EAI Endorsed Transactions on Context-aware Systems and Applications
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
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Temporal assessment of cognitive load factors using ocular features during a visual search
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.
Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
Reflecting Society Through Art: The Aesthetic Evolution of Paintings
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.
Tracing the Evolution of Max-Min Aggregation and Fuzzy Systems in AI: A Bibliometric Review
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.
Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications
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.
Explainable Artifical Intelligence (XAI)
Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review
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
A Review of Quantum Lambda Calculi: Linearity, Semantics, and Programming Models
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.
Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning
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.
Copyright (c) 2024 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by/3.0/
A Survey of Quantum Type Theory: From Linearity to Formal Verification
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.
Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings
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.
Analyzing online reviews at the word level to understand customer experience
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.
A hybrid classification model in improving the classification quality of network intrusion detection systems
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.
Augmented Reality and Virtual Reality Crafting New Dimensions of Human Perception, Interaction, and Societal Evolution
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.
Augmented Reality, Virtual Reality, Synthetic Worlds, Reality Distortion, Hybrid Dimensions
Human Emotion Recognition with an Advanced Vision Transformer Model
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.
Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra
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.
Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques
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.
Elevating User-Centered Design with AI: A Comprehensive Exploration using the AI-UCD Algorithm Framework
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, ethical audits, iterative improvements, and compliance with industry standards. Examples of how these steps work in practice are provided.
Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis
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."
Integration and Recommendation System of Profiles based on Professional Social Networks
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.
Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications https://creativecommons.org/licenses/by/4.0/
Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning
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.
Manipulation of the Multi-Vehicle System for the Industrial Applications
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.
Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam
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.
Study of Robot Manipulator Control via Remote Method
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.
Facial Sentiment Recognition using artificial intelligence techniques.
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.
The data preprocessing in improving the classification quality of network intrusion detection systems
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%.
Breast Tumor Classification using Machine Learning
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.
Human Centered Design and Design Thinking for Entrepreneurship
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.
Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification
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.
Control issues, artificial neural network (ANN) for acrobot system
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.
Creative Brain Training Apps and Games Can Help Improve Memory, Cognitive Abilities, and Promote Good Mental Health for The Elderly
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.
A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data
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 0.05. CONCLUSION: This research has demonstrated that it is possible to apply tests of falsifiability that incorporate reproducible methods into the quasi-experiment design and apply this to the field of machine learning. Applied to AI/ML (artificial intelligence/machine learning) models, this pre-assessment methodology supports the appropriateness of cyber network flow datasets in which a final test of statistical significance would be required. The authors believe that this represents a substantially useful precursor assessment stage for the suitability and reliability of the utilization of any nonparametric statistical learning algorithms applied to cyber network data predictive analytics.