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Keyword: KNNIndoor Positioning System using WKNN and LSTM Combined via Ensemble Learning
Indoor positioning system (IPS) has become a high demand research field to be developed and has made considerable progress in recent years. Wi-Fi fingerprinting is the most promising technique that produces an acceptable result. However, despite the large amount of research that has been done using Wi-Fi fingerprinting, only a few Wi-Fi based IPS in…
Read MoreEKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection
Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data. Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection. The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency. This work is an extension of…
Read MorePredictive Analytics in Marketing: Evaluating its Effectiveness in Driving Customer Engagement
Understanding and responding to customer feedback is critical for business success. Customer response data offers valuable insights into preferences, behaviours, and sentiment. By analysing this data, businesses can optimize strategies, enhance customer experiences, and drive growth. Many analysis have been conducted in this field, while the review covers a broad range of AI and ML…
Read MoreAccuracy Improvement-Based Wireless Sensor Estimation Technique with Machine Learning Algorithms for Volume Estimation on the Sealed Box
Currently, the quality and quantity of product must be inspected before transporting. Currently the popular unsealing box product inspecting is performed by weighing the box where the errors occur according to the tolerance of the weighting machine and tolerance weight of the product. On the other hand, the quantity of product can be inspected automatically…
Read MoreCyberbullying Detection by Including Emotion Model using Stacking Ensemble Method
Cyberbullying is a serious problem and caused an immense impact to the victim. To prevent the cyberbullying, the solution is to develop an automatic detection system. In this research, we propose a combined model for cyberbullying detection and emotion detection by using stacking method. The experiment is to create a better model for cyberbullying detection…
Read MoreElectroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli
A methodology of medical signal-based biometrics has been proposed in this paper for implementing a human identification system controlled by electroencephalogram in respect of different color stimuli. The advantage of biosignal based biometrics is that they provide more efficient operation in simple experimental condition to ensure accurate identification. Red, Green, Blue (primary colors) and Yellow…
Read MoreDevelopment of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach
Brain-computer interface (BCI) has extensively been used for rehabilitation purposes. Being in the research phase, the brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity in neural behavior in different conditions, and lower accuracy. Being sensitive to the color stimuli, the EEG signal changes promises a better…
Read MoreImproved Detection of Advanced Persistent Threats Using an Anomaly Detection Ensemble Approach
Rated a high-risk cyber-attack type, Advanced Persistent Threat (APT) has become a cause for concern to cyber security experts. Detecting the presence of APT in order to mitigate this attack has been a major challenge as successful attacks to large organizations still abound. Our approach combines static rule anomaly detection through pattern recognition and machine…
Read MoreDiagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning
Addiction such as tobacco smoking affects the human brain and thus causes significant changes in the brainwaves. The changes in brain wave due to smoking can be identified by focusing on changes in electroencephalogram pattern, extracting different time-frequency domain features. In this aspect, a laboratory-based study has been presented in this paper, for assessing the…
Read MoreClassification of Wing Chun Basic Hand Movement using Virtual Reality for Wing Chun Training Simulation System
To create a Virtual Reality (VR) system for Wing Chun’s basic hand movement training, capturing, and classifying movement data is an important step. The main goal of this paper is to find the best possible method of classifying hand movement, particularly Wing Chun’s basic hand movements, to be used in the VR training system. This…
Read MoreAn Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware
Android Operating system (OS) has been used much more than all other mobile phone’s OS turning android OS to a major point of attack. Android Application installation serves as a major avenue through which attacks can be perpetrated. Permissions must be first granted by the users seeking to install these third-party applications. Some permissions can…
Read MoreHuman Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor
Human emotional states can transform naturally and are recognizable through facial expressions, voices, or body movements, influenced by received stimuli. However, the articulation of emotions is not practicable by every individual, even when feelings of joy, sadness, or otherwise are experienced. Biomedically, emotions affect brain wave activities, as the continuously functioning brain cells communicate through…
Read MoreDense SIFT–Flow based Architecture for Recognizing Hand Gestures
Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures difficult in any vision-based method. Diversity in finger shape, size, distribution and motion dynamics is also a big constraint. This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based…
Read MoreCustomer Satisfaction Recognition Based on Facial Expression and Machine Learning Techniques
Nowadays, Customer satisfaction is important for businesses and organizations. Manual methods exist, namely surveys and distributing questionnaires to customers. However, marketers and businesses are looking for quick ways to get effective and efficient feedback results for their potential customers. In this paper, we propose a new method for facial emotion detection to recognize customer’s satisfaction…
Read MoreMachine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database
With the current amount of data nowadays, the need for processing power has vastly grown. By relying on CPU processing power, current processing power is depending on the frequency and parallelism of the current CPU device. This means this method will lead to increased power consumption. Current research has shown that by utilize the power…
Read MoreReview on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring
With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in…
Read MoreSkin Tissue Oxygen Saturation Prediction: A Comparison Study of Artificial Intelligence Techniques
Noninvasive measurement of skin tissue oxygen saturation, StO2, is of interest especially in the studies of wound healing and detection of vascular diseases. This work aims to compare Partial Least Square (PLS) regression, K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) technique in the prediction of StO2 using spectral data obtained from Monte Carlo simulations.…
Read MoreClassification Model of Contact Center Customers Emails Using Machine Learning
E-mail is one of the media services used at the contact center. The challenge faced by e-mail services is how to handle e-mails that enter large quantities every day efficiently to provide fast and appropriate service to customers. The purpose of this study is to find which method has the best accuracy in classifying emails…
Read MorePredictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education
Currently, one of the challenges of educational institutions is drop-out student issues. Several factors have been found and determined potentially capable to stimulate dropouts. Many researchers have been applied data mining methods to analyze, predict dropout students and also optimize finding dropout variables in advance. The main objective of this study is to find the…
Read MoreFeature Selection for Musical Genre Classification Using a Genetic Algorithm
Music genre classification is an important multimedia research domain, including aspects of music piece representation, distances between genres, and categorization of music databases. The objective of this study was to develop a model for automatic classification of musical genres from audio data by using features from low-level time and frequency domains. These features can highlight…
Read MoreHuman Sit Down Position Detection Using Data Classification and Dimensionality Reduction
The analysis of human sit down position is a research area allows for preventing health physical problems in the back. Many works have proposed systems that detect the sitting position, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an…
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