ISSN: 2409-0026
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2024
Issue 1
2024
Issue 1
2022
Issue 1
2020
Issue 23Issue 22Issue 21Issue 20
2019
Issue 19Issue 18Issue 17Issue 16
2018
Issue 15
2017
Issue 14Issue 13Issue 12Issue 11
2016
Issue 10Issue 9Issue 8Issue 7
2015
Issue 6Issue 5Issue 4Issue 3
2014
Issue 2Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/casa/index

Editor(s)-in-Chief: Phan Cong Vinh

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.

INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

 

The works that will be presented in the journal will focus on the following topics:

Context-awareness of Computation and Communication

Fundamentals:

• Context-aware models

• Context-aware control

• Context-aware algorithms

• Context-aware networks

• Context-aware computing

• Context-awareness calculi

• Context-awareness representation

• Context-awareness-based systems

• Logic in context-awareness

• Context-awareness reasoning

• Formal methods of context-awareness

• Context-awareness-based optimization and swarm Intelligence

• Theory, computational models and algorithms in context detection for computing

• Awareness of context in collaboration or crowdsourcing

• Context recognition and artificial intelligence (AI)

• Privacy issues

Context-aware Systems:

• Routing, transport, and reliability issues of context-aware systems

• Data dissemination and replication in context-aware systems

• Applications and middleware support, mobile social networking applications

• Mobility models and statistical analysis of mobility traces

• Context and social awareness mechanisms and algorithms

• Co-existence of opportunistic networks with infrastructure mobile wireless networks

• Service composition in autonomic and opportunistic networks

• Cognition-driven information processing and decision making

• Performance modeling, scaling laws, and fundamental limits for autonomic and opportunistic communications

• Game-theoretic insights into the operation of autonomic and opportunistic networks

• Participatory and urban sensing in autonomic and opportunistic networks

• Trust, security, and reputation in context-aware systems

• Autonomic and opportunistic communication testbeds and prototypes, measurement data from real experiments

• Socio-economic models for autonomic and opportunistic communications

• Mobile data measurement and collection platforms for context detection

• Automated systems to model and detect context

Context-aware Technologies and Applications

• Context-aware information retrieval

• Context-aware profiling, clustering, and collaborative filtering

• Machine learning for context-aware information retrieval and ontology learning

• Context-aware e-learning/tutoring

• Ubiquitous and context-aware computing

• Use of context-aware technologies in UI/HCI

• Context-aware advertising

• Recommendations for mobile users

• Context-awareness in portable devices

• Context-aware services

• Social Agents and Avatars

• Emotion and Personality

• Virtual Humans

• Autonomous Actors

• Awareness-based Animation

• Social and Conversational Agents

• Inter-Agent Communication

• Social Behavior

• Crowd Simulation

• Understanding Human Activity

• Memory and Long-term Interaction

• Context representations and signal characteristics that describe and identify the context

• Context detection algorithms/approaches using data collected with mobile devices, wearable devices, and pervasive sensors (e.g. RF sensors)

• User studies and evaluation techniques for context detection

• The novel use of context information in computing applications

• Integration of context into lifelogging applications.

• Applications of context information for the work-life balance, for a healthier life and behavior

• Applications of context information in situational or social awareness

• Application of context information in health care

• Applications of context information in industrial production

• Applications of context information in pervasive games

• Applications of context information in mobile multimedia devices

Nature of Computation and Communication

• autonomic computing/communicating

• Amorphous computing

• Artificial Intelligence

• Artificial immune computing

• Artificial Life

• Artificial neural computing

• Big data

• Biologically-inspired computing/communicating

• Cellular automata

• Cellular computing

• Collective intelligence in computing/communicating

• Collision-based computing

• Computation/communication based on chaos and dynamical systems

• Computation based on physical principles such as relativistic, optical, spatial, collision-based computing

