EAI Endorsed Transactions on Context-aware Systems and Applications
type: channel
description: EAI Endorsed Transactions on Context-aware Systems and Applications (CASA) is a place for highly original ideas about how context-aware systems are going to shape networked computing systems of the future. Hence, it focuses on rigorous approaches and cutting-edge solutions which break new ground in dealing with the properties of context-awareness. eISSN: 2409-0026 INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Context-aware Systems and Applications
issn: 2409-0026
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY NC SA 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: Temporal assessment of cognitive load factors using ocular features during a visual search
items: Reflecting Society Through Art: The Aesthetic Evolution of Paintings
items: Tracing the Evolution of Max-Min Aggregation and Fuzzy Systems in AI: A Bibliometric Review
items: Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications
items: Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review
items: A Review of Quantum Lambda Calculi: Linearity, Semantics, and Programming Models
items: Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning
items: A Survey of Quantum Type Theory: From Linearity to Formal Verification
items: Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings
items: Analyzing online reviews at the word level to understand customer experience
items: A hybrid classification model in improving the classification quality of network intrusion detection systems
items: Augmented Reality and Virtual Reality Crafting New Dimensions of Human Perception, Interaction, and Societal Evolution
items: Human Emotion Recognition with an Advanced Vision Transformer Model
items: Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra
items: Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques
items: Elevating User-Centered Design with AI: A Comprehensive Exploration using the AI-UCD Algorithm Framework
items: UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis
items: Integration and Recommendation System of Profiles based on Professional Social Networks
items: Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning
items: Manipulation of the Multi-Vehicle System for the Industrial Applications
items: Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam
items: Study of Robot Manipulator Control via Remote Method
items: Facial Sentiment Recognition using artificial intelligence techniques.
items: The data preprocessing in improving the classification quality of network intrusion detection systems
items: Breast Tumor Classification using Machine Learning
items: Human Centered Design and Design Thinking for Entrepreneurship
items: Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification
items: Control issues, artificial neural network (ANN) for acrobot system
items: Creative Brain Training Apps and Games Can Help Improve Memory, Cognitive Abilities, and Promote Good Mental Health for The Elderly
items: A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data

Temporal assessment of cognitive load factors using ocular features during a visual search
type: item
description: The possibility of evaluating temporal changes in cognitive workloads during a visual search task is examined using microsaccade (MS) rates and pupillary changes. The experimental task was designed as a search for a speciﬁc ﬁgure, where task diﬃculty and reaction accuracy during the trials were controlled. Individual cognitive workloads were measured after the experimental sessions were conducted, using NASA-TLX scale ratings. Temporal changes in the cognitive load were identiﬁed using metrics of oculomotors during two stages of task processing, by comparing cognitive loads with individual ratings on a scale. Since the source of the load may be a common one, changes in latent attention resources required for the task were estimated with a designated state-space model, using the observation data in order to synthesise measurement of MS rates and pupillary changes. The predicted levels of attention resources correspond to the activity during the performance of the experimental tasks during the trials, and reﬂected some of the rating scores for workload scales. Also, the ranges of conﬁdence intervals for attention resources correlate signiﬁcantly with the ratings for information processing at the stage where visual stimulus is presented during tasks.
subject: Research article
  topic: section
subject: cognitive load
  topic: keywords
subject: microsaccade rate
  topic: keywords
subject: pupil change
  topic: keywords
subject: modelling
  topic: keywords
subject: chronological analysis
  topic: keywords
creator: Minoru Nakayama
creator: Tomomi Okano
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-08-05
publication date: 2025-08-05
doi: 10.4108/eetcasa.8797

