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International Conference on Developments in eSystems Engineering (DESE)

作者： Khattab M. Ali Alheeti Abdulkareem Alzahrani Omar Hammad Jasim Duaa Al-Dosary Hamsa M. Ahmed Muzhir Shaban Al-Ani Computer Networking Systems Department University of Anbar Ramadi Iraq Faculty of Computer Science and Information Technology Al Baha University Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq Department of Information Technology University of Human Development College of Science and Technology Sulaymaniyah Iraq

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms.

关键词： Measurement Training Intrusion detection Machine learning Malware Complexity theory Reliability

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Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media

arXiv

引用

arXiv 2021年

作者： Wu, Julia Sivaraman, Venkatesh Kumar, Dheekshita Banda, Juan M. Sontag, David Dept of Electrical Engineering and Computer Science Massachusetts Institute of Technology Human-Computer Interaction Institute Carnegie Mellon University Department of Computer Science Georgia State University

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research. © 2021, CC BY.

关键词： Data mining

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HPV Vaccine Hesitancy in Rural America and Exploring Artificial-Intelligence Interventions 58

HPV Vaccine Hesitancy in Rural America and Exploring Artific...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Argyris, Young Anna Nelson, Victoria R. Kwon, Kyungeun Kim, Yongsuk Tan, Pang-Ning Lehto, Georgia Department of Media and Information Michigan State University United States Department of Advertising and Public Relations Michgan State University United States Kookmin University Korea Republic of Department of FinTech Sungkyunkwan University Korea Republic of Department of Computer Science and Engineering Michigan State University United States College of Human Medicine Michigan State University United States

ISBN: (纸本)9780998133188

This study illuminates human papillomavirus (HPV) vaccine hesitancy in rural America and explores the potential of using chatbot-enabled social media interventions. The results from our nationally representative survey among US parents of adolescents (n = 1,196) show the differences in HPV vaccine uptake between rural, suburban, and urban areas. Our results indicate that rural parents' hesitancy in initializing or continuing the HPV vaccine regimen with their children appears to be a result of a lack of knowledge about the necessity, safety, and efficacy of the vaccine. Chatbots with natural language processing abilities are proposed to educate rural parents about the preventive benefits of HPV vaccines and alleviate their hesitancy. With the increasing broadband Internet connectivity in rural communities, chatbots can create effective and economical interventions. The opportunities created by chatbots, their integration into the existing clinical workflow for vaccine deliveries, and their benefits to healthcare providers are discussed. © 2025 IEEE computer Society. All rights reserved.

关键词： Natural language processing systems

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Automated Grading Hemifacial Spasm Using Smartphone Cameras 10

Automated Grading Hemifacial Spasm Using Smartphone Cameras

引用

10th IEEE Smart World Congress, SWC 2024

作者： Chan, Ka I Hei, Bo Meng, Linghao Liu, Ruen Wang, Yuntao Chen, Chang Hao, Qingpei Shi, Yuanchun Global Innovation Exchange Institute Tsinghua University Beijing100084 China Peking University People's Hospital Department of Neurosurgery Beijing100044 China Tsinghua University Department of Computer Science and Technology Beijing100084 China National Key Laboratory of Human Factors Engineering Beijing100094 China Qinghai University Qinghai Xining810016 China

ISBN: (纸本)9798331520861

Hemifacial spasm is a chronic neurological condition characterized by involuntary facial muscle contractions caused by nerve compression. While familiar to specialists, it is less known to the public and general practitioners, which can lead to difficulties in diagnosis and severity assessment, and even misdiagnosis. Consequently, patients are common to have a long medical history. However, long-term patients tend to have poorer outcomes following surgery, and one-third of patients experience a delayed cure during postoperative rehabilitation. Moreover, 4 % of patients experience recurrence, highlighting the importance of early and accurate diagnosis as well as postoperative monitoring. In this paper, we collected a video dataset of 50 hemifacial spasm patients and 9 healthy adults. We identified three facial features from the videos to establish a novel grading system closely aligned with the medical standards, specifically the Cohen-Albert Grading System. We also developed algorithms capable of automatically grading hemifacial spasm using smartphone cameras based on facial keypoint detection. These algorithms were evaluated on the dataset, achieving an accuracy of 88 % for detection and a mean absolute error of 0. 4 2 for grading. © 2024 IEEE.

关键词： Smartphones

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3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems

arXiv

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arXiv 2024年

作者： Zhang, Liang Lin, Jionghao Borchers, Conrad Cao, Meng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Memphis MemphisTN38152 United States

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN’s superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology. © 2024, CC BY.

关键词： Generative adversarial networks

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

arXiv

引用

arXiv 2024年

作者： Vedove, Matteo Dalle Lamon, Edoardo Fontanelli, Daniele Palopoli, Luigi Saveriano, Matteo Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator. © 2024, CC BY-SA.

关键词： Intelligent robots

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The Crafts+Fabrication Workshop: Engaging Students with Intangible Cultural Heritage-Oriented Creative Design 22

The Crafts+Fabrication Workshop: Engaging Students with Inta...

