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With the increase in frequency and intensity of natural disasters induced by extreme weather events globally, there are growing threats to human life and property safety. Effective utilization of unmanned aerial vehicle (UAV) vision techniques for rapid disaster assessment and relief is of great value, but still faces many challenges. The purpose of this study is to provide a comprehensive review on the application status, technological progress, and existing problems of UAV vision technology in disaster response. Through extensive investigation of relevant literature, this paper compares and analyze the advantages and disadvantages of key UAV visual techniques for disaster response. The mainstream algorithms and future improvements of several critical technologies are summarized, including image stabilization, target recognition, and three-dimensional reconstruction. Image stabilization technology compensates for UAV motion to obtain clear and stable video, crucial for further analysis. Both mechanical and digital methods have been explored. Target recognition enables UAVs to automatically detect disaster-related entities like survivors, dangerous areas, and blocked roads. Deep learning methods are now dominant. Three-dimensional reconstruction builds precise environment models, providing essential data for assessment and planning. Multi-view methods improve accuracy. In addition, this paper discusses the trends and open challenges faced by UAV vision applications in complex disaster environments, such as robustness, real-time performance, energy efficiency, and data security. Integrating multiple visual modules into a unified system is an important direction. This study provides valuable insights to promote the engineering development and field deployment of UAV vision technology for disaster response. The analysis and summaries serve as important references for future research, development, and applications in this domain. [ABSTRACT FROM AUTHOR]

Maze problem have been a classic problem and have been studied for many times. It inspired lots of mathematics to create new algorithm. This paper's purpose is to summarize the main solutions of the maze problem and states the advantage and disadvantage of each method. In addition, it will also provide an algorithm for creating a random maze. There are variable solutions for solving the maze problem like BFS, DFS and other solutions like biological algorithm. BFS figures out the shortest way but often required lots of storage place, while DFS finds out a often longer way and required more time to get the answer. The essay will also provide a short introduction of biological algorithm and state their strength and weaknesses. The paper focus on the advantage and disadvantage of the maze solutions and summarize two ways of creating a maze. The maze problem has a significant status in the development of algorithm, and it will also influence the development of algorithm in the future. [ABSTRACT FROM AUTHOR]

At present, autonomous vehicle is becoming a popular means of transportation, and can gradually become the public choice of transportation. However, autonomous vehicles still face numerous technical challenges. Due to the influence of different forces during the driving process of a car, there are different situations during the starting stage of the car. When approaching the target speed, there may be shaking or difficulty in reaching the target value. Therefore, the PID controller method is adopted to regulate it. This paper focuses on the research of the starting stage of an autonomous vehicle and uses Simulink and MATLAB for analysis. PID controller is used to adjust the parameters of its starting stage. This paper first analyzes the force on the moving vehicle and lists the function formula. Then, this paper puts forward a method of PID controller based on Simulink to adjust the starting parameters of autonomous vehicles. The simulation experiment shows the advantages of the PID controller system, for example, the accuracy can be higher and the response speed can be faster. This study is of great significance for the starting stage of autonomous vehicles. [ABSTRACT FROM AUTHOR]

With the constant development of Industry 4.0, robotics technology keeps advancing. Correspondingly, people's requirements for robots have become higher. Robots are required to not only realize general linear motion and complex curved motion, but also to have finer control, more stable motion, and to realize the trajectory planning of the specified path. Many previous industrial robots' gripping and handling were planned separately for different parts, which made the operation process cumbersome. Therefore, in this paper, the KUKA youBot robot is used as a research object to study the problem of robotic grasping and handling of materials, to derive a stable model for grasping and handling. In this paper, the robot is modeled and analyzed in terms of forward and inverse kinematics. The interpolation algorithm is used to plan the trajectory of the end of the robot arm during the whole process and also to get the data of each joint and wheel. Finally, the data of each joint and wheel are transferred by a simulator to the robot in Coppeliasim to complete the simulation of grasping and handling of cubes. The simulation achieves stable operation and overall planning. It is inspiring and provides a viable model for real-world robot grasping and handling. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles have evolved from their military origins to a wide range of civilian and commercial uses. However, commercial uses have a higher occupation. This means that civilian uses have high exploitability. The low number of civilian Unmanned Aerial Vehicles is largely dependent on the fact that they are expensive and have few counterparts. However, they have the advantage of being fast and unaffected by urban traffic. An Unmanned Aerial Vehicle with path planning modules and flight controllers makes it possible to transport goods within cities. According to this situation, this paper introduces a possible application to the logistics industry. Urban areas usually have a high demand for the logistics industry. The security and efficiency of delivery are the most significant issues for people in urban areas. However, the traditional control method of UAVs is time-consuming and laborious. Therefore, a new combination method to control a UAV is proposed in this paper. The functions and simulation in this paper provide the feasibility of using the new combination method to control a UAV. In the simulation, a UAV can fly from the start point to the destination steadily. All the obstacles on the map are avoided. This means a UAV can deliver packages with this technology. This study gives a new potential for UAVs in civilian uses, which also improves the existing delivery method. [ABSTRACT FROM AUTHOR]

Indoor robots are one of the current hot directions of artificial intelligence, which can assist people to complete some simple tasks, and the use of indoor robots is becoming more and more common in daily life. With the gradual increase of daily application requirements, the functions that indoor robots need to be equipped with also need to be improved. This paper will introduce how three robots equipped with different sensor combinations take optimization measures in three aspects: signal processing, semantic information extraction and path planning, and the ultimate goal of optimization is to improve the accuracy and robustness of (Simultaneous Localization and Mapping) SLAM autonomous mapping and improve the efficiency of path planning. The optimization strategies in signal processing primarily target the resolution of issues related to particle degradation and particle scarcity. Two distinct improvement measures are employed: the first involves enhancing the filter algorithm based on its application principles, while the second focuses on refining the resampling step within the particle filter to establish more rigorous criteria for particle selection. The extraction of semantic information constitutes a challenging aspect in the current phase of SLAM autonomous mapping. To attain precise semantic information extraction, various network models can be adopted. Two optimization methods for enhancing semantic information extraction encompass refining the algorithmic accuracy of the network model and optimizing the algorithm for segmenting image information. Path planning is now a well-established practice in indoor navigation applications. The optimization measures delineated in this paper primarily pertain to the A* algorithm and the Dynamic Window Approach (DWA) algorithm, with the aim of augmenting the accuracy of path planning. [ABSTRACT FROM AUTHOR]

