individual traits. Through a 2 x 2 x 3 online experiment, a robot’s physical human-likeness, gender, and status were manipulated and individual differences in robot beliefs and personality traits were measured. The effects of robot traits on phobia were non-significant. Overall, subjective beliefs about what robots are, cultivated by media portrayals, whether they threaten human identity, are moral, and have agency were the strongest predictors of robophobia. Those with higher internal locus of control and neuroticism, and …

What’S In A Name And/Or A Frame? Ontological Framing And Naming Of Social Actors And Social Responses, David Westerman, Michael Vosburg, Xinyue Liu, Patric R. Spence

Human-Machine Communication

Artificial intelligence (AI) is fundamentally a communication field. Thus, the study of how AI interacts with us is likely to be heavily driven by communication. The current study examined two things that may impact people’s perceptions of socialness of a social actor: one nonverbal (ontological frame) and one verbal (providing a name) with a 2 (human vs. robot) x 2 (named or not) experiment. Participants saw one of four videos of a study “host” crossing these conditions and responded to various perceptual measures about the socialness and task ability of that host. Overall, data were consistent with hypotheses that whether …

Anonymized Identity Recognition And Classification Using Privacy Preserving Facial Encoding, Manas Sanjay Pakalapati

USF Tampa Graduate Theses and Dissertations

The need for sharing large-scale datasets to train deep neural network models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.

In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized publicly available facial databases and focused on evaluating the trade-offs inherent in image encoding techniques, with a …

Digimindready: Enhancing Military Readiness Through Edge Ai-Driven Wellness, Education, And Digital Discipline Via Privacy-First Mhealth Innovation, Md Mehedi Hasan

Master's Theses

Military personnel often find themselves in intense situations that require high focus. Successfully engaging in these dangerous missions means they must efficiently control cognitive load, manage overwhelming stress, and stay focused through distractions to perform at their best. Military training significantly focuses on human performance, which benefits military readiness. The 21st century has introduced unanticipated challenges to all, such as the adverse effects of excessive screen time, external distractions, and over-reliance on technology without being aware of digital discipline. Militaries are no exception. These challenges have become an emerging threat to military personnel's cognitive, emotional, and physical well-being. On top …

Cognitive Manufacturing: Definition And Current Trends, Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik

Publications

Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the …

California 4-H Computer Science Education Pathway, Steven M. Worker, Roshan Nayak, Fe Moncloa

Journal of Extension

Young people need digital competency and confidence to effectively harness computing power to solve problems and design solutions; a core component is improving young people’s computational thinking. Unfortunately, access to computer science education is lacking for all youth, and in particular for youth who live in lower-income households, who are Black or Latino, or live in rural areas. With funding from Google, through the National 4-H Council, California 4-H embarked on a three-year project to build the capacity of 4-H professionals, volunteers, and teenagers to facilitate computer science education with youth. Our programming was effective as assessed with survey methodology. …

Corridor Counting, Anthony Bryson, Vincent Zhou, Amy Ha

Computer Science and Engineering Senior Theses

Information on traffic patterns is essential for identifying and addressing sources of traffic congestion and informing future road layouts to create safer and more efficient roads. To this end, we develop a Corridor Counting, or Multi- Camera Vehicle Counting, algorithm that quantifies the number of vehicles traveling along a specific stretch of road. Our work builds upon the related problem of Multi-Camera Vehicle Tracking and draws inspiration from methods used for Single-Camera Counting. We propose a six-step solution comprising Vehicle Detection, Feature Extraction, Single- Camera Vehicle Tracking, Re-Identification, Movement Matching, and Multi-Camera Vehicle Counting. Finally, we adapt an evaluation metric …

Two-Step Hierarchical Multi-Camera People Tracking, Eerina Haque, Eric Huang, Sihang Li

Computer Science and Engineering Senior Theses

The possibility of an efficient and accurate solution for multi-camera people tracking (MCPT) is enabled by the improvement of computing power and the advancement of machine learning technologies. The problem of multi-camera people tracking serves as a cornerstone of real-world applications such as video surveillance or warehouse automation. The current solutions for MCPT suffer from problems such as appearance inconsistency, object occlusion, etc. Our work targets tackling the challenges faced by modern MCPT algorithms to bring a more robust, efficient, and accurate solution.

The Impact Of Emojis On User Engagement With Trolling Content In Online Platforms, Diya Saraf

Computer Science and Engineering Senior Theses

This thesis examines the impact of emojis on user engagement within online discussions, particularly focusing on the trolling behavior on social media platforms such as Reddit. The central problem addressed is understanding how different types of emojis—in emotional, informational, and popularity-based categories—affect user interactions and engagement in online communications. Through a robust methodological approach, combining quantitative data analysis and Random Forest regression modeling, this research meticulously analyzes emoji usage patterns and their correlations with engagement metrics like upvotes and comments.

The results highlight that emotional and popular emojis significantly enhance engagement by facilitating emotional expression and connection among users. In …

Ragu Conversational Menu Assistant: An Llm Retrieval Augmented Generation Approach, Sam Abdel, Seth Mak, Aaron Pham, Christopher Michael

Computer Science and Engineering Senior Theses

This project introduces a novel Conversational Menu Assistant leveraging Retrieval Augmented Generation (RAG) techniques within a modified Large Language Model (LLM) framework to enhance dining experiences by providing personalized menu assistance. The main innovation lies in the system’s ability to integrate up-to-date menu information from various sources, including web scraping, into its responses, thereby circumventing the limitations commonly associated with LLMs, such as the need for frequent retraining and the challenge of handling dynamic information. Our solution addresses the pressing issue of reducing the workload on restaurant servers and streamlining the ordering process by offering precise menu details, personalized recommendations …