Gabriel Avelino Sampedro

• De La Salle University

Epilepsy, often associated with neurodegenerative disorders following brain strokes, manifests as abnormal electrical activity bursts in the cerebral cortex, disrupting regular brain function. Electroencephalogram (EEG) recordings capture these distinctive brain signals, offering crucial insights into seizure detection and management. This study pr...

Electric Vehicle Charging Station (EVCS) security is a growing concern in today’s connected world due to the growing complexity and frequency of cyber threats. Traditional Intrusion Detection Systems (IDS) for EV chargers struggle to detect novel or unexpected attacks due to their usage of predetermined signatures and limited detection capabilities...

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature...

Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. The application of machine learning, and block-chain techniques into medical image retrieval, classification and auxiliary diagnosis has become one of the research hotspots at present. Brain tumo...

Skin cancer is one of the most common, deadly, and widespread cancers worldwide. Early detection of skin cancer can lead to reduced death rates. A dermatologist or primary care physician can use a dermatoscope to inspect a patient to diagnose skin disorders visually. Early detection of skin cancer is essential, and in order to confirm the diagnosis...

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis (MS), a chronic autoimmune neurological condition. It disrupts signals between the brain and body, causing symptoms including tiredness, muscle weakness, and difficulty with memory and balance. Traditional methods fo...

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Con...

In recent years, driven by the continuous development of mobile Internet technology and artificial intelligence technology, the improvement of the manufacturing level of 6G Internet-of-Everything (IoE) products and the increase in residents’ income level, the 6G IoE industry has shown a sustained and stable development trend. However, 6G IoE has gr...

Suspicious Human activity recognition (SHAR) is crucial for improving surveillance and security systems by recognizing and reducing possible hazards in different situations. This study focuses on the task of precisely identifying potentially suspicious human behaviour by utilizing an innovative approach that harnesses advanced deep learning methods...

This paper investigates virtual reality (VR) technology which can increase the quality of experience (QoE) on the graphics quality within the gaming environment. The graphics quality affects the VR environment and user experience. To gather relevant data, we conduct a live user experience and compare games with high- and low-quality graphics. The q...

Adversarial malware poses novel threats to smart devices since they grow progressively integrated into daily life, highlighting their potential weaknesses and importance. Several Machine Learning (ML) based methods, such as Intrusion Detection Systems (IDSs), Malware Detection Systems (MDSs), and Device Identification Systems (DISs), have been used...

As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research suggests using deep learning methods for precise and ra...

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering...

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detectio...

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic resea...

Based on the development needs of Industry 5.0, Advanced On-orbit Systems (AOS) can be integrated with terrestrial 5G networks, with large-scale IoT links as well as with flexible deployment and resource optimization, enabling efficient transmission of multiple types of industrial data, human-machine collaboration, and improving the flexibility, in...

Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities...

The evolution of smartphones made people more dependent on them since they save their data on smartphones for convenience. This convenience aroused threats to the stored data through cyber attacks. Smartphones consist of various hardware sensors, including a magnetometer, an accelerometer, and a gyroscope, which can be vulnerable to cyberattacks su...

The robust development of the blockchain distributed ledger, the Internet of Things (IoT), and fog computing-enabled connected devices and nodes has changed our lifestyle nowadays. Due to this, the increased rate of device sales and utilization increases the demand for edge computing technology with collaborative procedures. However, there is a wel...

The robust development of the blockchain distributed ledger, the Internet of Things (IoT), and fog computing-enabled connected devices and nodes has changed our lifestyle nowadays. Due to this, the increased rate of device sales and utilization increases the demand for edge computing technology with collaborative procedures. However, there is a wel...

The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitatio...

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consu...

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning...

The imminent arrival of 6th Generation (6G) consumer electronics wireless networks heralds a paradigm shift in communication necessitated by diverse quality-of-service (QoS) demands. Meeting these demands efficiently requires novel strategies, particularly within multi-tier cellular networks encompassing femto cells, pico cells, macro cells, and de...

Background Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or ext...

The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attac...

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). An effective remedy is the incorporation of active learning strategies. This study explores this junction, intending t...

This research explores how technology can be used to understand and identify activities among elderly individuals. By utilizing HAR70+ data and applying methods like Active Learning (AL), Machine Learning (ML), and Deep Learning (DL), this research aims to predict various activities performed by older adults. Moreover, the study leverages the HAR70...

This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels. This study utilizes active learning strategies to increase the effectiveness of machine learning models in predicting anxiety levels amo...

This paper introduces PureFed, an innovative Federated Learning (FL) framework designed for efficiency, collaboration, and trustworthiness. In the background of FL research, it was observed that previous frameworks often neglected participant privacy, a critical aspect not aligned with the core FL concept. Additionally, there was room for improving...