In recent years, there have been advancements in endoscopic applications that aim to improve surgical accuracy and outcomes. One particular area of focus has been the integration of Simultaneous Localization and Mapping (SLAM) techniques. This paper introduces SLAM methodologies specifically designed for endoscopic scenarios. The study introduces a SLAM system that combines the UNet architecture with ORB SLAM2 to enhance instrument tracking. A vSLAM method is proposed for endoscopic capsule robots. The goal is to improve the reconstruction of flexible environments inside the body in three dimensions. The paper also emphasizes the significance of camera positioning and imaging during procedures. It highlights the process involved in converting points into 2D image points, which is crucial for successful outcomes. Furthermore, the research explores the integration of localization systems for small-scale robotic capsules. This system offers a level of precision. Aims to optimize minimally invasive surgeries by minimizing complications and enhancing overall surgical outcomes. Advanced mathematical formulations are presented in detail to explain how the SLAM system functions and operates. The final part of the research delves into visualization and 3D reconstruction. It investigates the use of Generative Adversarial Network (GAN) frameworks to enhance the precision of depth maps. [ABSTRACT FROM AUTHOR]

In recent years, the development of virtual reality (VR) and augmented reality (AR) technologies has become an increasingly strong and vibrant field. These technologies have been more and more widely used in various fields, such as medicine, education, military, industry, entertainment, etc. Simultaneous Localization and Mapping (SLAM) is a technology widely used in robotic engineering that can enable the robot to localize itself and construct the surroundings in a fully unknown environment. With the utilization of SLAM, the problem of automatic locating of VR and AR devices can be solved and greatly improve the application field of VR and AR. The paper will introduce some algorithms of SLAM used in VR and AR devices and the practical application of VR and AR devices that utilize SLAM technology. The research in this paper will have a significant value in the application of VR and AR. [ABSTRACT FROM AUTHOR]

With the growth of global energy demand and the pressure of environmental protection, ocean energy has received widespread attention as a clean and sustainable form of energy. The purpose of this paper is to analyze various forms of ocean energy, explore its working principles, power generation technologies and application potential, and evaluate its research progress on a global scale. This paper details the working principle and power generation technology of ocean current, tidal, wave, temperature difference and salt difference energy. Especially in terms of salt differential energy, this paper analyzes the key role of permeable membrane technology and the large-scale production of low-cost special membranes. The analysis shows that countries have made significant progress in research in the field of ocean energy, especially in ocean current energy and tidal energy. China has also made breakthroughs in ocean current, wave and temperature difference energy. In short, ocean energy has great potential for development as a clean and sustainable energy source. Future development trends will focus on improving power generation efficiency, reducing costs, and reducing ecological environmental impact. As technology advances and costs fall, ocean energy is expected to occupy a more important position in the global energy system. [ABSTRACT FROM AUTHOR]

The importance of thermal management of batteries and motors is rising as electric vehicles gain popularity. Three crucial technologies for the thermal control of electric car batteries are thoroughly examined in this study: LC, PCMC, and HPC. Each methodology exhibits distinct benefits and is tailored to specific use cases, yet it also presents its set of obstacles and complexities. This paper finds that combining these three technologies can provide a more comprehensive battery thermal management solution. Heat pipes can quickly dissipate heat from hot spots and reduce thermal gradients. Phase change materials can help smooth out temperature fluctuations by absorbing and storing excess heat under high-load conditions. Finally, LC systems can manage the overall heat load by circulating coolant to remove heat from the system and dissipate it into the environment. However, this integrated approach requires careful design and optimization, including consideration of the layout of the components, the heat-generating properties of the different components, and the properties of the selected coolant and phase change materials. Overall, the research in this paper provides valuable insight and opens up new perspectives for understanding and improving battery thermal management in electric vehicles. [ABSTRACT FROM AUTHOR]

Ammonia, recognized as a promising clean energy source, plays a significant role in advancing sustainable development. This paper detailly investigates the multifaceted aspects of ammonia, encompassing its background, synthesis methods, storage, transportation, and diverse applications. A comparative analysis is presented, contrasting the conventional Haber process of ammonia synthesis with innovative methods utilizing clean energy. The green ammonia synthesis process, currently gaining prominence, is highlighted for its potential to significantly reduce carbon emissions. The efficacy of various catalysts in green ammonia synthesis is also examined, revealing the superiority of low-displacement catalysts. Furthermore, the paper explores ammonia's extensive applications in the energy sector, from its role in fuel cells, which can be supplied directly or post-catalytic decomposition, to its utility as engine fuel, either in pure form or in combination with other fuels. The study also underscores ammonia's potential as an auxiliary hydrogen carrier, enhancing the safety and efficiency of hydrogen storage and transportation. By offering insights into ammonia's synthesis processes and its multifarious applications, this research contributes to the discourse on new energy paradigms, emphasizing the benefits of transitioning from traditional energy sources to cleaner, more efficient alternatives. [ABSTRACT FROM AUTHOR]

With the increasing problems of energy shortage and environmental damage, rational recycling of resources and the development of green energy have become an important direction of research. The mechanism and research goals for the photocatalytic conversion of carbon dioxide (CO2) to hydrocarbons are the main topics of this paper. Inspired by natural photosynthesis, photocatalytic carbon dioxide reduction utilises the strong reducing properties of photogenerated electrons to form hydrocarbons by reacting chemically and thermodynamically stable CO2 with H+ in H2O under mild conditions. This paper provides a detailed summary of the application of semiconductor materials such as metal-organic frameworks, bismuth-based materials, tungsten oxide materials and graphene in photocatalytic carbon dioxide reduction and finds that modified or composite materials tend to exhibit superior catalytic performance. For example, NH2-UiO-66(Zr) constructed with mixed ligands showed a 50% increase in formic acid yield in the reaction over NH2−UiO-66(Zr) constructed with a single ligand. In addition, this paper also points out the problems in the current study, such as narrow spectral response range, low product selectivity and low efficiency. In general, photocatalytic carbon dioxide reduction offers a favourable and promising approach to the treatment of greenhouse gases and resource recycling, which is crucial for preventing global climate change and advancing sustainable development. [ABSTRACT FROM AUTHOR]

