International Journal on Robotics, Automation and Sciences

Characterization and Evaluation of Mechanical Properties of Carbon Nanotube Filler Epoxy Composite

Chockalingam Palanisamy, Logah Perumal, Ganeshkumar Krishnan — Tue, 30 Apr 2024 00:00:00 +0800

The outstanding mechanical properties of carbon nanotubes (CNT) have made them the focus of extensive investigation. To characterize and study the effects of the different volume fraction of multi walled carbon nanotubes (MWCNTs) on the mechanical properties of the nanocomposites attempted. The purpose of this work is to use experimental techniques to ascertain the mechanical characteristics of the nanocomposite. Tests on mechanical tensile strength were conducted to see how the MWCNT filler content affected the reinforced epoxy nanocomposite. The result of altered percentage of MWCNTs on the mechanical properties of the composites had been inspected. Results showed that 0.2 wt% MWCNT addition has the best effect on the mechanical properties of the matrix.

[Manuscript received: 21 June 2023 | Accepted: 11 December 2023 | Published: 30 April 2024]

Development of Automated Attendance System Using Pretrained Deep Learning Models

Muhammad Shahrul Zaim Ahmad, Nor Azlina Ab. Aziz, Anith Khairunnisa Ghazali — Tue, 30 Apr 2024 00:00:00 +0800

Abstract - Smart classroom enables better learning experience to the students and aid towards efficient campus' management. Many studies have shown positive correlation between attendance and student's performance, where the higher the attendance, the better the student's performance. Therefore, many higher learning institutions make class attendance compulsory and students' attendance are recorded. Technological solutions for an advanced attendance system such as face recognition is highly desirable. The authenticity of attendance can be ensured by using such solution. In this work, artificial intelligence based face recognition system is used for attendance recording system. The recognized face is used to confirm the presence of a student to the class. Six pretrained face recognition model are evaluated for the adoption in the system developed. The FaceNet, is adopted in this work with accuracy of more than 95%. The automation system is supported by IoT.

[Manuscript received: 1 July 2023 | Accepted: 12 December 2023 | Published: 30 April 2024]

Vision-based Egg Grading System using Support Vector Machine

Way Soong Lim, Kang Lai Desmond Ji, Sin Ting Lim, Boon Chin Yeo — Tue, 30 Apr 2024 00:00:00 +0800

Being known as a nutrient-dense food, eggs are high in demand in the marketplace and high-quality eggs are much sought-after. Hence, egg grading is in place to sort eggs into different grades. Experienced graders are required for their knowledge to classify egg grades and as humans are involved, errors when performing manual grading are unavoidable. This study aims to develop a vision-based egg classification system that requires minimal human intervention. The proposed system houses a camera to acquire real-time images of the eggs and these images are served as the input to the algorithm. Based on the 6 geometrical features derived from the geometric parameters of the egg image, the eggs are classified using Support Vector Machine (SVM). The experiment results show the proposed egg grading system with a linear kernel SVM model can yield as high as 92.59% training accuracy.

[Manuscript received: 1 July 2023 | Accepted: 10 October 2023 | Published: 30 April 2024]

The Enhanced Speech Recognition in Automated Home Lighting System using Adaptive Time-Frequency Domain Noise Removal Algorithm Filter

Sk Abu Baker Siddik, Wan Nor Al-Ashekin Wan Husin , Thagirarani Muniandy — Tue, 30 Apr 2024 00:00:00 +0800

Numerous studies have explored speech recognition performance in Smart Home environments. However speech recognition accuracy diminishes when voice commands are captured in noisy areas of the home. This study aims to enhance speech recognition performance in such noisy environments. Instead of relying on remote control signals, a Bluetooth system is employed for short-range wireless communication to identify speech commands. Various sound levels are measured in decibels (dB) at different distances using the Smart Noise Application. A filter algorithm with Adaptive Filtering is used to minimize unwanted noise. The algorithm uses Adaptive Time-Frequency Domain Noise Removal (TFDNR) to mitigate background noise. Overall, the integrated system comprising Smartphone, Bluetooth, Arduino microcontroller, and noise detection software exhibits improved performance compared to previous studies, highlighting its potential for seamless smart home automation.