引用

2022 ACM Designing Interactive Systems Conference: Digital Wellbeing, DIS 2022

作者： Lu, Zhicong Tan, Peng Ji, Yi Ma, Xiaojuan Department Of Computer Science City University Of Hong Kong China Center For Human-Engaged Computing Kochi University Of Technology Japan Guangdong University Of Technology China Hong Kong University Of Science And Technology Hong Kong

ISBN: (纸本)9781450393584

Engaging local communities is essential to safeguarding intangible cultural heritage (ICH). To better encourage the participation of local communities, especially younger generations, e.g., students, ICH experience and education workshops are widely adopted by academia, museums, governments, and non-profit organizations. The expected outcomes of these workshops, such as archives, documents, and even creative design solutions, can benefit the promotion and preservation of ICH. However, because of the steep learning curve of using traditional ICH tools and the lack of interactions between students and ICH practitioners, many ICH workshops currently fail to engage students in learning ICH-related knowledge, developing empathy with ICH, or designing novel artifacts with ICH elements. To bridge this gap, we designed a workshop, which integrated ICH in China, digital fabrication, creative technology, and making, to engage Chinese students with ICH and creative design. We conducted empirical studies to collect feedback from students (N = 30) and ICH professionals (N=6). The application of digital fabrication tools successfully piqued students' interest in ICH and enabled them to create interactive ICH artifacts through quick prototyping. However, the ICH professionals pointed out several issues of using digital fabrication, especially regarding tacit knowledge, use of traditional tools, and cultural authenticity. We discuss the importance of these factors in students' acquisition of ICH knowledge and ICH-oriented design, and provide implications for future ICH design workshops. © 2022 ACM.

关键词： Students

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Artificial Intelligence's Function in Chatbots That Use NLP and SVM Algorithms 3

Artificial Intelligence's Function in Chatbots That Use NLP ...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Panduro-Ramirez, Jeidy Melanielourens Anitagehlot Gangodkar, Durga Prasad Tanushreesarkar Sivakumarsattanathan Universidad Tecnológicadel Peru Durban University of Technology Berea Department of Human Resources Management Durban South Africa Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Department of English India Hindustan Institute of Technology and Science Department of Mechatronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350399264

A healthy lifestyle depends heavily on proper healthcare. Nevertheless, it might be exceedingly challenging to schedule a doctor's appointment for any health issue. Before contacting a doctor, the goal is to develop a medical chatbot utilizing machine learning that can identify the illness and offer basic information about it. By using a medical robot, this will save healthcare expenses and increase access to medical information. The bots are computational models that communicate with people by using speech recognition. In order to recognize the language terms, make a query choice, and provide a response, the chatbot saves the data in a database. N-gram, TF-IDF, and mutual information are used to calculate ranking and text similarity. Each verb in the supplied input word will be assigned a score and additional comparable phrases will be found for the specified query. When a bot formulates a query that is unclear or not found in the system, a supplier, an expert software, responds. © 2023 IEEE.

关键词： Natural language processing systems

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Decision Making on Optimal Selection of Advertising Agencies using Machine Learning 9

Decision Making on Optimal Selection of Advertising Agencies...

引用

9th International Conference on Information Management, ICIM 2023

作者： Joe Anand, M. Clement Martin, Nivetha Clementking, Arockiasamy Rani, S. Priyadharshini, Sujitha Siva, S. Department of Mathematics Karnataka Bengaluru560052 India Human Resource Development Karnataka Bengaluru560052 India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai600117 India Pvt. Ltd Karnataka Bengaluru560017 India Reva University Department of Computer Science & Applications Karnataka Bengaluru560064 India

ISBN: (纸本)9798350336382

The decision makers of every business firm take strenuous efforts to achieve the target of sales promotion using different marketing strategies. In recent times, advertising is becoming one of the most preferred and adopted marketing techniques to reach customers at larger magnitude. The business firms irrespective of their production sizes and scales associate with various advertising agencies to familiarize their products to the customers. The advertising strategic approach of marketing also gives rise to a decision-making problem on the optimal choice of advertising agencies as the wrong selection of the agencies harms the sales. This research paper intends to propose a decision making (DM) model integrating both multi-criteria decision methods and machine learning algorithms to make optimal selection of advertising agencies. The proposed DM model considers 25 alternatives of advertising agencies, six criteria, linguistic analytic hierarchy process (LAHP) and random forest algorithm for ranking the advertising agencies. The machine learning (ML) algorithm identifies the feasible alternatives with the help of criterion weights obtained using the method of LAHP. This method is more compatible as it minimizes the risks of decision making by grouping the alternatives into two groups of acceptance and rejection based on their likelihood. The computations are done using Python programming language especially in Google Colabs. © 2023 IEEE.

关键词： Machine learning

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Lee, Jangjae Lee, Sangkeun Matthew Paal, Stephanie German Chinthavali, Supriya Texas A&m University Zachry Department of Civil and Environmental Engineering College StationTX77843 United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN37831 United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak RidgeTN37831 United States

ISBN: (纸本)9798350362480

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model. © 2024 IEEE.

关键词： Extreme weather

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