In the context of low carbon emissions, new energy vehicles powered by battery technology are rapidly emerging as the dominant driving force, replacing traditional fossil fuel vehicles at an astonishing pace. To comprehensively understand the current development and trends of automotive battery technology, this paper analyzes the application status of power batteries in new energy vehicles. Furthermore, it conducts a performance study on the three mainstream chemical batteries—lead-acid batteries, nickel-metal hydride batteries, and lithium-ion batteries. Simultaneously, this paper delves into a discussion on the three major challenges encountered while developing new energy vehicles—battery safety, range anxiety, and charging speed. Ultimately, it identifies significant growth potential and market applications for lithium-ion batteries. Finally, the article proposes current technological strategies to address the challenges faced by the development of new energy vehicles and highlights relevant policy support. With the anticipation of technological breakthroughs in the near future, it is believed that new energy vehicles will eventually dominate the traditional fossil fuel vehicle market and achieve global proliferation. [ABSTRACT FROM AUTHOR]

After the establishment of the Semantic Web concept by Tim Berners-Lee in 2001, there has been a lot of work and study to understand the goals of the Semantic Web. According to initial perceptions and strategy declarations, the Semantic Web will perform as an addition to the present Web, which is largely understandable by humans. Semantic Web is also called Web 3.0, Which enables World Wide Web information, databases, and other structured resources to be presented in a consistent structure that other applications can use. The review paper begins by examining the nature of the Semantic Web and the difference between the traditional Web and the Semantic Web. Moreover, the paper includes a brief overview of the Semantic Web and its technologies and discusses seven of the domains that the Semantic Web has been used. In addition, the paper describes the position of the Semantic Web nowadays. As a result, a researcher who is not familiar with the Semantic Web should find this paper a beneficial tutorial. [ABSTRACT FROM AUTHOR]

Pavement structures are subjected to high traffic loads, which deteriorates the pavement over time. In recent years, various techniques for improving the performance of asphalt pavements have gained popularity. One of these technologies is polymer-modified-binders (PMB) in asphalt mixtures, which are gaining popularity due to reduction in thermal susceptibility and increase in fatigue and rutting resistance. As a result, this paper provides a relative assessment of the recycled polymers utmost recently addressed as bitumen modifiers to enable their selection and cover the utilization of bitumen. The melting point of the polymer, the mixing environments, and the maximum amount of polymer supplemented are all studied. Their influences on the mechanical performance and constancy of asphalt binders in the pavement with and without additives are also discussed. Polymer modification has a major consequence on the rheological characteristics of asphalt binder, according to the findings. The findings of this paper will be useful to the local road construction industry in determining the properties of locally available asphalts and modified versions. A case study of the performance of 3 polymer forms in asphalt paving mixtures is provided; namely Polystyrene-Polymer, Polyvinyl-chloride (PVC), and Phenol-resin. These modified asphalt mixtures were made with asphalt cement that had been modified previously with 2%, 4%, and 6% with weight of asphalt polymer. The effect of polymer modification on mix performance was investigated utilizing Retained Stability Tests (RST) and Indirect Tensile Strength (ITS). Two separate measuring temperatures (25°C and 45°C) are used to evaluate the behavior of the mixture due to the change in the temperature. According to the findings of a case study, using a small percentage of polystyrene polymer lead to an increase in tensile strength, permitting polystyrene modified asphalt mixtures to be used in cold climates. The test results also revealed that offering phenol resin in modified asphalt mixes can increase deformation resistance and strength, allowing it to be used in hot climates. [ABSTRACT FROM AUTHOR]

Information is divided into two types: classical and quantum. Quantum computing, quantum cryptography, and quantum teleportation are just a few of the miracles they can do when working together. Some of the uses range from assisting in the prevention of spies reading private messages to assisting in the prevention of spies reading private communications. Quantum purification and quantum error correction are examples of techniques that will make their implementation easier. Although some of these concepts are still beyond present technological capabilities, quantum cryptography has been established. The possibilities for small-scale quantum computer device prototypes by the end of the millennium are promising. This paper is focusing on addressing the depended approaches which deal with implementation cryptography based on quantumcomputing. Remembering that cryptography is a method of encryption that uses the naturally occurring propertiesof quantum mechanics to secure and transmit data in a way that cannot be hacked. Cryptography is the process ofencrypting and protecting data so that only the person who has the right secret key can decrypt it. For decades now, quantum computing has been hailed as one of the next big revolutions. Quantum computing is not just fasterthan traditional computing methods, but a fundamentally different approach to solve seemingly intractable problems. The mathematical operations that most traditional cryptographic algorithms rely on could be cracked with a sufficiently strong quantum computer. This paper aims to explain how quantum computing affects currentcryptography and expose readers to fundamental post-quantum methods. Quantum computing's influence on IT professionals is becoming increasingly important. As a result, we provide a straightforward introduction to post-quantum cryptography, including quantum-resistant algorithms and quantum key distribution. [ABSTRACT FROM AUTHOR]

In today's power scenario, the increasing power demand is a prevailing concern. The electrical power generated by non-renewable energy sources falls short in meeting this escalating demand. Utilizing renewable sources in conjunction with non-renewable energy becomes imperative. Among various renewable energy sources like wind, tidal, biomass, and solar, the latter is especially advantageous due to its widespread availability and environmentally friendly nature. However, the power generated by solar cells is influenced by atmospheric conditions, and its delivery hinges on connected loads. Achieving maximum solar cell power output is challenging due to atmospheric variations, necessitating the use of various Maximum Power Point Tracking (MPPT) methods. This paper introduces the application of the Incremental Conductance (INC) method for MPPT, which, in comparison to the Perturb and Observe (P & O) method, is better suited for tackling oscillations arising from diverse atmospheric conditions. The selection of a DC-DC CUK converter holds significance for maximizing power, offering low ripple levels at both input and output stages. The paper comprehensively covers converter design, simulation outcomes for a battery-connected standalone PV system employing the INC MPPT method [ABSTRACT FROM AUTHOR]