[Manuscript received: 31 July 2023 | Accepted: 26 March 2024 | Published: : 30 April 2024]

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Thirumalaimuthu Thirumalaiappan Ramanathan, Md. Jakir Hossen, Md. Shohel Sayeed — Tue, 30 Apr 2024 00:00:00 +0800

Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

[Manuscript received: 23 August 2023 | Accepted: 12 December 2023 | Published: 30 April 2024]

Design and Development of Automated Solar Grass Trimmer with Charge Control Circuit

Tue, 30 Apr 2024 00:00:00 +0800

The focus of this paper is the design and development of an automatic solar grass trimmer prototype, emphasizing high operational efficiency with renewable energy as the primary power source. This solar-based device not only contributes to reduced air pollution but also aligns with environmental sustainability objectives. The prototype features a charge control circuit and is powered by a rechargeable 12V battery. To address low battery levels, a solar panel is employed for automatic recharging. A DC-DC buck converter is integrated to step down the higher voltage from the solar panel to match the battery requirements. Safety features, such as a current limiter and overcharge protection circuits, have been incorporated into the design. The paper provides a detailed discussion on the conceptual design of the solar grass trimmer, including the placement of sensors and motors. Additionally, the cutting motor force analysis and total weight calculation of the prototype are presented.The solar charge control circuit, along with details on the current limiter and overcharge protection circuit, is thoroughly explored. Successful development of the prototype is reported, and the battery charging circuits are analyzed using a Bench PSU and solar panel. The paper includes plots of voltage, current, and power generated by the solar panel under various weather conditions. A comparison between Bench PSU and solar panel power is also provided. This research contributes to the advancement of automated solar-powered devices, offering sustainable solutions for environmentally friendly grass trimming.

[Manuscript received: 11 October 2023 | Accepted: 29 January 2024 | Published: 30 April 2024]

Design and Development of an Arduino Based Automated Solar Grass Trimmer

Tue, 30 Apr 2024 00:00:00 +0800

This paper focuses on the design of an Arduino-based Automated Solar Grass Trimmer with a primary emphasis on achieving high operational efficiency. A solar panel is utilized to automatically charge the battery when its level is low. A voltage level indicator circuit is incorporated to assess various battery voltage levels. The prototype integrates ultrasonic sensors for obstacle detection and inductive sensors for boundary detection. To ensure safe operation in the field, a perimeter signal generator circuit is constructed for boundary detection using inductive sensors. Ultrasonic sensors are employed for obstacle detection. The paper introduces an algorithm for detecting obstacles, boundaries, and other impediments in the path of the solar grass trimmer, illustrated through a comprehensive flowchart. Detailed discussions on the voltage level indicator circuit and boundary detection circuits are provided. The implemented algorithm, utilizing an Arduino microcontroller, is tested in the field, and the results are explained in the paper. Tabulated data from the prototype testing, specifically focusing on boundary detection and obstacle detection, demonstrate satisfactory performance.

[Manuscript received: 17 October 2023 | Accepted: 29 January 2024 | Published: : 30 April 2024]

Validity and Reliability of a Conceptual Framework on Enhancing Learning for Students via Kinect: A Pilot Test

Marianne Too, Siong Hoe Lau, Choo Kim Tan — Tue, 30 Apr 2024 00:00:00 +0800

Traditional method of teaching poses two significant problems – not all students learn alike, and the physical interaction needed poses health risk during pandemic. As such, for these students, an alternative learning method such as those that uses natural user interface (NUI) can be considered. This method would be beneficial for kinesthetic type learners and can be conducted remotely. The alternative learning program is a complementary method, thus still incorporates the current subject syllabus. However, the delivery, learning and execution of the syllabus will be varied. In minimizing these gaps found in the current Malaysian education system, a conceptual framework utilizing Microsoft Kinect is proposed. Since this is a new framework, a pilot study is needed to gauge the validity and reliability of the survey instrument prior to embarking on further study on the outcome of the alternative learning program. Face and content validity conducted on the questionnaire were found to be clear, not confusing, and measures what the questions were supposed to measure. Reliability measured using Cronbach’s Alpha indicated values above the acceptable range. Thus, these results indicate that the instrument is valid and reliable to be applied for data collection in the future study to assess the intention of Malaysian students to adopt an alternative medium for learning.

[Manuscript received: 22 October 2023 | Accepted: 13 March 2024 | Published: : 30 April 2024]

Performance Improvement Scheme of NIDS Through Optimized Intrusion Pattern Database

Nouman Amjad, Anam Mumtaz, Sara Abbas, Umar Hayat — Tue, 30 Apr 2024 00:00:00 +0800

Network-based intrusion detection systems (NIDS) are perceptively distributed devices within computer networks. They aim to examine traffic passing through the network on which they are installed passively. The database is the most vital part of network intrusion detection systems, as all the data converted information from the NIDS needs to be saved in a patterned structured manner. Understanding the usability of several available types of databases like central databases, Distributed databases, operational databases, etc., it is on the developer’s end to choose the most comprehensive one. Data transformation and performance speed are essential features that a stable database can handle. In this paper, we have analyzed the performance of multiple databases to find out the proficient way that favors NIDS optimization.