Reflecting Society Through Art: The Aesthetic Evolution of Paintings
type: item
description: The COVID-19 pandemic has sparked a powerful artistic response in the form of a captivating painting series, offering a compelling reflection of society during this unparalleled crisis. Comprising five distinctive pieces, these artworks provide a poignant glimpse into humanity's collective response to the pandemic, encapsulating emotions ranging from fear and anxiety to resilience and adaptability. Central to this artistic narrative is the face mask, an innovation born out of necessity that has come to symbolize humanity's resolve to adapt for survival. The mask represents not only our need for protection but also serves as a tangible reminder of our shared struggle and our capacity to respond to adversity with creativity and unity. Through this artistic endeavor and subsequent exhibition, the artist skillfully navigates the complexities of human existence. Life, as depicted in these artworks, is a tapestry woven with threads of happiness and suffering, where smiles and tears coexist harmoniously, reflecting the intricate truth of our journey. These paintings compel us to confront life's challenges and adapt, mirroring the collective response to the pandemic. They remind us that, as individuals and as a society, we possess the resilience to persevere and evolve in the face of adversity. This series is a testament to the indomitable human spirit, showcasing our ability to find strength and beauty even amidst the most trying circumstances. In summary, this artistic painting series is a profound exploration of the human experience during the COVID-19 pandemic. It encapsulates our shared emotions and the significance of adaptation as a vital element of survival. These artworks serve as a poignant reminder that, no matter the challenges we encounter, we have the inner resources to endure, thrive, and discover moments of profound beauty amidst adversity.
subject: Research article
  topic: section
subject: The COVID-19 pandemic
  topic: keywords
subject: Reflection of society
  topic: keywords
subject: Adapatability
  topic: keywords
subject: The mask
  topic: keywords
subject: Human spirit
  topic: keywords
creator: Nattanun Siricharoen
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-22
publication date: 2025-07-22
doi: 10.4108/eetcasa.8849

Tracing the Evolution of Max-Min Aggregation and Fuzzy Systems in AI: A Bibliometric Review
type: item
description: This paper presents a bibliometric review of Max-Min aggregation functions and fuzzy systems in artificial intelligence (AI) from 1990 to 2024. Drawing on data from Scopus and analyzed using Bibliometrix and VOSviewer, we map publication trends, key contributors, thematic developments, and emerging research areas. The findings reveal growing interest in interpretable AI, neuro-fuzzy models, and hybrid systems. We highlight the integration of Max-Min aggregation in explainable AI and identify key research gaps. This review provides a structured overview of the field’s evolution and offers guidance for future research directions.
subject: Review article
  topic: section
subject: Max-Min Aggregation
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Fuzzy Systems
  topic: keywords
subject: Neuro-Fuzzy Models
  topic: keywords
creator: Nguyen Van Han
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-22
publication date: 2025-07-22
doi: 10.4108/eetcasa.9750

Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications
type: item
description: This systematic review investigates the use of max-min aggregation in fuzzy systems and interpretable machine learning. Rooted in fuzzy set theory and triangular norms, max-min aggregation offers a transparent and mathematically simple approach to modeling uncertainty and decision-making. We examine theoretical foundations, practical applications, evaluation methods, and comparative taxonomies. The review identifies key challenges such as scalability and integration with learning algorithms, and highlights future directions for improving transparency in AI. Our findings underscore the relevance of max-min aggregation in developing interpretable and responsible AI systems.
subject: Review article
  topic: section
subject: Systematic Review
  topic: keywords
subject: Aggregation Operators
  topic: keywords
subject: Linguistic Modeling
  topic: keywords
subject: Interpretable Machine Learning
  topic: keywords
subject: Max-Min Aggregation
  topic: keywords
subject: Explainable Artifical Intelligence (XAI)
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
creator: Nguyen Van Han
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-22
publication date: 2025-07-22
doi: 10.4108/eetcasa.9752

Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review
type: item
description: Max-min aggregation functions play a fundamental role in fuzzy linguistic systems and machine learning by providing interpretable and mathematically sound methods for combining imprecise and qualitative information. This narrative review synthesizes the key concepts, models, and applications of max-min aggregation, highlighting its significance in enabling human-centric reasoning and explainable AI. We discuss theoretical foundations, linguistic modeling frameworks, and diverse practical applications, including decision support systems and fuzzy rule-based classifiers. Challenges such as scalability, integration with deep learning, and semantic standardization are identified, along with promising future research directions. This review aims to provide a comprehensive understanding of max-min aggregation’s contributions to interpretable and flexible AI systems
subject: Review article
  topic: section
subject: Linguistic Variables
  topic: keywords
subject: Aggregation Operators
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Max-Min Aggregation
  topic: keywords
subject: Fuzzy logic
  topic: keywords
subject: Fuzzy Linguistic Systems
  topic: keywords
creator: Nguyen Van Han
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-22
publication date: 2025-07-22
doi: 10.4108/eetcasa.9751

A Review of Quantum Lambda Calculi: Linearity, Semantics, and Programming Models
type: item
description: Quantum lambda calculi extend classical lambda calculus to model quantum computation by integrating linear types, quantum operations, and classical control. This paper surveys key calculi—including QΛ, QLC, Proto-Quipper, and QML—highlighting their design principles, type systems, and semantic foundations. By comparing their approaches to handling quantum data, control flow, and circuit construction, we provide insights into the current state and future directions of quantum programming language research.
subject: Review article
  topic: section
subject: Quantum Lambda Calculus
  topic: keywords
subject: Quantum Programming Lanugages
  topic: keywords
subject: Quantum Circuits
  topic: keywords
subject: Formal Semantics
  topic: keywords
creator: Tran Ngoc Dan
creator: Nguyen Thi Kim Phung
creator: Nguyen Thi Hong Tu
creator: Nguyen Van Han
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-17
publication date: 2025-07-17
doi: 10.4108/eetcasa.9668

Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning
type: item
description: Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data. However, traditional GNNs often fail to address uncertainty inherent in many real-world applications. Fuzzy Graph Neural Networks (FGNNs) integrate fuzzy logic into GNNs to provide a robust mechanism for managing uncertainty, imprecision, and vagueness. This paper presents a comprehensive review of FGNNs, examining their theoretical underpinnings, methodologies, applications, challenges, and potential research directions.
subject: Review article
  topic: section
subject: Fuzzy Graph Neural Networks
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Graph Representation Learning
  topic: keywords
subject: Explainable
  topic: keywords
creator: Ngoc Dan Tran
creator: Thi Nhung Tong
creator: Thi Kim Phung Nguyen
creator: Thi Hong Tu Nguyen
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by/3.0/
date: 2025-07-16
publication date: 2025-07-16
doi: 10.4108/eetcasa.9483

A Survey of Quantum Type Theory: From Linearity to Formal Verification
type: item
description: Quantum Type Theory (QTT) provides a formal system that combines ideas from quantum mechanics, type theory, and logic to support reliable quantum programming. Since quantum information cannot be copied or deleted like classical data, QTT uses linear types to ensure that quantum operations follow the laws of physics. This paper reviews the main concepts in QTT, such as linear functions, tensor products, and dependent types, and explains how they help programmers write safe and correct quantum code.
subject: Review article
  topic: section
subject: Quantum Type Theory
  topic: keywords
subject: Type Systems
  topic: keywords
subject: Quantum Programming Languages,
  topic: keywords
subject: Linear Logic
  topic: keywords
subject: Dependent Types
  topic: keywords
creator: Van Han Nguyen
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-16
publication date: 2025-07-16
doi: 10.4108/eetcasa.9669

Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings
type: item
description: Graph Neural Networks (GNNs) have emerged as a powerful tool for learning representations in graph structured data. However, traditional message-passing mechanisms often struggle with uncertainty and noise in node features and graph topology. In this paper, we propose Fuzzy Message Passing (FMP), a novel approach that integrates fuzzy max-min aggregation into GNNs to improve robustness against uncertainty. Our method enhances node embeddings by leveraging fuzzy logic principles, ensuring better stability and interpretability in complex graph tasks. Experimental results on benchmark datasets demonstrate that FMP outperforms conventional message-passing schemes, particularly in scenarios with noisy or incomplete data.
subject: Short communications
  topic: section
subject: Graph Neural Networks
  topic: keywords
subject: Message Passing
  topic: keywords
subject: Uncertainty
  topic: keywords
subject: Node Embeddings
  topic: keywords
subject: Max-Min Aggregation
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
creator: Minh Tuan Duong
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-15
publication date: 2025-07-15
doi: 10.4108/eetcasa.8947

Analyzing online reviews at the word level to understand customer experience
type: item
description: INTRODUCTION: In the competitive business environment, customer experience plays a pivotal role in driving brand success. Brands that deliver exceptional customer experiences benefit from increased loyalty, advocacy, and stronger market differentiation. With the rise of digital platforms, customers frequently share post-purchase experiences online, making sentiment analysis essential for strategic marketing. OBJECTIVES: This study aims to explore customer experience with the Trung Nguyen Legend coffee brand by analyzing user-generated content on TripAdvisor. It seeks to identify key aspects of customer feedback and measure satisfaction and loyalty levels. METHODS: The research employs natural language processing (NLP) techniques and Python-based sentiment analysis tools. Specifically, aspect-based sentiment analysis (ABSA) is used to extract and evaluate sentiment associated with different service dimensions based on online reviews. RESULTS: The analysis reveals that Trung Nguyen Legend achieves a Customer Satisfaction (CSAT) score exceeding 66% and a Net Promoter Score (NPS) over 34%. These results indicate a generally positive customer experience, with specific strengths and areas for improvement clearly identified. CONCLUSION: The study demonstrates that ABSA is a cost-effective and time-efficient method for understanding customer sentiment. The findings offer valuable insights for enhancing customer experience management and inform strategic improvements for the Trung Nguyen Legend brand.
subject: Research article
  topic: section
subject: Customer Experience
  topic: keywords
subject: Trung Nguyen Legend
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: CSAT
  topic: keywords
subject: NPS
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Ha Thi Thu Nguyen
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-07-08
publication date: 2025-07-08
doi: 10.4108/eetcasa.7059