This research proposes a unique approach to human action recognition using mobile sensor data and a computationally efficient 1D Convolutional Neural Network (1D-CNN). In this research, a 1D-CNN model is constructed to recognize human actions using data from an accelerometer sensor. In order to increase model performance, the study looks into the ideal number of layers and epochs. The proposed method also automates the data annotation to simplify the training process. This research highlights the significance of the chosen model and the size of segments in action recognition. This paper investigates the ideal segmentation size for precisely identifying actions. Experimental analysis confirms the effectiveness of the segmentation length in recognizing human actions by reducing false alarms. This paper demonstrates that increasing the number of fully connected layers does not increase precision or accuracy. The paper concludes by proposing subject-independent methods for action recognition and optimizing power consumption for wearable devices. It highlights the potential of using mobile sensor data and 1D-CNNs for future research in human action recognition. The method and model presented in this paper achieve 95% of recognition accuracy. [ABSTRACT FROM AUTHOR]

Containerization has become a very popular go-to technology for many developers in recent times. This is because of the ease of packaging and deploying software applications with the help of containers. It offers a lot of benefits concerning scalability, security, and portability. However, as is the case with any new and upcoming technology, containerization is not immune to security vulnerabilities and malware infections. Trojans are a type of malware that conceals itself as legitimate software. If these Trojans are embedded inside a container image, it can have severe and disastrous consequences since it will be difficult to detect such contaminated images and it is very easy for an attacker to spread this infected image because of the portability that containers provide. This paper suggests an innovative machine-learning technique to detect and identify Trojans embedded within container images without the need for deploying them to check for their runtime behavioral anomalies. This approach focuses on specific types of Trojans such as Remote Access Trojans and Distributed Denial of Service Trojans. The paper centres around the use of machine learning techniques to make it easier for the detection and identification of such Trojans within container images. The model is deployed as a Flask-based application which provides a simple user interface for interaction, hence making it user-friendly. The proposed system helps in automating the process of Trojan detection and helps in fast identification as well. Overall, this paper gives a very practical approach to Trojan detection and identification within container images, emphasizing the role of machine learning techniques in cyber security in general, thus helping to improve the overall security of containerized applications. [ABSTRACT FROM AUTHOR]

In the modern age, a significant number of official documents, including IDs approved by the government and certificates, are predominantly image-based or paper-based. Extracting, entering, and searching for information from these documents manually can be a cumbersome and time-consuming process for organizations. To address this challenge, Automated ID Document Extraction and Classification (AIDEX) is introduced. This model utilizes machine learning algorithms to automatically classify identity documents issued by the government into predetermined categories such as AADHAAR card, Driving License and PAN card. By identifying unique indicators present in these ID proofs, AIDEX effectively detects and displays vital information, eliminating the need for manual intervention and achieving automation. Through appropriate access and permissions, the extracted data can be securely saved and accessed by authenticated users as required. The application of this strategy significantly reduces the time spent on physical labor, leading to resource conservation. Notably, AIDEX stands out by integrating the identification and data extraction from images of Driving Licenses, distinguishing itself as a novel approach. This paper provides a comprehensive overview of the objectives set for AIDEX, the various techniques employed during its development, and the technical specifications of the resulting model. The findings highlight the potential of AIDEX to streamline information retrieval processes, enhance operational efficiency, and promote automation in handling government-issued identity documents. [ABSTRACT FROM AUTHOR]

Human resource management practices that have unfavorable and negative outcomes in the hotel business, leading to an increase in employee turnover intention, are discussed at length in this study paper. Human resources management plays an effective part in solving the problem of the high rate of planned employee turnover in Malaysian hospitality business. Information included in the study came from secondary data through a wide variety of publications covering topics including human resource management and employee turnover. The literature has revealed how human resource management is related to turnover intention in the hospitality sector. Future researchers can use the insights gained from this study to better understand the unfolding patterns and skill gap analyses in human resource practices necessary to counteract the turnover intention in the industry. This paper discusses the human resource management practices of compensation, performance appraisal, training, recognition, information sharing and competence development and its impact on employee turnover. This study presents the results of previous studies on the topic in order to provide expanded recommendations. Practitioners can use the findings of this study to inform their own efforts to reduce turnover with confidence. Not much research has been done on this possibility in the Malaysian region. [ABSTRACT FROM AUTHOR]

Self-Curing, Self-Compacting and Self-Healingconcrete are comparatively new field in construction industry and futuristic concrete. The composition and fresh properties of these concrete are different from the conventional concrete. Researchers are currentlyconcentrating to promote the different types of concrete basedon their analysis considering the sustainability criteria. In this scientometricanalysis,the research evolution of 50 years from 1974 to 2024 is taken into consideration. The contributions of 732 research papers are taken into account, in that nearly 500 papers are related to the proposed topic. The total number of authors contributing in this area isfound as around 2000. The international collaborationof the result outcome is found as 14.34%. For this scientometric analysis, more than 19742 references are taken from Scopus data. The average citation for this analysis is 7.601 and more. This reflects the importance of the scientometric analysis. This type of scientometric study will act as a valuable resource for the new researchers who are interested to undertake the research in thisfield. [ABSTRACT FROM AUTHOR]

In this paper we have used a detailed reaction network, and the reaction experimental data that has been obtained from a thorough literature survey is used as a basis for the comparison and selection of plug flow reactor (PFR) and continuous stirred tank reactor (CSTR) for the synthesis of methanol over Cu/ZnO/Al2O3 Catalyst. The experimental data that has been obtained is operating between 210°C and 260°C and at pressure up to 70 bar. A generalized Machine learning-based data-driven methodology principal component analysis (PCA) is used for dimensionality reduction of the parameter presented in experimental data; further linear regression model is presented to compare the accuracy of the model with industrial-scale application. The data obtained was also utilized for the prediction of variable for the model which is proposed in this paper. The performance of the model for two different reactors has been compared using the R-Square value. Based on this comparison it is found that the plug flow reactor (PFR) is more efficient, with 94.48% prediction accuracy (R-Square) as compared to continuous stirred tank reactor (CSTR), with 91.98% prediction accuracy. However, more experimental data Is needed, as the used dataset is unable to differentiate for the regression of the adsorption isotherms. Model used is still performing well which suggests that this model can be further used for predictive analysis for experiments outside the training set. [ABSTRACT FROM AUTHOR]

