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Social Media Analysis of Modifiable Lifestyle Factors in Multiple Sclerosis
No full textAround 1 million people in the USA and 2.8 million people around the globe are living with Multiple Sclerosis (MS), which currently has no cure. Incomplete recovery, long-term disability, worsening of symptoms leads to disruption in the Quality of Life among MS patients. However, modifiable lifestyle factors (MLFs) have demonstrated the potential to improve health and wellbeing in MS patients. Therefore, by leveraging social media analytics, this study aims to understand the public perspective on MLFs as reflected in the online generated information. The study identified topics pertaining to MLFs and evaluated the relative prevalence of these topics in social media discourse. The results and analysis showed that Diet and Temperature are the two main factors for MS patients to constantly maintain and monitor. Further, this study can help the medical community in recommending the best lifestyle measures that is necessary for an MS patient
Ant Colony-based Approach for solving an Unmanned Aerial Vehicle Routing Problem
Get PDFWaste management issues are affecting the economic and environmental aspects of modern societies. Thus, growing the interest of academic and industrial research and development in optimizing the process of waste management. As these issues greatly impact human health and environmental aspects and impose a threat, hazardous waste management requires even much more attention. The problem studied in this research is a variant of the vehicle routing problem using an unmanned aerial vehicle (UAV). The focus of this research is on planning the routes for waste collection and disposal using a UAV. The aim is to collect all the waste as early as possible respecting two constraints; the maximum flying and load capacities of the UAV. A two-phase approach has been proposed to solve the investigated problem. This approach is a hybridization of a developed heuristic (IMWMTT) and an Ant Colony Optimization (ACO) algorithm. The experimental study showed that the hybrid approach outperforms a recently published heuristic MWMTT for all tested instances of various size
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW
Get PDFWith a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning. This work takes literature from 2008 to 2022 on the subject of using machine learning for malware classification to understand the impact of this technology on malware classification. Throughout this study, we seek to answer three main research questions: RQ1: Is the application of machine learning for malware classification growing? RQ2: What is the most common machine-learning application for malware classification? RQ3: What are the outcomes of the most common machine learning applications? The analysis of 2186 articles resulting from a data collection process from peer-reviewed databases shows the trajectory of the application of this technology on malware classification as well as trends in both the machine learning and malware classification fields of study. This study performs quantitative and qualitative analysis using statistical and N-gram analysis techniques and a formal literature review to answer the proposed research questions. The research reveals methods such as support vector machines and random forests to be standard machine learning methods for malware classification in efforts to detect maliciousness or categorize malware by family. Machine learning is a highly researched technology with many applications, from malware classification and beyond
Convolutional Autoencoder Neural Network Design Evaluation for an Anomaly Detection Subsystem in Autonomous Spacecraft Computer Vision Systems
Get PDFIn the rapidly advancing field of space technology and defense, the imperative for Security by Design in autonomous spacecraft systems has never been more pressing. This dissertation presents a pioneering approach to adapting this principle, traditionally rooted in software engineering and cybersecurity, to the specialized domain of security-oriented autonomous spacecraft. It addresses a crucial gap between cyber and astronautical sciences, advocating for a proactive stance in security—anticipating and mitigating potential threats in the physical space environment rather than responding to incidents post-factum.
Central to this research is the development of an innovative computer vision anomaly detection system that integrates a convolutional autoencoder with a glowworm swarm optimization algorithm. This novel union is designed to refine anomaly detection methods and establish robust measures for true and false positive detections. The resulting artifact is a cornerstone of a broader, general-purpose computer vision system intricately woven into the spacecraft’s operational fabric, encompassing both flight software and remote agency.
The primary objective of this endeavor is to devise a specialized visual anomaly detection system tailored for autonomous spacecraft, enhancing their capability to conduct security and reconnaissance missions autonomously. By harnessing advanced AI-driven methodologies, the system is engineered to perform nuanced inspections within the unpredictable and complex environment of space, a vital component for preserving situational awareness and mission safety.