A hybrid classification model in improving the classification quality of network intrusion detection systems
type: item
description: Stream-based anomaly detection is an issue that continues to be researched in the cybersecurity environment. Much previous research has applied machine learning as a method to improve anomaly detection in network intrusion detection systems. Recent research shows that network intrusion detection systems still face challenges in improving accuracy, reducing false alarm rates, and detecting new attacks. The article proposes a hybrid classification model that combines improved data preprocessing techniques with ensemble techniques. Experimental results on the UNSW-NB15 dataset show that the proposed solutions have helped improve the classification quality of network intrusion detection systems compared to some other research.
subject: Research article
  topic: section
subject: Machine Learning
  topic: keywords
subject: NIDS
  topic: keywords
subject: Ensemble
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Resampling
  topic: keywords
subject: UNSW-NB15
  topic: keywords
creator: Thanh Hoàng Ngọc
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-06-17
publication date: 2025-06-17
doi: 10.4108/eetcasa.6735

Augmented Reality and Virtual Reality Crafting New Dimensions of Human Perception, Interaction, and Societal Evolution
type: item
description: The rapid evolution of Augmented Reality (AR) and Virtual Reality (VR) is not just an expansion of human-computer interaction but a radical redefinition of human existence itself. This paper delves into the extraordinary potential of AR and VR to reshape reality, alter consciousness, and create immersive, parallel dimensions. As AR/VR technologies blur the boundaries between the physical and digital realms, they are poised to redefine industries, social structures, and even the nature of human cognition. We explore how AR/VR could soon transcend entertainment and education, emerging as the cornerstone of a hybrid reality where the digital and physical worlds are indistinguishable. Furthermore, we speculate on their role in crafting synthetic worlds, which challenge our notions of reality, identity, and interaction, and question what it means to exist in an era where the virtual is as real as the tangible.
subject: Research article
  topic: section
subject: Augmented Reality, Virtual Reality, Synthetic Worlds, Reality Distortion, Hybrid Dimensions
  topic: keywords
creator: Waralak Vongdoiwang Siricharoen
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-05-27
publication date: 2025-05-27
doi: 10.4108/eetcasa.8994

Human Emotion Recognition with an Advanced Vision Transformer Model
type: item
description: This paper proposes a novel deep-learning technique that leverages the Efficient Vision Transformer –M5 (Efficient ViT-M5) model to improve the existing design by offering a more computationally economical version that maintains good performance, making it highly suitable for practical applica-tions. The utilization of transfer learning involved leveraging pre-trained weights from the ImageNet dataset, substantially enhancing the model's accu-racy and efficiency. The proposed method involves training the advanced Effi-cientViTM5 model utilizing three widely recognized facial emotion recognition datasets: FER2013+, AffectNet, and RAF-DB. A comprehensive data augmentation pipeline is employed to enhance the diversity of the training data and bolster the model's robustness. The trained proposed model proved exceptional accuracy rates of 94.28% (FER2013+), 94.69% (AffectNet), and 97.76% (RAF-DB). The results emphasize the strength and effectiveness of the proposed model in identifying face emotions in various datasets, showcasing its potential for practical use in emotion-aware computing, security, and health diagnostics. The research significantly improves facial emotion recognition by introducing a reliable and practical way of recognizing emotions using cutting-edge deep learning techniques. The results show the possibility of enhancing and flexible interactions between humans and computers, highlighting the efficacy of sophisticated deep learning models in addressing complex computer vision problems.
subject: Research article
  topic: section
subject: facial expression
  topic: keywords
subject: facial emotion detection
  topic: keywords
subject: face recognition
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: ViT
  topic: keywords
subject: EffectiveViT-M5
  topic: keywords
creator: Kha Tu Huynh
creator: Vo Nhat Anh Nguyen
creator: Tan Duy Le
creator: Thuong Le-Tien
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2025-04-30
publication date: 2025-04-30
doi: 10.4108/eetcasa.8101

Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra
type: item
description: This paper presents an innovative approach to modeling Linguistic Fuzzy Maximum Spanning Trees (L-FMSTs) using Hedge Algebra (HA). HA provides a robust framework for quantifying linguistic terms, which is essential for handling the vagueness inherent in natural language. By integrating HA with L-FMSTs, we aim to enhance the interpretability and performance of fuzzy systems in applications requiring complex decision-making and optimization.
subject: Short communications
  topic: section
subject: Linguistic Fuzzy Maximum Spanning Tree
  topic: keywords
subject: Hedge Algebra
  topic: keywords
subject: Fuzzy graph
  topic: keywords
subject: Linguistic Variables
  topic: keywords
creator: Nguyen Van Han
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2024-09-27
publication date: 2024-09-27
doi: 10.4108/eetcasa.7337

Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques
type: item
description: One of the most reliable information sources is video, and in recent years, online and offline video consumption has increased to an unprecedented degree. One of the main difficulties in extracting information from videos is that unlike images, where information can be gleaned from a single frame, a viewer must watch the entire video in order to comprehend the context. In this work, we try to use various algorithmic techniques, such as deep neural networks and local features, in conjunction with a variety of clustering techniques, to find an efficient method of extracting interesting key frames from videos to summarize them. Video summarization plays a major role in video indexing, browsing, compression, analysis, and many other domains. One of the fundamental elements of video structure analysis is key frame extraction, which pulls significant frames out of the movie. An important frame from a video that may be used to summarize videos is called a key frame. We provide a technique that leverages convolutional neural networks in our suggested model, static video summarization, and key frame extraction from movies.
subject: Technical Article
  topic: section
subject: Video summarization
  topic: keywords
subject: Key Extraction
  topic: keywords
subject: Edge Detection
  topic: keywords
subject: Motion Analysis
  topic: keywords
subject: Key frames
  topic: keywords
creator: Anjali H Kugate
creator: Bhimambika Y Balannanavar
creator: R.H Goudar
creator: Vijayalaxmi N Rathod
creator: Dhananjaya G M
creator: Anjanabhargavi Kulkarni
creator: Geeta Hukkeri
creator: Rohit B. Kaliwal
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2024-07-17
publication date: 2024-07-17
doi: 10.4108/eetcasa.5131

Elevating User-Centered Design with AI: A Comprehensive Exploration using the AI-UCD Algorithm Framework
type: item
description: This paper presents a comprehensive exploration of the synergistic relationship between User-Centered Design (UCD) and Artificial Intelligence (AI) within the context of the AI-UCD Algorithm Framework. With the growing influence of AI in digital interfaces, the need to prioritize user needs and preferences has become paramount. The AI-UCD Framework, consisting of nine pivotal steps, acts as a structured guide for integrating AI into user interfaces while ensuring a user-centric, data-driven, and ethical approach. The exploration begins by highlighting the importance of understanding user needs and context through robust user research and contextual inquiry. It then delves into the process of defining AI integration objectives and brainstorming AI-enhanced solutions, emphasizing the creative aspects of UCD in tandem with AI capabilities. Subsequently, the paper discusses the critical role of designing AI-driven interfaces, from information architecture to user flow design, ensuring seamless integration of AI features.Implementation and testing of AI features are addressed, highlighting the collaboration between UI/UX designers and AI developers. The paper emphasizes the iterative nature of the framework, relying on usability testing and user feedback to drive continuous improvements. Moreover, it considers user training and assistance, a vital aspect of introducing users to AI features.The framework's data-driven aspect is covered by discussing data collection, analysis, and performance monitoring to ensure AI features are meeting objectives and KPIs. Additionally, the exploration addresses AI's role in personalization, adapting to user behavior and preferences. It recognizes the ethical dimensions of AI, promoting transparency, fairness, and accessibility.The paper then presents a five-step AI-UCD Validation Model, designed to verify the framework's effectiveness in real-world applications. These validation steps encompass user testing and feedback, data analysis, e…
subject: Research article
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: User-Centered Design (UCD)
  topic: keywords
subject: AI-UCD Framework
  topic: keywords
subject: Human Centered Design
  topic: keywords
subject: User Experience (UX)
  topic: keywords
creator: Waralak Vongdoiwang Siricharoien
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2024-03-15
publication date: 2024-03-15
doi: 10.4108/eetcasa.4211

UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis
type: item
description: In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."
subject: Research article
  topic: section
subject: Breast Cancer
  topic: keywords
subject: Classification
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Segmentation
  topic: keywords
subject: Ultrasonic Image
  topic: keywords
creator: Tran Cao Minh
creator: Nguyen Kim Quoc
creator: Phan Cong Vinh
creator: Dang Nhu Phu
creator: Vuong Xuan Chi
creator: Ha Minh Tan
rights: Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2024-01-12
publication date: 2024-01-12
doi: 10.4108/eetcasa.4681

