International Journal of Computer (IJC)

Optimisation of University Examination Timetable Using Hybridised Genetic and Greedy Algorithms: A Case Study of Computer Science Department, University of Ibadan

Sunday J. Agbolade — 2024-06-15

Timetable scheduling is an important aspect of decision-making in any organisation, particularly in academia. An examination timetable is expected to coordinate students, invigilators, courses, examination hall allocation, and time slots. However, the problem could be viewed as a Nondeterministic Polynomial (NP); NP-hard problem, scheduling problem has plagued humanity since its inception. Due to the complex structure of the problem in terms of hard and soft-constraints, most organisations schedule time inefficiently using manual approach. This study introduced an algorithms hybridisation method of genetic and greedy algorithms to automate the timetable scheduling process efficiently. A genetic algorithm is a heuristic search technique based on Charles Darwin's theory of natural evolution. The fitness of each course, venue, and faculty content is determined by the probabilistic optimisation which is the solution candidate in the initial population of all the objects. Subsequently, the greedy algorithm's activities selector selects the best solution. The output demonstrates that the method effectively handled all the constraints associated with timetable scheduling. Hybridising the two algorithms to build a scheduling system, such as the examination timetable. Therefore, it is a viable option to combine genetic and greedy algorithms to have an optimised examination timetable that is flexible to any situation.

Creating Custom Animations Using Motionlayout in Android

Chike Mgbemena — 2024-09-25

This article discusses the process of creating custom animations using Motion Layout in Android. Motion Layout, as an extension of ConstraintLayout, provides developers with a powerful tool for managing animations and transitions between layouts. The main focus is on describing the features of Motion Layout, such as creating smooth transitions and complex animation effects with a minimum amount of code, as well as integration with various user interactions. An overview of the key components, including MotionScene and ConstraintSet, that provide flexibility and power in animation development is provided. Practical code examples and recommendations for using Motion Layout to improve the user interface of mobile applications are considered. The article also focuses on the relevance and demand for the use of animations in modern mobile applications, supporting this with statistical data on the growth of the mobile device market and user expectations.

Acoustic Based Induction Motor Fault Detection System Using Adaptive Filtering Algorithm and Fusion Based Feature Extraction Method

Aye Theingi Oo — 2024-09-14

The proposed machine fault diagnostic system utilizes acoustic signal processing and machine learning for early fault detection and localization in induction motors. The growth of the fault in an induction motor tends to be quick and can result in a significant failure that can lead to economic loss and huge maintenance expenses. Therefore, developing accurate and sensitive induction motor fault diagnostic procedures for the maintenance system is crucial. The main purpose of this paper was to propose an optimized noise reduction technique for an induction motor fault diagnosis system and two novel acoustic feature vectors that can be used in machine learning algorithms. The contribution of this paper is to implement the effectiveness of the fusion features of acoustic signals by concatenating them from different domains. The acoustic dataset for an induction motor is collected in a motor workshop, and the NLMS algorithm is used for background noise cancellation due to its quick adaptation, stability, and efficient error minimization. Data are segmented and normalized during pre-processing, and the induction motor fault diagnosis system is implemented using MATLAB. Zero Crossing Rate (ZCR), Spectral Entropy (SE), and Energy Entropy (EE) feature vectors are combined, and the F1 feature vector is built. Correlation calculations are employed to assess the motor's condition status, and if a fault is detected, the system proceeds with feature extraction for fault localization. In the feature extraction stage for induction motor (IM) fault localization, Gammatone Cepstral Coefficients (GTCC) and Mel Frequency Cepstral Coefficient (MFCC) features are combined to construct the second feature vector (F2). This feature vector is used as training feature data in machine learning algorithms. If the input test signal is strongly correlated with the faulty signals, the type of faults is classified using a Support Vector Machine (SVM) classifier. According to the experimental results, the proposed system achieved an average accuracy of 99% in fault detection, 97.5% in fault localization, and an error rate of 2.5%.

Realistic Sketch-based Face Photo Synthesis using Generative Adversarial Networks (GANs)

Hnin Ei Ei Cho — 2024-06-15

Facial photo-image synthesis and sketch-based face recognition are highly advantageous, particularly in the fields of security forces and forensics. Furthermore, it makes it more feasible for law enforcement to reduce the number of possible suspects in criminal identification operations. However, since pencil drawings and photographs have different properties by nature, creating a synthesis of photographs based on sketches presents a difficult topic. In the last few decades, generative adversarial network-based systems have achieved enormous advances towards improving the performance of image synthesis. It can speed up identification times while improving matching outcomes by reducing gaps among sketch and photo representations. We perform investigations on the well-known photo-sketch pair database CUHK. First, we demonstrate how a generative adversarial network transforms hand-drawn sketches into realistic photos. Secondly, we employ suspect identification by using the pre-trained VGG16-based feature extractor network and KNN classifier. Our technique focuses on the use of deep learning-based networks, which are well-known for their capacity to process data and extract hierarchical features. The presented image-to-image translation framework minimizes the modality differences between hand-drawn face sketches and color images while improving visual quality. Tests on sketch-photo matching demonstrate significant improvements over current state-of-the-art methods on the challenging task of matching sketches with corresponding photos.