[Manuscript received: 21 December 2023 | Accepted: 26 March 2024 | Published: : 30 April 2024]

Review on Detecting Pneumonia in Deep Learning

Toh Jun Jie , Md Shohel Sayeed — Tue, 30 Apr 2024 00:00:00 +0800

Deep learning is a machine learning technique that has been optimized for image classification and object detection. Deep learning has brought huge advancement to the medical field as it helps to diagnose various diseases through computed tomography (CT) scan or X-ray images. Pneumonia is a respiratory disease, and it is one of the killer diseases that causes numerous death all around the world. In 2019, the outbreak of COVID-19 has increased the number of pneumonia patients tremendously. With the increasing number of patients, the clinical and medical facilities have become insufficient. The lack of doctors and radiologists to diagnose pneumonia has caused a high number of patients to be misdiagnosed. Chest image is one of the most effective methods to diagnose this disease, however, examining the X-ray or CT images requires specialists such as radiologists. Meanwhile, examining chest CT or X-ray images might be subjective as the presence of pneumonia can be unclear in the images. The main objective of this paper is to provide a comprehensive review of recent advancement in the diagnosis of pneumonia with deep learning, including state-of-art methodology, datasets, discussion, challenges, and future improvements.

[Manuscript received: 22 December 2023 | Accepted: 21 February 2024 | Published: : 30 April 2024]

A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System

Ang Jia He, Min Thu Soe — Tue, 30 Apr 2024 00:00:00 +0800

Obstacle detection system is a system that reacts to the object in the path and perform action such as stopping robot movement and collision prevention according to the design of algorithm which enhance the safety level of robot. This paper examines the overview of sensor technology that associates with obstacle detection system and car-like robot. This review summarizes the effectiveness and weakness of common type of sensors such as lidar, radar, ultrasonic sensor, infrared sensor, computer vision, sensor fusion and sensor array. This paper will also discuss on control methods for car-like robot that includes hand gestures, voice control, infrared remote control, Android based Bluetooth mobile control, and Wi-Fi based mobile control, outlining the effectiveness and limitation of each control method.

[Manuscript received: 24 December 2023 | Accepted: 21 February 2024 | Published: : 30 April 2024]

A Cutting-Edge Hybrid Approach for Precise COVID-19 Detection using Deep Learning

Hamza Youns, Safdar Abbas, Umar Hayat, Muhammad Hammad Musaddiq, Adeel Hashmi — Tue, 30 Apr 2024 00:00:00 +0800

The early detection of COVID-19 is essential for decision-makers to develop effective containment and treatment plans. Traditionally, researchers interpret computer tomography (CT) scans or X-ray images in order to diagnose this disease. This study aims to demonstrate that deep learning models can be applied to three common medical imaging modes: X-rays, ultrasounds, and CT scans. This study employs and enhances four convolutional neural networks for coronavirus detection, including DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2. In this study, two main experiments were carried out. In the first experiment, a model was developed by combining imagery data to detect this virus. In order to determine which model performed the best, separate models were trained using different datasets in the second experiment. Because there were only so many photos accessible, data augmentation techniques were used to enhance the amount artificially. The results indicate that the proposed models effectively accomplished the task of classifying COVID-19. The accuracy rates achieved by the combined model, utilizing DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2, were 88.21%, 93.02%, and 88.89% respectively. When using the combined imaging dataset, the CNN model employing ResNet101v2 exhibited superior accuracy compared to NASNetMobile, DenseNet121, and MobileNetV2 models.

[Manuscript received: 23 January 2024 | Accepted: 26 March 2024 | Published: : 30 April 2024]

Review on Present-day Breast Cancer Detection Techniques

Wai Ti Chan — Tue, 30 Apr 2024 00:00:00 +0800

Breast cancer remains a prevalent health complication among the female population. Early and reliable detection in an individual is necessary for effective treatment. Thus, R&D into techniques for detection of breast cancer continues to the present. Non-invasive techniques include tactile examinations, electromagnetic scanning and checks for chemical markers. Invasive techniques include biopsies that extract tissue and liquid samples. These techniques have limitations and setbacks that are being addressed with supplementary or complementary techniques. Like the pre-existing techniques, these techniques also rely on comparison of data between control samples and afflicted patients to measure their reliability. Therefore, R&D efforts towards detection of breast cancer have resulted in incremental improvements on established methodologies.

[Manuscript received: 25 January 2024 | Accepted: 13 March 2024 | Published: : 30 April 2024]