This paper discusses the development of a 3D educational simulator for teaching power supply system fundamentals. The simulator's creation followed a meticulously planned algorithm, beginning with clear educational objectives. It involved 2D conceptual designs and detailed 3D modeling of components like transformers and voltage regulators using software such as AutoCAD, SolidWorks, or Blender. The simulator's interactivity is a significant feature, allowing users to adjust parameters like voltage and observe real-time effects, enhancing understanding. It integrates theory with practical visualization, providing annotations and explanations for each component, creating a comprehensive learning experience. User feedback guided testing and iterative improvements, resulting in a user-friendly, adaptable simulator compatible with various educational platforms. This project highlights the importance of interactive learning tools in technical education, addressing challenges in accurate yet user-friendly educational technology design. It emphasizes the need for continuous improvement to align with evolving technical knowledge. In summary, this paper demonstrates the potential of advanced 3D simulations in STEM education, offering insights into effective tool design and implementation. [ABSTRACT FROM AUTHOR]

This paper analyzes an educational simulator developed for the "Fundamentals of Electrical Supply" course, focusing on its impact. The simulator enhances student understanding and engagement, known for its reliability, user-friendly design, and adaptability. It has received positive feedback from both educators and students. Quantitative assessments demonstrate improvements in grades and analytical skills. The study discusses its integration with traditional teaching methods as a complementary tool. Challenges in modeling electrical systems were overcome through collaboration and refinement. The paper concludes by emphasizing the broader implications of interactive tools in technical education and the potential for future enhancements like virtual reality to further improve the educational experience. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of the development and implementation of a 3D functional scheme for educational simulators focused on power supply fundamentals. The growing complexity of electrical systems and the demand for interactive and engaging educational tools have led to the creation of this innovative approach. The scheme incorporates various components such as transformers, circuit breakers, power lines, and control systems, all interconnected in a 3D network. This paper discusses the step-by-step process, from requirement analysis to deployment, highlighting the benefits of 3D visualization in enhancing student engagement and comprehension. The results indicate a significant improvement in learning outcomes and user satisfaction. The potential for broader applications in engineering education is also explored, marking a promising avenue for future research and development. [ABSTRACT FROM AUTHOR]

This paper presents a new method of soft start of high-voltage and high-power asynchronous electric drives of auxiliary units in mining enterprises, pumping stations and thermal power plants. Advantages and disadvantages of the proposed method are substantiated. The possibility of soft starting of asynchronous motors of high power from thyristors of low power has been proved. The disadvantages in the analysis of existing methods have been studied, and suggestions and solutions have been given. In soft starting of low and medium power induction motors, uses the system "Thyristor voltage converter-induction motor". In soft starting of high-voltage and high-power induction motors, a method of starting using combined low-power thyristors was proposed, taking into account the fact that the prices of high-power thyristors are expensive and that they are unreliable. A scheme of operation of parallel and series low power thyristor groups has been developed. The main disadvantage of this method is the need for uniform distribution of current and voltage values. In the paper, technical solutions for uniform distribution of current and voltage values have been developed. It was proposed to develop an improved system "Thyristor voltage converter-asynchronous motor" formed of parallel-sequentially collllected low-power thyristor groups. [ABSTRACT FROM AUTHOR]

Essential oils are chemical substances found in plants. Essential oil-producing plants include patchouli, clove, and lemongrass plants. Components of essential oil compounds such as flavonoids, phenolics, saponins, terpenoids, and glycosides are thought to have antibacterial activity. The study was conducted to determine the antibacterial activity of patchouli essential oil, cloves, and lemongrass against pathogenic bacteria Escherichia coli and Bacillus subtilis. The antibacterial test method is carried out using diffusion of agar on disc paper. The test samples used were patchouli essential oil, cloves, and lemongrass from Batang district as much as 10 μL 100% concentration, positive control using 20% concentration chloramphenicol as much as 10 μL, and negative control using sterile blank disc paper. The results showed that there was a clear zone formed in the two test bacteria. The clear zone formed in the Escherichia coli test bacteria is until 9.5 mm in patchouli essential oil, until 20 mm in clove essential oil, and until 18 mm in lemongrass essential oil, while the clear zone formed in the test bacteria Bacillus subtilis is until 15 mm in patchouli essential oil, until 26 mm in clove essential oil, and until 20 mm in lemongrass essential oil. It can be concluded that patchouli essential oil, clove essential oil, and lemongrass essential oil have antibacterial activity against E. coli and B. subtilis. [ABSTRACT FROM AUTHOR]

This paper exposes the controllability problem of the nonlinear smoking model. Mathematically, the form of smoking model is the first order of nonlinear differential equation system. The model consists of six variables and two controls. The most important thing before controlling a smoker is to determine its controllability. If the smoking model is in nonlinear standard control model, then it has drift. Applying the Lie Bracket method, the controllability matrix can be found. After determining the rank of the controllability matrix, the controllability of the model can be determined. The result can be stated that the smoking model is locally accessible. This means the control design can be made to control the smoker. In addition, if the optimal control approach is used in controlling the smoking model, through its cost functional model, we can make minimization of beginners and smokers. Another result of this paper is the presentation of optimal control analysis using the Pontryagin Principle approach. The analysis results can be stated as follows, first, each solution of the equation in the dynamic system model is a positive function, second, the model of the system of differential equations with the given initial conditions, has a solution, and that solution is a single solution. [ABSTRACT FROM AUTHOR]

The impact of construction activities on the environment has become a significant global concern due to the environmental degradation caused by the development of construction projects and activities. Noise pollution, water pollution, and air pollution are some of the types of pollution caused by construction activities, which can lead to serious health and environmental consequences. This review paper aims to examine the various types of pollution that occur during different construction activities in construction projects. In addition, this paper also focuses on the impact of concrete batching plant operations on the environment and the techniques that can be used to minimize these impacts. Concrete batching plants have been identified as one of the significant contributors to environmental pollution due to the dust, noise, and exhaust emissions generated during their operation. This paper highlights the importance of adopting appropriate pollution control measures to minimize the impact of concrete batching plant operations on the environment. Furthermore, the paper emphasizes the need to provide a better working environment for onsite workers and minimize the health impact on nearby communities. The use of advanced technologies and eco-friendly practices can contribute significantly to reducing the impact of construction activities on the environment. Overall, this paper provides valuable insights into the impact of construction activities on the environment and highlights the importance of adopting sustainable practices in the construction industry to protect the environment and promote a healthier and safer living environment. [ABSTRACT FROM AUTHOR]