Integration and Recommendation System of Profiles based on Professional Social Networks
type: item
description: The aim of our investigation is to personalize bilateral recommendation of job-related proposals based on existing professional social networks. In a context where the points of view of job seekers and employers can be contradictory, our approach consists in trying to bring the both in a best possible matching. To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions.
subject: Research article
  topic: section
subject: Integration system
  topic: keywords
subject: job recommender system
  topic: keywords
subject: social recommendations
  topic: keywords
subject: personalized recommendations
  topic: keywords
subject: bilateral matching problem
  topic: keywords
creator: Paul Dayang
creator: Ulriche Mbouche Bomda
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by/4.0/
date: 2024-01-15
publication date: 2024-01-15
doi: 10.4108/eetcasa.4500

Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning
type: item
description: The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.
subject: Research article
  topic: section
subject: COVID-19
  topic: keywords
subject: influenza-like illness
  topic: keywords
subject: machine learning
  topic: keywords
subject: predictive modeling
  topic: keywords
subject: xgboost
  topic: keywords
subject: random forest
  topic: keywords
subject: diagnostic parameters
  topic: keywords
subject: clinical symptoms
  topic: keywords
subject: disease detection
  topic: keywords
subject: pandemic response strategies
  topic: keywords
creator: Dang Nhu Phu
creator: Phan Cong Vinh
creator: Nguyen Kim Quoc
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-10-10
publication date: 2023-10-10
doi: 10.4108/eetcasa.v9i1.4030

Manipulation of the Multi-Vehicle System for the Industrial Applications
type: item
description: This approach should indicate some challenges in routing and scheduling for the multi-vehicle system. The proposed method delivers a novel method to generate the free-collision trajectory as well as optimal route from starting point to destination. The estimated time at one node and the classification of load level support vehicle to decide which proper route is and stable movement is reached. From these results, it could be observed that the proposed approach is feasible and effective for many applications. The proposed method for routing and scheduling might be useful in the multi-vehicle system. In the large scale system, some intelligent schemes should be considered to integrate.
subject: Research article
  topic: section
subject: Robotics
  topic: keywords
subject: Intelligent System
  topic: keywords
subject: Motion Control
  topic: keywords
subject: Complex System
  topic: keywords
creator: Lourve Vincent
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-10-02
publication date: 2023-10-02
doi: 10.4108/eetcasa.v9i1.3978

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam
type: item
description: The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical method is appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.
subject: Research article
  topic: section
subject: Geostatistics
  topic: keywords
subject: COVID-19
  topic: keywords
subject: Kriging
  topic: keywords
subject: Statistical
  topic: keywords
subject: Interpolation
  topic: keywords
creator: Nguyen Cong Nhut
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-09-25
publication date: 2023-09-25
doi: 10.4108/eetcasa.v9i1.3954

Study of Robot Manipulator Control via Remote Method
type: item
description: INTRODUCTION: The study introduces a novel approach to the design and management of industrial robots using virtual reality technology, enabling humans to observe a wide range of robot behaviors across various environments. OBJECTIVES: Through a simulation program, the robot's movements can be reviewed, and a program for real-world task execution can be generated. Furthermore, the research delves into the algorithm governing the interaction between the industrial robot and humans. METHODS: The robot utilized in this research project has been meticulously refurbished and enhanced from the previously old version robotic manipulator, which lacked an electrical cabinet derived. RESULTS: Following the mechanical and electrical upgrades, a virtual setup, incorporating a headset and two hand controllers, has been integrated into the robot's control system, enabling control via this device. CONCLUSION: This control algorithm leverages a shared control approach and artificial potential field methods to facilitate obstacle avoidance through repulsive and attractive forces. Ultimately, the study presents experimental results using the real robot model.
subject: Research article
  topic: section
subject: Robotics
  topic: keywords
subject: Intelligent System
  topic: keywords
subject: Motion Control
  topic: keywords
subject: Complex System
  topic: keywords
creator: Tuan Nguyen
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-09-25
publication date: 2023-09-25
doi: 10.4108/eetcasa.v9i1.3884