• Cognitive computing

• Context-aware computing/communicating

• Data Mining

• DNA computing

• Evolutionary computing

• Fractal geometry

• Fuzzy computing

• Hypercomputation

• Image Processing

• Machine Learning

• Massive parallel computing

• Membrane computing

• Molecular computing

• Morphological computing

• Natural Language Processing

• Optical computing/communicating

• Physarum computing

• Quantum computing/communicating

• Relativistic computing

• Spatial computing

• Swarm intelligence in computing/communicating

• Wetware computing

  • Abdur Rakib (The University of Nottingham, UK, Malaysia Campus)
  • Adina Magda Florea (University Politehnica of Bucharest, Romania)
  • Ashad Kabir (Swinburne University of Technology, Australia)
  • Chien-Chih Yu (National ChengChi University, Taiwan)
  • David Sundaram (University of Auckland, NZ)
  • Emil Vassev (University of Limerick, Ireland)
  • Giacomo Cabri (Università di Modena e Reggio Emilia, Italy)
  • Giovanna Di Marzo Serugendo (University of Geneva, Switzerland)
  • Giuseppe De Pietro (ICAR-CNR, Italy)
  • Kurt Geihs (University of Kessel, Germany)
  • Mirko Viroli (University of Bologna, Italy)
  • Ondrej Krejcar (University of Hradec Kralove, Czech Republic)
  • Vangalur Alagar (Concordia University, Canada)
  • Muhammad Athar Javed Sethi (University of Engineering and Technology Peshawar Pakistan)
  • Yaser Jararweh (Jordan University of Science and Technology, Jordan)

  • Elevating User-Centered Design with AI: A Comprehensive Exploration using the AI-UCD Algorithm Framework

    Appears in:
    casa 24(1):

    Authors:
    Waralak Vongdoiwang Siricharoien

    Published:
    4th Dec 2024

    Abstract:
    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.

  • Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra

    Appears in:
    casa 24(1):

    Authors:
    Nguyen Van Han

    Published:
    27th Sep 2024

    Abstract:
    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

    Appears in:
    casa 24(1):

    Authors:
    Bhimambika Y Balannanavar, Vijayalaxmi N Rathod, Geeta Hukkeri, Anjanabhargavi Kulkarni, R.H Goudar, Dhananjaya G M, Anjali H Kugate

    Published:
    18th Jul 2024

    Abstract:
    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.

  • Integration and Recommendation System of Profiles based on Professional Social Networks

    Appears in:
    casa 24(1):

    Authors:
    Ulriche Mbouche Bomda, Paul Dayang

    Published:
    15th Jan 2024

    Abstract:
    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.

  • UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

    Appears in:
    casa 24(1):

    Authors:
    Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc, Tran Cao Minh, Vuong Xuan Chi, Ha Minh Tan

    Published:
    12nd Jan 2024

    Abstract:
    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."

  • UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

    Appears in:
    casa 24(1):

    Authors:
    Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc, Tran Cao Minh, Vuong Xuan Chi, Ha Minh Tan

    Published:
    12nd Jan 2024

    Abstract:
    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."

  • Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning

    Appears in:
    casa 23(1):

    Authors:
    Phan Cong Vinh, Nguyen Kim Quoc, Dang Nhu Phu

    Published:
    10th Oct 2023

    Abstract:
    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

    Appears in:
    casa 23(1):

    Authors:
    Lourve Vincent

    Published:
    2nd Oct 2023

    Abstract:
    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

    Appears in:
    casa 23(1):

    Authors:
    Nguyen Cong Nhut

    Published:
    25th Sep 2023

    Abstract:
    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

    Appears in:
    casa 23(1):

    Authors:
    Tuan Nguyen

    Published:
    25th Sep 2023

    Abstract:
    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.

  • A Novel Blockchain-Based Model for Blood Donation System

    Appears in:
    casa 22(1): e8

    Authors:
    M.H. Zafar, A.U. Rehman, I. Khan, S. Zafar

    Downloads:
    1414

    Abstract:
    In Pakistan, existing blood control systems or blood information management systems are limited in terms of efficient data retrieval of donor to consumer. There is no communication network in place for extra blood in one location to be demanded from a region if blood is limited, resulting in blood wastage. Due to a lack of accessibility and sufficient blood quality testing, blood contaminated with illnesses such as HIV has been used for transfusion in some cases. This study proposes a ledger blood management system to address these challenges. The trail has been represented as a supply-chain management problem following the blood. By trailing the blood stream and donation a single platform for transferring blood and the problem results among blood groups, the proposed system, built on the hyperledger fabric model, adds more traceability toward the blood transfusion process. It also helps to reduce unjustified blood wastage by providing an integrated system for transferring lifeblood and the thing extracts among lifeblood banks. A web app is also designed for accessing the network for simplicity of usage and security is enhanced by implementing block chain hyperfebric ledger system through Key Value System (KVS) system.

