Efficiency and accuracy of scheduling algorithms for final year project evaluation management system

Scheduling algorithms play a crucial role in optimizing the efficiency and precision of scheduling tasks, finding applications across various domains to enhance work productivity, reduce costs, and save time. This research paper conducts a comparative analysis of three algorithms: genetic algorithm, hill climbing algorithm, and particle swarm optimization algorithm, with a focus on evaluating their performance in scheduling presentations. The primary goal of this study is to assess the effectiveness of these algorithms and identify the most efficient one for handling presentation scheduling tasks, thereby minimizing the system's response time for generating schedules. The research takes into account various constraints, including evaluator availability, student and evaluator affiliations within research groups, and student-evaluator relationships where a student cannot be supervised by one of the evaluators. Considering these critical parameters and constraints, the algorithm assigns presentation slots, venues, and two evaluators to each student without encountering scheduling conflicts, ultimately producing a schedule based on the allocated slots for both students and evaluators

COVID-19 fake news detection model on social media data using machine learning techniques

Social media sites like Instagram, Twitter, and Facebook have become indispensable parts of the daily routine. These social media sites are powerful instruments for spreading the news, photographs, and other sorts of information. However, since the emergence of the COVID-19 pandemic in December 2019, many articles and headlines concerning the COVID-19 epidemic have surfaced on social media. Social media is frequently used to disseminate fraudulent material or information. This disinformation may confuse consumers, perhaps causing worry. It is hard to counter the widespread dissemination of disinformation. As a result, it is critical to develop a model for recognizing fake news in the news stream. The dataset, which would be a synthesis of COVID-19-related news from numerous social media and news sources, is utilized for categorization in this work. Markers are retrieved from unstructured textual data gathered from a variety of sources. Then, to eliminate the computational burden of analyzing all of the features in the dataset, feature selection is done. Finally, to categorize the COVID -19 related dataset, multiple cutting-edge machine-learning algorithms were trained. Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) are the machine learning models presented. Finally, numerous measures are used to evaluate these algorithms such as accuracy, precision, recall, and F1 score. The Decision Tress algorithm reported the highest accuracy of 100% compared to the Support Vector Machine 98.7% and Naïve Bayes 96.3%

Evaluation of Transfer Learning Pipeline for ADHD Classification via fMRI Images

In recent times, diverse machine learning models have been employed in this field of technology. Nevertheless, the implementation of learning models for image classification remains uncertain and has proven to be challenging. The utilization of transfer learning (TL) has been showcased as a potent technique for extracting crucial features and can significantly reduce training time. Moreover, the feature extractor model has demonstrated excellent performance in the TL method across numerous applications. As of now, there has been no evaluation of using these methods for ADHD classification through functional magnetic resonance imaging (fMRI) applications. The objective of this study is to identify an appropriate pipeline consisting of transfer learning and conventional classifiers for effectively discriminating between individuals with ADHD and those without. For feature extraction, InceptionV3, VGG16, and VGG19 models were employed, which were subsequently combined with either k-nearest neighbor (k-NN) or support vector machine (SVM) classifiers. A dataset consisting of 556 images was collected from the ADHD-200 competition dataset. The data were divided into an 80:20 ratio, with 80% used for training and 20% for testing. The hyperparameters of both k-NN and SVM were optimized using the grid search method. The experimental results revealed that the optimal pipelines were achieved using InceptionV3 coupled with k-NN classifier, where the best parameters were determined as the Minkowski distance metric and a k-value of 1. The pipeline demonstrated a macro-average classification accuracy of 1.00 for the training set and 0.95 for the test set. In summary, the results demonstrate that TL models have successfully exhibited the capability to differentiate fMRI images for ADHD classification

Traffic sign classification using transfer learning: An investigation of feature-combining model

The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS system

Component Testing for VsImaging Library Using Pixel Comparison Technique

Testing the image similarity between two images is a non-trivial task. Image is not a quantitative data input and output. Image contains several complex properties that can be evaluated. In the present paper, properties like height, length and pixel between the two image are compare to get the similarly of the component testing from the VSImaging library image output with the expected image from the library to validate the output image are match the criteria of the expected output image. Furthermore these paper will explain the automatic unit testing of the VSImaging component will be conducted

Managing building checkist plans Using BUSCLIS

This paper presents the software namely Building Submission Checklist System (BUSCLIS). It has been developed to manage the submission of building checklist plans process in the construction industry. BUSCLIS helps to simplify the management for acquiescence data of building plan approval for the Local Authority (LA) and Country Planning in Malaysia through the web based system. BUSCLIS facilitates user through the computerization forms, which provides fast, efficient and effective service to the engineer, architect and contractor. Relevant and timely information manage by sophisticated BUSCLIS with the database management system MySQL

Capturing Requirement Specification for Safety-critical System using SOFL and UML

This paper discusses two different types of capturing and presenting requirement specifications: UML and SOFL. The reason for choosing UML is because it employs an object-oriented approach, and SOFL is chosen as it is a combination of structured and object-oriented, which is an integration of Petri Net and VDM-SL. An On-Board Automation Systems is used to demonstrate how the two techniques work. Also, the differences between the two techniques is discussed, specifically with respect to the requirement in informal and semi-formal specifications

An Investigation on Learning Performance among Disabled People Using Educational Multimedia Software: A Case Study for Deaf People

The increasing number of people with hearing-impaired in Malaysia attracts to produce a variety of technologies, which can assist the deaf people in carrying out their tasks in everyday life as normal people. New technology can help to decrease the difficulty that hearing-impaired people faces in daily life to use the information services like normal people. Thus, this paper focuses on: i) developing a new multimedia courseware for pre-school students with hearing problem, and ii) comparing the deaf student’s learning performance before and after using the courseware. Four modules were developed for e-MSL courseware consist of alphabets, numbers, words and quizzes with colorful text, animation, sounds, video and pictures using Malaysian Sign Language (MSL). Sekolah Rendah Pendidikan Khas (SRPK) Indera Mahkota II, Kuantan has been chosen as the case study for data collection and for investigating the student learning performance on the courseware. The survey results show that 100% of the respondents have agreed that using e-MSL courseware managed to reduce the student learning time more than 80%. The result has indicated that students have shown better learning performance using e-MSL compared to traditional learning

An Investigation on Learning Performance among Disabled People Using Educational Multimedia Software: A Case Study for Deaf People

The increasing number of people with hearing-impaired in Malaysia attracts to produce a variety of technologies, which can assist the deaf people in carrying out their tasks in everyday life as normal people. New technology can help to decrease the difficulty that hearing-impaired people faces in daily life to use the information services like normal people. Thus, this paper focuses on: i) developing a new multimedia courseware for pre-school students with hearing problem, and ii) comparing the deaf student’s learning performance before and after using the courseware. Four modules were developed for e-MSL courseware consist of alphabets, numbers, words and quizzes with colorful text, animation, sounds, video and pictures using Malaysian Sign Language (MSL). Sekolah Rendah Pendidikan Khas (SRPK) Indera Mahkota II, Kuantan has been chosen as the case study for data collection and for investigating the student learning performance on the courseware. The survey results show that 100% of the respondents have agreed that using e-MSL courseware managed to reduce the student learning time more than 80%. The result has indicated that students have shown better learning performance using e-MSL compared to traditional learning