This paper illustrates the detailed accreditation process of ABET, Accreditation Board of Engineering and Technology, for accrediting engineering and Technology programs. ABET being non-governmental agency that accredits engineering & technology, applied & natural sciences, and computing sciences programs. ABET was founded on 10th May 1932 and was founded by Institute of Electrical and Electronics Engineering. International industries accept ABET accredited institute having highest standards in their academic programs. In this accreditation there are eight criterion in general, criterion 1 describes the student outcome evaluations, criteria 2 measures the program educational objectives, criteria 3 is the student outcome calculated from the marks obtained by students, criteria 4 establishes continuous improvement, criteria 5 focus on curriculum of the institute, criteria 6 is about faculties of this institute, criteria 7 measures the facilities provided by the institute and finally, criteria 8 focus on institutional support towards staff of the institute. In this paper we focused on the calculative part of each criterion with equations and suitable examples, the files and documentations required for each criterion and the total workflow of the process. The references and the values used to illustrate the calculations are all taken from the samples provided at ABET official website. [ABSTRACT FROM AUTHOR]

Since clayey soils are highly plastic therefore they are unstable for the purpose of construction. These soils continuously keep on changing temporarily or permanently once they come in contact with water. Soil stabilization is a process of enhancing the properties of soil especially from engineering point of view. This literature review scientifically scrutinizes the functioning features of clayey soils made using industrial wastes such as lime, Granulated Blast Furnace Slag(GBFS) and bagasse ash. The organized exploration was incorporated from Web of Science and SCOPUS by means of altered keywords, and 111paperworkshave been recognized. Following the screening and suitability progression in consistency with PRISMA guidelines, 30 papers have been chosen carefully and hence chosen to be assessed and explored. Here the working features of the soil keeping in view the physical properties, mechanical properties, durability criteria, microstructural examination, statistical examination, cost determination, etc. were scrutinized. Most of the studies using different industrial wastes in soil stabilization have seen to be emphasizing on determining the compressive strength, water absorption, Atterberg's Limits, strength and durability by wetting drying cycles. The PH, electrical conductivity, maximum dry density and optimum water content, thermal conductivity, tensile strength, and flexural strength have also been assessed in latest studies and recounted in this paper. The research papers used industrial wastes such as lime, GBFS, bagasse ash etc and conducted several tests for each of the materials like unconfined compressive strength(UCS), compressibility indices, Atterberg's limit, indirect tensile strength, flexural strength, split tensile strength, California Bearing ratio(CBR) test, Scanning Electron Micrograph (SEM) test, SPC test, and many more tests and microstructural analysis during the stabilization of clayey soil via distinctive industrial wastes for advanced findings found to be short of in existing writings. [ABSTRACT FROM AUTHOR]

Photonic crystals (PC) are atomic materials that have a periodic structure on the optical wavelength scale and can inhibit light transmission at specific wavelengths. In this paper, we use the literature study method with a review of several journals to see the Plane Wave Expansion (PWE) method for calculating the two-dimensional photonic bandgap. The calculation of the band gap of the photonic crystal model in this paper is carried out for a cylindrical 2D periodic lattice and the lattice structure model used is a triangular lattice and a square lattice. From the calculation results, it is concluded that the calculation results for R<0,5a do not show a bandgap. While the calculation results of R<0,5a or R>0,5a on the TM polarization show a band gap. [ABSTRACT FROM AUTHOR]

In this paper, we describe the dynamics of the spread of the COVID-19 disease. In this paper, a mathematical model of the problem is rewritten. Mathematical models can be used to find strategies to prevent the Coronavirus disease spread. A Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model in the spread of COVID-19 disease is solved using fourth and fifth-order Runge-Kutta methods. The simulation results of the two methods show the same pattern of solution behavior as the results of the reference solution obtained using the ODE45 algorithm in the MATLAB software. Theoretically, the accuracy of the fourth-order Runge-Kutta method is not better than that of the fifth-order Runge-Kutta method. We show the correctness of this theory through simulations in this paper by solving the SEIR mathematical model for the COVID-19 disease. [ABSTRACT FROM AUTHOR]

A scaling method based on dimensional analysis was recently used in the research to predict the behaviour of the full scale model. However, this approach has a little impact on the deep drawing process due to the complexity of the structures. In this paper, zeroth-order finite similitude approach based on continuum mechanics is used to overcome these limitations. In this method, two spaces are assumed (physical and trial spaces) which are scaling by using transport equations (volume, mass, momentum and energy) with different time scaling, and dimensions. The aim of this paper is to introduce a new technique which has the ability to scale the full-scale cylindrical deep drawing process where the blank sheet of the full-scale model made from magnesium and conducts under a low-velocity and comparing with trial models where the blank sheets made of steel 3034, aluminium 7074 and copper OFHC. The numerical simulation of the deep drawing under the low-velocity is investigated by using the commercial finite-element program (ABAQUS 6.14). Results showed the capability of this new approach to accurate estimation of the full-scale model behaviour from the different trial material models, also an excellent correlation given with the force-time history. [ABSTRACT FROM AUTHOR]

The basic purpose of this paper is to design and construct a reliable and low-cost system for generating electricity from the sun which represents the greatest renewable source of energy in nature. To achieve this goal, the system includes four sensors that are used to track down the sun to locate optimum solar radiation, and then send this data to Arduino-Uno which functions as a controlling device for this solar energy system. Next, Arduino signals to the servo DC motors to turn the PV panels to face optimum solar radiation. Matlab program was employed for analysis, calculations, and drawing figures in this research paper. The outcomes of experiments reveal that the tracking photovoltaic panels are superior to the static photovoltaic panels in producing electricity. However, the productive capacity of solar panels decreases when their temperature rises above the desired level. Moreover, windblown dust which daily accumulates on the surface of the PV panels reduces their efficiency. Therefore, the solar system is provided with controlled water sprinklers which serve cooling and cleaning purposes. It has been proved that providing the PV panels with this smart cooling technique can maximize relative power gain by (20.0698%). [ABSTRACT FROM AUTHOR]

