EAI Endorsed Transactions on Context-aware Systems and Applications

Control issues, artificial neural network (ANN) for acrobot system

2023-08-03T08:57:37+00:00

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.

Human Centered Design and Design Thinking for Entrepreneurship

2023-08-14T10:08:21+00:00

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.

A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data

2023-07-12T10:05:46+00:00

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.

Opinion Mining with Density Forests

2023-07-10T07:42:24+00:00

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

Creative Brain Training Apps and Games Can Help Improve Memory, Cognitive Abilities, and Promote Good Mental Health for The Elderly

2023-07-28T11:29:22+00:00

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.

Breast Tumor Classification using Machine Learning

2023-08-15T14:13:07+00:00

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.

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

2023-08-14T10:08:10+00:00

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.

The data preprocessing in improving the classification quality of network intrusion detection systems

2023-09-06T13:07:48+00:00

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%.

Study of Robot Manipulator Control via Remote Method

2023-09-25T13:20:43+00:00

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.

2023-09-22T13:14:00+00:00

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.

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

2023-09-25T13:46:27+00:00

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.

Manipulation of the Multi-Vehicle System for the Industrial Applications

2023-10-02T14:01:09+00:00

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.

Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning

2023-10-10T08:56:46+00:00

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.

Predicting Breast Cancer with Ensemble Methods on Cloud

2023-03-29T12:30:06+00:00

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.

IoT based Human Activity Recognition using Deep learning

2022-09-19T23:15:58+00:00

Artificial intelligence and the Internet of things (IoT) are the fastest and latest growing technologies that can handle a huge amount of data in computing services. This paper presents a smart human activity recognition system based on IoT that can be used for surveillance purposes working as IoT-based armour. Pose estimation model viz. MoveNet has been employed to extract the anatomical key points from RGB video frames. Different subjects from different camera angles were employed to make the approach person-independent. Diverse Machine learning models such as Decision tree, support vector machines, XGboost, and random forest classifiers were employed using extracted keypoints for training the model for estimating human activity during posture estimation monitoring. SMS are sent to the designated person with the raising of buzzer alarm in case of anomalous behaviour detection.

Linear Quadratic Gaussian with noise signals for lateral and longitudinal of F-16

2023-04-20T14:09:49+00:00

Today, classical control methods are still widely used because of their excellent performance in a working enviroment with noise signals. Besides, they are suitble for functiions of the system : operations to control a machine are more flexible, easy to perform, less unwanted risks occur, the efficiency of controlling a system better. In the early years of the 21^st century, traditional algorithms still promote their effects. Besides the traditional control methods, the author has applied more moderm and smarter algorithms such as adjusting Linear Quadratic Gaussian (LQG) to control a system on the ground or a system moving in the air. In the paper, LQG regulator is applied to a flight model to demonstrate its effectiveness in all cases. LQG regulator has not been applied before for this model. Results are as expected by the author for the working enviroment with noise signals affecting the system. Kalman filter used in this paper has shown its usefulness in the problem of dealing with unwanted signals. Simulation is done by Matlab.

Deep Biased Matrix Factorization for Student Performance Prediction

2023-04-20T14:32:25+00:00

In universities that use the academic credit system, selecting elective courses is a crucial task that can have a significant impact on a student's academic performance. Students who perform poorly in their courses may receive formal warnings or even face expulsion from the university. Thus, a well-designed study plan from a course recommendation system can play an essential role in achieving good academic performance. Additionally, early warnings regarding challenging courses can help students better prepare and improve their chances of success. Therefore, predicting student performance is a vital component of both the course recommendation system and the academic advisor's role. To this end, numerous studies have addressed the prediction of student performance using various approaches such as association rules, machine learning, and recommender systems. More recently, personalized machine learning approaches, particularly the matrix factorization technique, have been used in the course recommendation system. However, the accuracy of these approaches in predicting student performance still needs improvement. To address this issue, this study proposes an approach called Deep Biased Matrix Factorization, which carries out deep factorization via multi-layer to enhance prediction accuracy. Experimental results on an educational dataset have demonstrated that the proposed approach can significantly improve the accuracy of student performance prediction. By using this approach, universities can better recommend elective courses to their students as well as predict student performance, which can help them make informed decisions and achieve better academic outcomes.

Polarity assignment method (PAM), ANN, Neural networks strategy for the data of PAM for the single degree of freedom flexible joint robot

2023-04-20T14:46:02+00:00

This paper “describes” the investigation of the stability of a single Degree of Freedom (DOF) flexible robotic arm by the diagrams shown below. The derived model is based on Euler- Lagrange approach. Exploration of a flexible robotic arm with using state-of-the-art controllers is essential for intelligent applications. These robot arms have joints that work independently of each other in order to create a smooth connection between joints. They still ensures the natural properties like a real human arm. The use of polarity assignment method “helps” the system to achieve desired output signals which has not been thoroughtly studied before for this system. The author can also compare the effectiveness of control methods for this system to find the most effective method for control strategies. In particular, ANN ( artificial neural network) is the most modern technique currently applied to this system to investigate the security and stability of the system through this program. This is new and it has never been used before for a system of this type. Neural networks strategy has been implemented in this paper as an application of artificial intelligence. It has successfully performed a mission in re-simulating functions of another control method: Polarity assignment method. Simulation results are done by Matlab.