  • Automatic Data Clustering using Dynamic Crow Search Algorithm

    Appears in:
    casa 22(1): e5

    Authors:
    Jitender Kumar Chhabra, Rajesh Ranjan

    Downloads:
    681

    Abstract:
    This work proposes Automatic clustering using Dynamic Crow Search Algorithm, which updates its parameters dynamically. Crow Search is a recently proposed algorithm that imitates the working of crow. Clustering is an essential aspect of data analysis whose significance has increased manifold since the advancements of technology which has led to enormous data generation, which need to be analysed in real-time. Automatic clustering detects optimal cluster numbers and produces sustainable cluster centroids. ACDCSA uses Cluster Validity using Nearest Neighbour as an internal validity measure that acts as a fitness function to find the optimal cluster centres. The present work is compared with some well-known other meta-heuristic search algorithms like PSO, DE, WOA and GWO for the automatic clustering task over seven benchmark clustering datasets. Inter-cluster distance, intra-cluster distance and the optimal cluster number produced are used to assess the performance of ACDCSA.

  • Analyzing Healthcare Device Security through Fuzzy Rule-based Multi-criteria Model

    Appears in:
    casa 22(1): e4

    Authors:
    Neetu Yadav, Shafeeq Ahmad, Naseem Ahmad Khan

    Downloads:
    617

    Abstract:
    Managing risk as well as safeguarding electronic health records can be difficult for small medical practises. As a result of their vulnerability to various attacks, Internet of Health Things (IoHT)-based devices require appropriate security. In this paper, fuzzy TOPSIS is used to assess the security characteristics of IoHT-based devices in a medical setting. This technique utilizes a security evaluation of alternative solutions depending on security factors. The results of the presented security evaluation approach demonstrate that the most trustworthy as well as safe alternative among several of the alternative solutions is chosen for the IoHT model. This strategy could be used as a model for future IoHT structures or even other IoT-based domains. To the authors’ knowledge, it is an unique strategy to IoT security evaluation, as well as such MCDM method have not been utilised before for evaluation as well as decision - making process in IoHT security systems.

  • A Fuzzy TOPSIS Based Analysis to Prioritize Enabling Factors for Strategic Information Technology Management

    Appears in:
    casa 22(1): e3

    Authors:
    Shafeeq Ahmad, Naseem Ahmad Khan, Raziya Siddiqui

    Downloads:
    587

    Abstract:
    Strategic management of information technology (IT) requires the attention provided to internal and external organizational factors. This paper discusses different enabling factors that allow strategic management of IT, making advances not only for using the approaches independently as well as in using them in a corresponding and adaptive way. A questionnaire-based survey and in-depth discussions were performed with 40 primary stakeholders to assess the relevance of enabling factors. Using available resources-based analysis, enabling factors were defined in four different categories: organizational, business, technological, and operational assessment. Subsequently, these four enabling factors were prioritized using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision analysis method. Finally, technological assessments were given high priority on the basis of the findings to allow more successful strategic IT management.

  • Fuzzy Logic Control Design and Implementation with DC-DC Boost Converter

    Appears in:
    casa 22(1): e6

    Authors:
    Abdullah J. H. Al Gizi

    Downloads:
    564

    Abstract:
    Being an electrical switch, this converter transforms an uncontrolled input DC voltage into a regulated one to get a desired output voltage. The MOSFET works in the circuit boost-converter as an electronic switch that closes and opens several times. The current passing through the inductor determines the modes operation of the boost-converter circuit. We proposed the new fuzzy control circuit (maximum power point (MPP) circuit using Fuzzy Logic Control (FLC) algorithm) was designed after replacing the DC source with a photovoltaic (PV) array and the duty cycle (constant) with the FLC and keeping the circuit components same except for the Pulses Width Modulation (PWM) of frequency 3800 Hz. In the full circuit, they controlled the MPP of the PV array through a boost converter and FLC., the relationship between the power and voltage of the PV array was drawn to access the MPP at fixed constant solar irradiance and temperature. The value of the solar irradiance altered during the day from low (in the morning) to high (with a peak at the noon) before being reduced to very low at the sunset. The proves that the FLC algorithm works efficiently to make the power of the PV cell always at the maximum value (MPP). The stability of the PV cell voltage and its current change also proves that it operates according to the specifications of the P-V and I-V characteristics of the PV cell referred to earlier the output voltage was increased because we used a step-up converter (boost converter with FLC). The achievement system is showed to be efficient and robust in improving solar charging and rectifying capacity.