This research aims to contribute to ending the electricity crisis in Iraq, as it is country rich in solar energy due to its location close to the equator. Electric power stations, which use traditional fuels, are unable to provide the population's electricity needs throughout the day. Moreover, electricity distribution lines sometimes malfunction due to high temperatures. This paper recommends solar energy as an optimal solution to the electricity problem in Iraq. Because it is renewable and environmentally friendly. The paper also focused on techniques that would improve the efficiency of the solar power system. This was achieved by designing and building a solar tracking system to absorb the largest possible amount of solar energy and increase electrical energy production, in addition to building a cooling system consisting of a small pump and nozzles that pump water in the form of a volatile mist to get rid of excess heat from the solar panels at the temperature It rises to extreme levels. The system is also equipped with a cleaning mechanism to rinse the solar panel once, which is harmful to getting dirt and dust because dust is the negative factor that affects the generation capacity of the solar panels. The solar tracking system and the cleaning system are operated by a control unit made in the local markets that work with the Internet of Things (LOT) system and is programmed in the Python language and depends on data completely on the Internet. As for the cooling system, it supplies energy during the day directly from the solar panel, depending on the temperature sensor. The results reveal that the solar tracking, cooling, and cleaning system has significantly improved the production capacity of the solar system and can achieve an energy gain of 39.68%. [ABSTRACT FROM AUTHOR]

In the design of power electronics converters, the operating temperature of semiconductor devices is vital. In this paper, the electrical model is integrated with a more accurate thermal model, which was performed in a circuit simulator and includes a temperature-dependent power losses model. The bidirectional DC-DC converter operating concept and performance are investigated in this work. The PLECS simulation software is utilized to simulate the high-voltage and the low-voltage side switches, both of which have switching losses. In this paper, the performance of two prominent non-isolated bidirectional DC-DC converters (CUK and Buck-Boost) is studied. The SCT50N120 MOSFET model is employed to compare the performance of the two selected converters with an output power of 1 kW. The comparison is based on two nominated ambient temperatures which are 25 °C and 40 °C. A thermal simulation is performed to estimate the safe, better temperature for the MOSFET and higher efficiency. The results show that the Cuk converter exhibits better temperature and fewer losses than the buck-boost converter with the selected operating conditions. [ABSTRACT FROM AUTHOR]

This paper described a hybrid methodology for analyzing a three-phase, four-pole, 1500 W brushless direct current (BLDC) motor. The motor is simulated and analyzed using MATLAB/Simulink with the assistance of RMXprt software to provide some motor missing data, since the motor is not available locally, also to use RMXprt simulation results to compare MATLAB results at full load operating performance, which includes efficiency, current, total losses, output power, and speed. The methodology of this paper succeeded in utilizing a combination of two software MATLAB and RMXprt in analyzing electric machines even with missing some machine data. The performance results of the BLDC motor taken from both software showed a good agreement. [ABSTRACT FROM AUTHOR]

In recent years, multi-element detection and tracking is an essential application of computer vision, including civilian or military surveillance systems. However, the precision and system's capacity to track cars is the key challenges these systems experience in their work. the speed of system interaction, and the reduction of noise associated with the video sequences captured by the system. Therefore, vehicle detection is an important stage in vehicle tracking. The approach to finding moving cars in a video is known as tracking. Vehicle tracking has many applications, including robot vision, traffic control, video surveillance, and simulation. Over the years, many scientists have sought to design useful and efficient algorithms. In this paper, a Kalman filter (KF) was developed to identify and track moving vehicles. The topic of vehicle monitoring and tracking with a fixed camera is addressed in this work. This includes a three-stage work in the first stage, the acquired video is converted to frames and the video is converted from RGB to gray in the first step of preprocessing, then the salt and pepper noise is eliminated using an average filter technology. Background subtraction is performed in the next phase of moving vehicle detection and extraction due to its ability to handle complex backgrounds and appearance changes due to lighting and size, and vehicle detection. The exposed front part is cleaned by applying the shaping process. The detected object is marked as a vehicle with a bounding box. The vehicles are then tracked using a Kalman filter in the next stage. This paper aims to identify and track vehicles in video frames sequence. An average filter was used to remove 70-80 percent of the noise during the noise reduction step. Since the noise, presumably salt and pepper, is less complex than that of the Wiener filter, an intermediate filter was used in place of the Wiener filter. KF was used as a discretionary filter during the tracing step. We conclude that the accuracy of the composite detection achieved using the Kalman filter approach is about 94 percent. It is a great technology for tracking the movement of moving vehicles and has a lot of potential in a traffic control system. The complete system was developed and tested using MATLAB. [ABSTRACT FROM AUTHOR]

The rapid development of new technologies and artificial intelligence offers the field of railway transports and not only that, a series of opportunities related to the aspects of safety, security, performance and other concerns that this sector faces. This paper presents the implications that artificial intelligence can have in railway transports and the relationship between this technology and the transport system. The implications of artificial intelligence in the field of rail transport can cover a wide range of problems that the system faces, such as: safety and security, maintenance and inspection, management and planning, driving and control of railway vehicles and, last but not least, passengers' mobility. This paper provides an overview of the role of artificial intelligence in the development of railways and what it can offer not only at the level of track maintenance, but also at the level of management and decision-making. Despite the rapid development of these new technologies, artificial intelligence research in the railway sector is still in its infancy. [ABSTRACT FROM AUTHOR]

Gamification is not a new term nowadays and was coined back in the year 2002. It started becoming a hot topic when Gartner, technological research and consulting firm based in Stamford rewrote and emphasized it in their CIO New Year resolution 2011. Since then, gamification has achieved multiple objectives ranging from business, employee satisfaction, innovation skill development, and changing behaviour. Curios regarding how this gamification can be used to increase motivation in people lead to this paper. In this paper, an overview of 2795 paper articles and publications related to the area of gamification and motivation from the Scopus database (2011-2022) was conducted. The result of the bibliometric analysis for this research area was made using the VOSviewer program. This bibliometric analysis will give interested researchers in gamification and motivation a starting step to venture into this trend of gamification and motivation toward young people. [ABSTRACT FROM AUTHOR]