The increasing occurrence of Hypertension highlights the need for advanced predictive tools in healthcare. This research proposes a novel approach that combines machine and deep learning for new feature generation and hypertension prediction. We explore machine learning-based models: Random Forest (RF), Logistic Regression (LR), Decision Tree (DT),...

Artificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a Log Anomaly Detection (LAD). To the rescue, Explainable Artificial Intelligence (XAI) improves system log analysis. It follows a white-box model for transparen...

In a smart home environment, multiple users can access a single smart device simultaneously. Moreover, these multiple users may have conflicting demands at a time; that is, one user’s demands differ from another for the same device based on the role of users and environmental factors. Therefore, existing single-user access control systems cannot ha...

The widespread use of smartphones has brought convenience and connectivity to the fingertips of the masses. As a result, this has paved the way for potential security vulnerabilities concerning sensitive data, particularly by exploiting side-channel attacks. When typing on a smartphone’s keyboard, its vibrations can be misused to discern the entere...

Adversarial attacks involve manipulating data to trick Artificial Intelligence (AI) models, making false predictions or classifications or even disrupting the normal functions of the smart grid. This can be done by providing the wrong information to the models, hence producing wrong predictions and therefore leading to instabilities, power imbalanc...

Darknet refers to a significant portion of the internet that is hidden and not indexed by traditional search engines. It is often associated with illicit activities such as the trafficking of illicit goods, such as drugs, weapons, and stolen data. To keep our online cyber spaces safe in this era of rapid technological advancement and global connect...

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample siz...

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection. Most c...

Cardiac disease is a chronic condition that impairs the heart’s functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning...

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial In...

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM met...

Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and...

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health...

High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods...

The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developme...

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track...

Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3...

The technology advancement is supported by additive manufacturing industries, especially 3D printing companies, that enable fast object prototyping and development in Industry 4.0. As 3D printed products are highly adopted in various fields, the final printed product must fulfill precise requirements without any defects. Therefore, an efficient fra...

In the digital age, the digital twin eliminates physical barriers and risks, facilitating seamless activities in both real and virtual worlds. In the context of additive manufacturing, testing 3D printers can be resource-intensive and prone to printing issues. This research introduces a digital twin-based system that employs the innovative ensemble...

This research article presents an enhanced YOLOv8 model with an additional feature extraction layer integrated into the traditional YOLOv8 architecture to improve fault detection performance in smart additive manufacturing, specifically for FDM 3D printers. Hyperparameter optimization techniques are employed to ensure the model is trained with opti...

Over time, the amount of textual data has increased drastically, especially due to the publication of articles. As a consequence, there has been a rise in anonymous content. Research is being conducted to determine alternative methods for identifying unknown text authors. To this end, a system has to be developed to accurately determine the author...

The advancement of Industry 4.0 has necessitated the development of reliable predictive maintenance systems in production and manufacturing processes. This need is particularly pronounced in facilities that employ multiple 3D printers concurrently. To address this, the present study proposes the establishment of an Industrial Internet of Things (II...

Drug-drug interaction (DDI) is a significant public health issue that accounts for 30% of unanticipated clinically hazardous medication events. The past decade has seen an evolution in informatics-based research for DDI signal identification. This paper aims to create an ensemble stacking machine learning (ML) approach capable of accurately predict...

The advancement of the communications system has resulted in the rise of the Internet of Things (IoT), which has increased the importance of cybersecurity research. IoT, which incorporates a range of gadgets into networks to offer complex and intelligent services, must maintain user privacy and deal with attacks such as spoofing, denial of service...

The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) feat...

Mobile Edge Computing (MEC) enhances social media customer reviews by providing real-time processing and analysis at the network edge. Social media platforms have revolutionized how users communicate their thoughts, ideas, and opinions on various issues, yielding a wealth of valuable data that can be used to get insights into people’s attitudes tow...

Product reviews are critical in informing customers about products and services and are crucial to customer inclinations and business success. Active learning enables Artificial Intelligence (AI) algorithms to learn more efficiently by actively selecting the most informative data samples for training. This study proposes a state-of-the-art approach...

The increasing demand for mobile phones has resulted in abundant online reviews, making it challenging for consumers to make informed purchasing decisions.In this study, we propose Graph Neural Network (GNN) models to classify mobile phone ratings using Term Frequency-Inverse Document Frequency (TF-IDF) features. We collected a dataset of over 13,0...

Energy providers and the power grid are severely harmed by electricity theft, which also causes economic and non-technical losses. Energy theft causes a decline in power quality and overall profitability. Smart grids may address the problem of power theft by merging data and energy flow. The analysis of smart grid data helps to find power theft. Th...

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explor...

Music influences our mood. Individuals have experienced music personally where their emotions become involved, allowing the tempo or lyrics of the music to impact them. Music not only provides entertainment but also helps boost overall wellbeing. Over the years, music streaming platforms have become popular for the way music is delivered and music...