A focal point of the study is the system’s ability to monitor and detect anomalies vigilantly—be they natural phenomena or adversarial actions—that could imperil space operations. The proactive detection framework established by this system is poised to revolutionize the operational security of space assets, ensuring a preemptive defense mechanism against emergent and evolving threats. The implications of this research extend beyond immediate applications, as it lays the groundwork for future innovations in autonomous defense systems, potentially transforming the landscape of space exploration and security
A Knowledge Extraction Approach for IT Tech-support Transcripts
No full textIn information technology support/helpdesk transcripts, most of the data of interest, such as the issue of concern, issue severity, context, and system status, is not provided in a structured form. Moreover, the special traits of product issue orientation, implicit background knowledge, and off-topic dialogues require a domain specialized approach to extract knowledge from these transcripts. Accordingly, this study analyzes the specific domain requirements and proposes a novel solution based on Natural Language Processing (NLP) approach. In the core process, this approach uses an adapted term frequency-inverse document frequency (TF-IDF) algorithm by adding a new parameter reflecting the term’s priority in the text. Experimental results show that the proposed NLP-based solution performs reasonably well in topic categorizing with an accuracy of 92.8%. Compared to the performance of keywords extraction, the proposed approach achieves an accuracy of 93.4%, which outperforms the classic TF-IDF method signifying the importance of extracting and accommodating domain-specific knowledge
Deep Neural Network at Edge: An Exploration of Hyper Parameter Tuning on Plant Seedling Dataset
No full textThis study explored different hyperparameters and their outcomes on multiple DNN architectures by incorporating transfer learning technique on the plant seedling dataset. Optimizers can have different strengths and weaknesses, and their performance may depend on the specific characteristics of the model and the dataset being used. We have observed that the choice of the optimizer is just one of many hyperparameters that can affect the performance of deep neural network (DNN) models. Other hyperparameters, such as the droprate, number of epochs, and batch size, can also have a significant impact. MobileNetV2 demonstrated superior performance while maintaining a smaller model size, making it a highly valuable option for edge devices where size is a crucial factor
Mining mobile application reviews to inform the design of anonymous live counseling applications
Get PDFMental health issues among American youth continue to increase. Technology integration and acceptance represent modern opportunities to provide more access to licensed professionals. School counselors are mental health experts in schools uniquely positioned to provide social and emotional support to students. However, not all students have access to a school counselor or another mental health professional. In 2018, Utah developed the SafeUT app to increase services provided by school counselors and licensed clinicians to students in the state. This free mobile app provides confidential, real-time crisis intervention through an anonymous live chat and confidential tip program. Although the number of tips continue to increase each year, little research explores the design principles influencing the user\u27s experience of anonymous live counseling applications. This research in progress paper uses data mining techniques on app reviews from the SafeUT mobile application to explore the common themes relating to design principles. Our results may inform future research and improve the design of similar applications to increase students’ access to counselors and the much-needed mental health services these professionals can provide
Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms
Get PDFHealthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs
Native American Community Digital Divide: Student Insights
Get PDFThe digital divide continues to be an issue for many Native American individuals in rural tribal areas. This research used a qualitative grounded theory method from the data collection of semi-structured interviews with Native American university students. The open coding of the transcribed responses was used to analyze the text data from individual Native American experiences. The data analysis codes included cost, location, access, digital literacy, and technology knowledge as continuing issues. The coding also shows limited technical support or training availability in the communities. The absence of technology use increases the need to understand factors that remain digital divide barriers for Native American communities. The digital divide - individual experiences model (DD-IEM) is based on three main categories: community, education, and home environments. Six propositions produced the DD-IEM that encompasses digital environments within the three settings that are unique to each individual
Tunneling time and Faraday/Kerr effects in PT-symmetric systems
Get PDFWe review the generalization of tunneling time and anomalous behaviour of Fara- day and Kerr rotation angles in parity and time (PT )-symmetric systems. Similarities of two phenomena are discussed, both exhibit a phase transition-like anomalous behaviour in a certain range of model parameters. Anomalous behaviour of tunneling time and Faraday/Kerr angles in PT -symmetric systems is caused by the motion of poles of scattering amplitudes in the en- ergy/frequency complex plane