Facial Sentiment Recognition using artificial intelligence techniques.
type: item
description: Facial emotion recognition technology is used to analyze and recognize human emotions based on facial expressions. This technology uses deep learning models to classify facial expressions, eyes, eyebrows, mouth, and other facial expressions to determine a person's emotions. The application of facial emotion recognition in the field of education is a potential way to evaluate the level of student absorption after each class period. Using cameras and emotion recognition technology, the system can record and analyze students' facial expressions during class. In this paper, we use the Convolutional Neural Network (CNN) algorithm combined with the linear regression analysis method to build a model to predict students' facial emotions over a period of time camera recorded.
subject: Research article
  topic: section
subject: Facial Sentiment Recognition
  topic: keywords
subject: Convolutional artificial neural network
  topic: keywords
subject: Linear regression
  topic: keywords
subject: Satisfied prediction
  topic: keywords
creator: Vuong Xuan Chi
creator: Phan Cong Vinh
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-09-22
publication date: 2023-09-22
doi: 10.4108/eetcasa.v9i1.3930

The data preprocessing in improving the classification quality of network intrusion detection systems
type: item
description: Stream-based intrusion detection is a growing problem in computer network security environments. Many previous researches have applied machine learning as a method to detect attacks in network intrusion detection systems. However, these methods still have limitations of low accuracy and high false alarm rate. To improve the quality of classification, this paper proposes two solutions in the data preprocessing stage, that is, the solution of feature selection and resampling of the training dataset before they are used for training the classifiers. This is based on the fact that there is a lot of class imbalanced data in the training dataset used for network intrusion detection systems, as well as that there are many features in the dataset that are irrelevant to the classification goal, this reduces the quality of classification and increases the computation time. The data after preprocessing by the proposed algorithms is used to train the classifiers using different machine learning algorithms including: Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines, k Nearest Neighbor and Artificial Neural Network. The training and testing results on the UNSW-NB15 dataset show that: as with the Reconnaissance attack type, the proposed feature selection solution for F-Measure achieves 96.31%, an increase of 19.64%; the proposed oversampling solution for F-Measure achieves 6.99%, an increase of 3.17% and the proposed undersampling solution for F-Measure achieves 94.65%, an increase of 11.42%.
subject: Research article
  topic: section
subject: Feature Selection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: NIDS
  topic: keywords
subject: Resampling
  topic: keywords
subject: UNSW-NB15
  topic: keywords
creator: Hoàng Ngoc Thanh
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-09-06
publication date: 2023-09-06
doi: 10.4108/eetcasa.v9i1.3778

Breast Tumor Classification using Machine Learning
type: item
description: One of the most contagious illnesses and the second-leading cause of cancer-related death in women is breast cancer. Early detection of tumor is critical for providing healthcare providers with useful clinical information which can help them make a more accurate diagnosis. To accurately diagnose breast cancer, a computer-aided detection (CAD) system that employs machine learning is required. The paper proposes web based tumor prediction system which analyzes different machine learning algorithms for breast tumor classification to determine the best performing model. Different evaluation criteria namely accuracy, ROC AUC, etc are mostly employed for evaluating models but they make the selection of the best model strenuous. A multi-criteria decision making (MCDM) approach has been employed for selecting the best performing model. Further, a web-based portal has been developed to provide the user interface for this functionality.
subject: Research article
  topic: section
subject: Machine Learning
  topic: keywords
subject: Tumor Classification
  topic: keywords
subject: Accuracy
  topic: keywords
subject: MCDM
  topic: keywords
subject: Breast Cancer
  topic: keywords
creator: Salman Siddiqui
creator: Mohd Usman Mallick
creator: Ankur Varshney
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-08-15
publication date: 2023-08-15
doi: 10.4108/eetcasa.v9i1.3600

Human Centered Design and Design Thinking for Entrepreneurship
type: item
description: What are entrepreneurs really thinking? And how can they use human-centered design to make their businesses successful? In this article, we explore the answers to these questions and more. Design thinking can be an incredibly powerful tool, helping entrepreneurs hone their products and services to perfection. And when combined with human-centered design? Well, you have a recipe for success! Still not convinced? Keep reading to learn more about the benefits of using both design thinking and human-center design in your business.
subject: Research article
  topic: section
subject: Human Centered Design
  topic: keywords
subject: Entrepreneurship
  topic: keywords
subject: Impact
  topic: keywords
subject: Design Thinking
  topic: keywords
creator: Waralak Vongdoiwang Siricharoen
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-08-14
publication date: 2023-08-14
doi: 10.4108/eetcasa.v9i1.3203

Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification
type: item
description: Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.
subject: Research article
  topic: section
subject: iris recognition
  topic: keywords
subject: periocular recognition
  topic: keywords
subject: iris segmentation
  topic: keywords
subject: iris biometric
  topic: keywords
subject: personal identification
  topic: keywords
creator: Nguyen Kim Quoc
creator: Ha Minh Tan
creator: Dang Nhu Phu
creator: Vuong Xuan Chi
creator: Phan Cong Vinh
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-08-14
publication date: 2023-08-14
doi: 10.4108/eetcasa.v9i1.3708