The migration of data between NoSQL databases faces challenges due to the heterogeneity of the data schema and storage paradigm between the data-bases. In addition, the flexibility of storage paradigm allows non-standardized schema between records in NoSQL database. For example, in MongoDB, documents within the same collection could have different schema. However, this flexi-schema may cause difficulties in data migration as there is without any standard data schema to follow by for the target data-base during data migration. This paper proposed a migration method ap-plied to suit the different data model or schema for different NoSQL databases. The paper focus on the migration between document-based database and column-based database. The paper also discusses on the database schema of the target database that need to store and support the data that is with different schema. [ABSTRACT FROM AUTHOR]

Customer profiling is one of the important aspects for retailers or service providers used in analyzing consumer characteristics and needs. To build a customer profile, various customer information can be used ranged from demographic information (such as age, gender, salary, address) to customer behavior (such as the frequency to visit shop and money spent). Due to the enormous amount of information available, it becomes inefficient to analyze it through descriptive analysis. Instead, data mining can be used to cluster and summarize the huge amount of information without much information loss. This paper discuss method of utilizing LRFMP (Length Recency Frequency Monetary Periodicity) model and K-means clustering in data mining techniques to perform customer profiling. Furthermore, this paper will describe the evaluation of this work which is based on Elbow method, Silhouette Index, Davies-Bouldin Index, and Calinski Harabasz test. The result suggested LRFMP model is a viable model in customer profiling when use together with K-means clustering technique despite some variable may had contributed less than another. [ABSTRACT FROM AUTHOR]

Recently, the advancements of handling occlusions for Augmented Reality (AR) introduces neural network-based image segmentation methods. However, it comes with increased computational costs. There has been some research that try to tackle this issue. Therefore, this paper compiles and reviews the recent articles that covers on this topic in order to identify the current achievements and the future works. The structure of this paper consists of explaining the basics of neural network-based image segmentation, discussing the methods in the published articles and suggesting expected future improvements. 14 articles were identified and 6 of them are discussed in this article. From the discussions, it is found that the common image segmentation methods comprise of semantic and instance segmentation, with instance segmentation giving more accurate and robust results for tracking objects but with higher computational costs. Some methods also incorporate depth-based techniques and 3D reconstruction to improve the accuracy. Based on the advancements, it is concluded that future works on this topic will be more on improving instance segmentation methods in order to reduce the computational costs. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems are the most widely renewable technologies used to generate power. In this paper, smart monitoring and fault detection on the off-grid solar system is proposed and designed to improve the solar PV system's operation more effectively. Environmental conditions, equipment interference, and changes in existing parameters normally cause problems in the system. A real-time monitoring system is required to monitor the performance of the solar PV system circuit. The internet of things (IoT) is used in this project to directly observe the energy in the system via an application platform. Also, this paper discussed solar PV system faults detection based on the difference in voltage and current values measured during usual and unusual periods based on parameters value. This project will reduce the time and effect in searching for faults in the solar system. The results show several categories, including open circuits and short circuits, are classified as faults in this system. In general, the fault detection system and smart power utilization using IoT monitoring for the photovoltaic system is the focus of this project's development. [ABSTRACT FROM AUTHOR]

This concept paper explores the challenges faced by employees from various industries as the world faces the COVID-19 pandemic at this point. The current situation finds that employees cannot go out to work face to face or are not allowed to come to the office physically as usual as a result of this COVID-19 pandemic outbreak. Furthermore, all citizens from almost all countries are involved in the implementation of the movement control order (MCO). However, jobs that involve day-to-day operations in the organisation still need to carry out their activities as usual for the sake of the survival of their respective organisations. Thus, there are various online platforms that are used to enable activities in the organisation to continue in the interest of stakeholders. To continue daily work activities there are several online platforms that are often used as communication media in organisations such as 'Google Meet', 'Google Classroom', 'Zoom', 'Webex', 'Microsoft Team' and 'Skype'. When the COVID-19 pandemic situation was announced to have subsided, there were organisations that adopted hybrid methods where online and face-to-face communication media were practiced for the purpose of discussions and meetings. This concept paper aims to identify the challenges faced by employees throughout working from home (WFH) when faced with this COVID-19 pandemic. Researchers have proposed three (3) recommendations in facing employee challenges throughout WFH with reference to Sociotechnical Systems Theory (STS) and Motivation-Hygiene Theory based on reference highlights of papers reviewed from 15 journals in previous studies. The results of this study can contribute to knowledge and in addition to the highlights of the work for this topic, especially to employers and employees who practice the concept of WFH. [ABSTRACT FROM AUTHOR]

This research paper investigates the effects of body weight and physical activities on the electrical generating capability of piezoelectric generators. The objectives of this paper include investigating the application of piezoelectric generators in converting mechanical energy from human activities to generate electricity, assessing the effect of active and sedentary activities on piezoelectric generation, and evaluating if different body weight affects the electrical output. To accomplish these objectives, two piezoelectric generators were built. The first generator was built to harvest electrical energy from jumping, walking, and running, and the second was built to harvest electrical energy from sitting. Next, an Arduino device was programmed as an oscilloscope to track the power output from the piezoelectric device. The experiment's sample size was five participants, with three males and two females. The results of the investigation concluded that active activities are more capable of producing higher amounts of power than sedentary activities. However, fatigue is observed when doing active activities, whereas sedentary activities do not show any sign of fatigue. Furthermore, this experiment reported that the body weight factor does not significantly affect the final power generation due to a possible limitation of the piezoelectric material. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples. [ABSTRACT FROM AUTHOR]

Intelligent models that tackle classification problems have shown that it can be developed using different algorithms that are available to use. Therefore, adding an additional level of complexity during the construction of such models. This paper focused on implementing Naive Bayes classification algorithm to the Optimized Electronic Nose System (OENS). Furthermore, the current implementation of the classification layer in OENS is by the usage of the SVM classification algorithm. This paper implemented and presented the results of the implementation of Naive Bayes algorithm by using the same dataset used during the development of OENS. In addition, the development of the Naive Bayes model was done by the usage of the WEKA tool. Apart from that, this paper served as an experiment to compare the results the Naive Bayes algorithm provided compared to the SVM algorithm used during the development of OENS. The classification model that was developed using Naive Bayes algorithm had an accuracy of 89.7% which is lower than the current accuracy of OENS classification model which is 98.1%. Therefore, the implementation of SVM for this problem has been deemed to be better than Naive Bayes. [ABSTRACT FROM AUTHOR]