  • An Algorithmic Approach to Adapting Edge-based Devices for Autonomous Robotic Navigation

    Appears in:
    casa 21(1): e2

    Authors:
    Mbadiwe S. Benyeogor, Kenneth A. Akpado, Eric J. Gratton, Oladayo O. Olakanmi, Piyal Saha, Kosisochukwu P. Nnoli, Olusegun I. Lawal, Sushant Kumar

    Downloads:
    563

    Abstract:
    A recent significant progress has been made in development of intelligent mobile robots that is capable of autonomous navigation using an edge-computing system. This could sense changes in its environment to control its mechanical behavior towards accomplishing preprogrammed motions. Several algorithms were used in developing the robot’s control software. These include the moving average filter, the extended Kalman filter, and the covariance algorithm. Using these algorithms, the robot could learn from its sensors to estimate and control its position, velocity, and the proximity of obstacles along its path, while autonomously navigating to a predetermined location on the earth’s surface. Results show that our algorithmic approach to developing software systems for autonomous robots using edge-computing devices is viable, cost-efficient, and robust. Hence, our work is a proof of concept for the further development of edge-based intelligence and autonomous robots.

  • Predicting Breast Cancer with Ensemble Methods on Cloud

    Appears in:
    casa 23(1): e1

    Authors:
    Tu Tran, Phuc Tran, Hiep Huynh, Au Pham

    Downloads:
    527

    Abstract:
    There are many dangerous diseases and high mortality rates for women (including breast cancer). If the disease is detected early, correctly diagnosed and treated at the right time, the likelihood of illness and death is reduced. Previous disease prediction models have mainly focused on methods for building individual models. However, these predictive models do not yet have high accuracy and high generalization performance. In this paper, we focus on combining these individual models together to create a combined model, which is more generalizable than the individual models. Three ensemble techniques used in the experiment are: Bagging; Boosting and Stacking (Stacking include three models: Gradient Boost, Random Forest, Logistic Regression) to deploy and apply to breast cancer prediction problem. The experimental results show the combined model with the ensemble methods based on the Breast Cancer Wisconsin dataset; this combined model has a higher predictive performance than the commonly used individual prediction models.

  • Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification

    Appears in:
    casa 23(1):

    Authors:
    Dang Nhu Phu, Phan Cong Vinh, Ha Minh Tan, Nguyen Kim Quoc, Vuong Xuan Chi

    Downloads:
    470

    Abstract:
    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.

  • Opinion Mining with Density Forests

    Appears in:
    casa 23(1):

    Authors:
    Dung Ngoc Le Ha, Phuc Quang Tran, Hanh Thi My Le, Hiep Xuan Huynh

    Downloads:
    435

    Abstract:
    In this paper, we propose a new approach for opinion mining with density-based forests. We apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify clusters of data points in a space of feature vectors that are important features of hotel and restaurant reviews, and then use the clusters to construct random forests to classify whether the opinions expressed about features in the reviews are positive or negative. Our experiment uses two standard datasets of hotel and restaurant reviews in two different scenarios. The experimental results show the effectiveness of our proposed

  • Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition

    Appears in:
    casa 22(1): e7

    Authors:
    J.L. Pickard, T.A. Woolman

    Downloads:
    433

    Abstract:
    INTRODUCTION: A solution to subject-independent HAR prediction through machine learning classification algorithms using statistical equivalency for comparative analysis between independent groups with non-subject training dependencies. OBJECTIVES: To indicate that the multinomial predictive classification model that was trained and optimized on the one-subject control group is at least partially extensible to multiple independent experiment groups for at least one activity class. METHODS: Gradient boosted machine multinomial classification algorithm is trained on a single individual with the classifier trained on all activity classes as a multinomial classification problem. RESULTS: Levene-Wellek-Welch (LWW) Statistic calculated as 0.021, with a Critical Value for LWW of 0.026, using an alpha of 0.05. CONCLUSION: Confirmed falsifiability that incorporates reproducible methods into the quasi-experiment design applied to the field of machine learning for human activity recognition.
Publisher
EAI
ISSN
2409-0026
Number of Volumes
10
Last Published
2024-01-12
EAI Logo

About EAI

Community

Publish with EAI