Control issues, artificial neural network (ANN) for acrobot system
type: item
description: Acrobot is a robotic system with several levels of operational states investigated by the author. Due to the limited nature of the investigation under certain ideal conditions, designers have to create some algorithms that control the system most appropriately in a given working environment. In this paper, the author proposed the problem of designing, modeling and controlling an acrobot system, including ANN. Mathematical models, Simulink are also presented in a specific way. Simulation parameters have been adjusted to be the most suitable and intuitive. Based on the simulation data, the performance analysis of the system becomes more accurate. Above suggestions are intended to serve vocational education and scientific research. ANN is the most intelligent control method currently added in this paper to firmly confirm its effectiveness in all problems. Proposing control strategies for different models is also applied by the author.
subject: Research article
  topic: section
subject: acrobot
  topic: keywords
subject: PID strategy
  topic: keywords
subject: LEAD strategy
  topic: keywords
subject: LAG strategy
  topic: keywords
subject: ANN
  topic: keywords
creator: Nguyen Danh
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-08-03
publication date: 2023-08-03
doi: 10.4108/eetcasa.v9i1.2782

Creative Brain Training Apps and Games Can Help Improve Memory, Cognitive Abilities, and Promote Good Mental Health for The Elderly
type: item
description: This qualitative research study aims to explore the effectiveness of creative brain training apps and games in enhancing memory, cognitive abilities, and promoting good mental health among the elderly. The research is based on feedback and experiences provided by a second-year university student who has close relationships with elderly individuals. Data will be collected between June and July 2023. The informant's firsthand experiences and insights will be utilized to evaluate games and applications specifically designed to strengthen memory, thinking, analysis, brain exercises, eyesight, and other cognitive functions. By investigating the impact of these interventions, this study aims to contribute valuable information to enhance the mental well-being and cognitive capabilities of the elderly population.
subject: Research article
  topic: section
subject: elderly
  topic: keywords
subject: thinking analysis
  topic: keywords
subject: cognitive abilities
  topic: keywords
subject: mental health
  topic: keywords
subject: brain training apps
  topic: keywords
subject: brain exercises
  topic: keywords
creator: Nattanun Siricharoen
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-07-28
publication date: 2023-07-28
doi: 10.4108/eetcasa.v9i1.3524

A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data
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description: INTRODUCTION: Although scientific hypothesis testing methodologies are well established, their application to falsifiable hypothesis testing for assessing causal relationships potentially identified by machine learning and artificial intelligence models is rare due to the primarily nonparametric statistical nature of these systems. OBJECTIVES: The primary objective of this study is to demonstrate the potential for applying nonparametric statistical tests to a mixed qualitative and quantitative cyber network dataset as a method to pre-assess the feasibility of applying forms of statistical hypothesis testing before a machine learning algorithm models the data. METHODS: A mixture of permuted analysis of variance models augmented by the use of transformed non-Euclidean multivariate distances between curated dependent variable classes produced this research data. Quasi-experimental data from an enclosed laboratory environment utilizing a monitored, locally unrestricted network that introduced known Internet of Things (IoT) malware software supplied network flow events. RESULTS: A PERMANOVA model was executed against 62,000 records of the network flow observations, using Euclidean distance measurements with variable-dependent relationship ordering, using terms added sequentially (first to last) in the order encountered in the raw network flow dataset, using 200 permutations. This precursor test resulted in a p-value for the PERMANOVA model that incorporated terms added sequentially of 0.02985, providing an F value of 0.00017 with which to determine the ratio of explained to unexplained variance. Utilizing an analysis of the F values for all of the residuals, we show 29,998 degrees of freedom with a residual F model score of 0.99983, indicating that there is a strong proportion of explained to unexplained variance across all of the independent variables contained in the model. The model is thus statistically significant with a p-value below the alpha test statistic of 0.0…
subject: Research article
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Malware
  topic: keywords
subject: network flows
  topic: keywords
subject: multinomial
  topic: keywords
subject: PERMANOVA
  topic: keywords
subject: NPMANOVA
  topic: keywords
subject: multinomial classification
  topic: keywords
creator: Thomas Woolman
creator: John Pickard
rights: Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
date: 2023-07-12
publication date: 2023-07-12
doi: 10.4108/eetcasa.v9i1.2929