Intelligent Clock Gating for FPGA-based RISC Architectures: A Novel Approach to Switching Activity and Dynamic Power Reduction

Prasanth Varasala — 2024-07-10

In modern digital systems, dynamic power consumption remains a critical concern, particularly in Field-Programmable Gate Arrays (FPGAs) utilized in power-sensitive applications. This paper presents a novel intelligent clock gating technique specifically tailored for FPGA-based RISC architectures to effectively reduce switching activity and dynamic power dissipation. Our approach leverages a combination of hardware and software strategies to dynamically control the clock signals to inactive modules, thereby minimizing unnecessary power consumption. The proposed method integrates seamlessly with existing FPGA design flows and RISC architectures, providing a scalable and efficient solution for power management. Through comprehensive simulations and experimental evaluations on standard benchmark circuits, we demonstrate a significant reduction in dynamic power consumption while maintaining performance and functionality. At higher frequencies overall 64% power on total power is saved.

Investigating the Casual Effect in Traffic Accident

Badounmar — 2024-09-05

An important field of study that attempts to increase road safety and lower the frequency and severity of accidents is the investigation of traffic accidents. For the purpose of creating effective preventative methods and policies, it is imperative to comprehend the underlying causes of traffic accidents. The practice of analyzing the relationship between two or more variables to ascertain whether one has a causal effect on the other is known as causal analysis. Through the integration of mutual information for causality analysis and Support Vector Machine (SVM) for prediction, this system is intended to examine the causative impacts of traffic accidents. The system primarily looks at the reasons behind traffic accidents in Thailand between 2016 and 2019, trying to pinpoint important elements and create practical preventative measures. The system gathers a wealth of information, such as the date, time, and position of each collision as well as information on the type of vehicle, the characteristics of the road, driver demographics, and weather. Mutual information is used to quantify dependencies, highlight important interactions, and study the causal linkages between various components. These analyses show how changes in one variable may have an impact on another. By concentrating on the most important variables, the mutual information and SVM integration improves the system's analytical skills and improves model accuracy and interpretability. As a result, our technology produces thorough reports and visualizations that give stakeholders—such as legislators and traffic safety authorities—actionable insights. These observations aid in the creation of focused initiatives and laws meant to lower the frequency and seriousness of traffic accidents in Thailand.

Multi-Class Cancer Classification with SVM Using Wrapper Forward and Backward Feature Selection for Dimension Reduction

May Myat Myat Khaing — 2024-07-01

The use of machine learning (ML) into healthcare has shown enormous growth in recent years. The efficacy of supervised ML models is significantly influenced by the quality of the training data. Feature selection is a crucial factor that affects the performance of machine learning models, especially in complex tasks like multi-class cancer classification. This research investigates the efficacy of using forward feature selection and backward feature elimination approaches in combination with logistic regression. The features generated using these approaches are then used for cancer type classification using support vector machines (SVM).The focus of our study is to use a partially complete gene dataset obtained from the Indian Council of Medical study (ICMR) for the purpose of classifying different types of cancer using Support Vector Machines (SVM). Our approach demonstrated a remarkable success rate of 96% when using features selected via the forward selection method and 97% when using features obtained through the backward selection method in multi-class cancer classification.

Construction of Real Time Data Warehouse

Sohail Irfan — 2024-09-14

Data warehouse is a copy of transaction data specifically structured for querying, analysis and reporting Data warehouse is a database application that actually store and collect the data from any particular business domain for decision support system. There are different ways to implement and design the data warehouse. Some processes are involved the data extraction, transformation and loading. Integration of the data from various sources in to the data warehouse is major concept. So, while the design of the data warehouse such that to entertain all these processes accurately then we can guarantee the data purity. Approach of recent times is becoming very much famous now days that is real time data warehouse. Real time data warehouse actually load the data from the transactional and operational data stores when in real time. As soon the data is coming up in the external data source it will appear into the real time data warehouse so this paper will cater the discussion of structure of real time data warehouse. Real time data warehouse approach has major deficit of extraction and loading process. It could be very efficient source of decision support system if we can eliminate the deficiencies.

Comparative Analysis of Machine Learning Algorithms for Diabetes Prediction: Finding the Optimal Approach

Aftab UL Nabie — 2024-06-21

Diabetes, as a chronic disease, poses a rapidly escalating risk to human health, stemming from a complex interplay of factors such as obesity, elevated blood glucose levels, and various other triggers. Central to its onset is the disruption of insulin hormone function, resulting in abnormal metabolism and increased blood sugar levels. In this paper, we propose a solution to this pressing issue using machine learning techniques. By applying various machine learning algorithms on the Pima Indian diabetes (PID) dataset, we aim to identify the most effective algorithm for this task. Leveraging powerful machine learning algorithms such as (SVM) Support Vector Machine, (RF) Random Forest and others, we endeavor to forecast the onset of diabetes. Through the amalgamation of these techniques, our objective is to proactively identify individuals at risk, enabling timely intervention and preventive measures to safeguard health. The primary goal of this initiative is to mitigate the risk of diabetes onset by forecasting individuals' susceptibility and advocating for lifestyle and dietary adjustments. This study has dual objectives: firstly, to develop and implement a predictive model for diabetes using machine learning techniques, and secondly, to explore effective strategies for achieving success in this endeavor.

Assessing the Effect of Gamification in Increasing the Mastery Level of Grade 8 Students in Technology and Livelihood Education

Marissa Shirley Rose Castro — 2024-07-13

This study aims to investigate whether the gamification in lesson can help on increasing the mastery level of grade 8 students. Gamification, it refers to the application game design elements to an educational setting where competition, points, badges, and rewards helps enhance students’ engagement and motivation. The main goal is to make learning more engaging and interesting specially for learners with short attention span. The research involved a pre-test at the beginning of the quarter, discussion of the lessons with the use of gamification and post-test at the end of the quarter to collect data if the used of gamification really helps in increasing the mastery level of students. Hence, the data supports the assertion that the use of gamification in TLE 8 lessons has resulted in a significant enhancement in student performance. The significant difference between pre-test and post-test scores indicates that gamification has positively impacted the students' learning outcomes.

Architectural Solutions for Scaling SAP BI Systems

Michal Gembčík — 2024-09-05

The article discusses architectural solutions for scaling SAP Business Intelligence (BI) systems. SAP BI is a set of tools designed for managing and analyzing large amounts of data, which allows organizations to obtain useful information to optimize operations. The main aspects of scaling SAP BI systems include architectural approaches, the use of various components such as SAP Business Objects, SAP BW and SAP HANA, as well as the introduction of flexible methods and tools to ensure the sustainability and performance of systems. The stages of development of BI solutions are discussed, starting from simple integrations to complex data storage and analytics systems. Examples of the use of various architectural solutions are provided, depending on the scale and needs of organizations, as well as recommendations for improving data quality and reducing the load on the source systems. The article highlights the importance of a systematic approach to data management and analysis in order to achieve long-term efficiency and competitiveness in the market.

Optimizing Energy Efficiency on Task Allocation for Cyber Foraging in a Transient Mobile Cloud System

Tiako Fani Ndambomve — 2024-09-05

In this research, we address the essential problem of achieving energy-efficient task allocation, which is a vital building block of cyber foraging on a transient mobile cloud. The goal is to minimize the total energy consumption for collaborative task executions among mobile devices in a multi-hop mesh network constructed on a mobile agent-based framework. Accordingly, we propose an energy-efficient task allocation problem formulation that takes into account the required restrictions. Next, we develop an optimal task allocation solution based on the modification of the Kuhn-Munkres algorithm by leveraging on the structural properties of the problem. We further evaluate the effectiveness of the suggested task allocation scheme through numerical study on a simulated system. The simulation reveals a performance gain on energy consumption reduction over other widely used task assignment algorithms.

Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Pose Estimation in Assistance Living Application

May Phyo Ko — 2024-07-01

Estimating human posture from an image or video is an essential task in computer vision. This task has detected body key points from a camera for body posture and gesture recognition technology, which enables the following applications: assisted living in the case of fall detection, yoga pose identification, character animation, and an autonomous drone control system. The rapid development of AI-based posture estimation algorithms for picture recognition has resulted in the availability of quick and dependable solutions for recognizing the human body joint in collected films. One major issue in human posture assessment is the system’s capacity to perform with high accuracy in real-time under shifting ambient conditions. The ultimate goal of the proposed transfer learning-based posture estimation assignment is to achieve real-time speed with virtually no drop accuracy. In this research paper, assisted living program (ALP) is implemented by using a single-shot deep estimation network and a pose key points angular feature. Experimental results show that transfer learning-based pose identifies and estimates posture with a frame rate of about 30 frames per second and a detection accuracy rate of 96.81%.

The Role of Continuous Integration in Accelerating Development and Reducing Defect Risks

Nikhil Badwaik — 2024-08-22

Continuous Integration (CI) is a key practice in software development aimed at minimizing integration errors and speeding up the development process by regularly and frequently merging code changes into a common repository. The paper analyzes the methodological foundations of CI and demonstrates how systematic integration contributes to the early detection of defects and improves the quality of the final product. The technical aspects of CI implementation are considered, including the choice of tools, process configuration and organizational challenges. The authors emphasize the importance of CI in the context of adapting to rapidly changing market demands and technological innovations, discussing benefits such as improved team collaboration, reduced risks associated with late error detection, and faster time to market. In conclusion, it is emphasized that successful CI integration requires cultural changes in teams and approaches to project management.