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This study delves into the pivotal role of chassis design within Unmanned Ground Vehicles (UGVs), a cornerstone housing various components critical to the UGV's operational functionality. The context of this investigation pertains to the UGV's deployment in environments necessitating resilience against external stresses, while proficiently navigating uneven terrains with low traction. This segment meticulously documents and examines the design process to create a chassis that aligns with the project's requisites. [ABSTRACT FROM AUTHOR]

The presented study focuses on the development of a dumping platform utilizing hydraulic and servo arm mechanisms. The platform serves as a site for dumping collected oil palm fruits by an Unmanned Ground Vehicle (UGV) within an oil plant estate. The investigation is guided by existing research and design principles from various sources, highlighting the mechanics of hydraulic scissor lifts and servo motor control. The prototype employs recycled plywood, surgical syringes, and servo motors to demonstrate its functionality, though acknowledging limitations of plywood in terms of strength and durability. The results showcase the successful lifting of the UGV and tilting of the dumping bucket. The study offers valuable insights into the design considerations and mechanics involved in creating an efficient and environmentally friendly dumping platform. [ABSTRACT FROM AUTHOR]

The oil palm plantation industry heavily relies on foreign labour for harvesting, particularly for collecting loose fruits (LF) alongside fresh fruit bunches (FFB). Manual LF collection, involving bending and repetitive movements, not only diminishes productivity but also poses health risks to workers. This study proposes an automated LF collector to mitigate these challenges. The developed system integrates an LF picker with a robot arm, an LF detector using image processing and a camera, a GPS-based human-follower vehicle, a back-to-home navigation system based on weight detection, and an obstacle avoidance system. The automated LF collector aims to operate autonomously, reducing workforce reliance and enhancing productivity in oil palm plantations. The study discusses the motivation, challenges, and objectives of developing such a system, emphasizing its potential economic and societal benefits. Additionally, the implementation of a novel image processing technique, Faster Objects More Objects (FOMO), using a neural network, is detailed for efficient LF detection. The proposed automated LF collector addresses labour shortages, enhances economic productivity, and reduces the risk of worker injuries in the oil palm industry. [ABSTRACT FROM AUTHOR]

This research delves into the intricate realm of electrical and power management for a prototype Unmanned Ground Vehicle (UGV), encompassing the configuration of circuitry, wire connections, and power dissipation calculations. Leveraging insights from past journal articles, this study selects electronic components based on their attributes, advantages, and drawbacks. The chosen components are then seamlessly integrated into the UGV's General- Purpose Input/Output (GPIO) pin layout, forming an interconnected web of functionality. The UGV's physical arrangement is meticulously crafted to fit within the chassis confines while ensuring a protective casing encapsulates and safeguards all components and wires. [ABSTRACT FROM AUTHOR]

This project proposes the design of a GPS-based autonomous vehicle for the purpose of navigation and following operators in the collection of loose fruits in oil palm plantations. The aim is to reduce the burden on operators who traditionally must manually collect the loose fruits while following behind a truck. The proposed autonomous vehicle utilizes a GPS module to track its real-time location and follows the operator based on their smartphone's GPS coordinates. The vehicle's navigation is controlled by calculating displacement, bearing, and heading angles using GPS coordinates and a digital compass. The vehicle's movement is determined by the heading-to-bearing ratio, allowing it to move forward, turn left or right, or stop depending on the ratio value. The proposed design includes the selection of appropriate components such as GPS module, digital compass, Bluetooth module, motor driver module, and Arduino Mega 2560 microcontroller. The circuit diagram and prototype of the vehicle are presented. The working principle of the vehicle involves point-to- point navigation, calculation of displacement and bearing angles, and utilization of a digital compass for heading determination. The system has been tested and the results show successful navigation and movement of the vehicle based on the operator's location. However, improvements are needed for real-life deployment and extended operation duration. Overall, the GPS-based autonomous vehicle presents a promising solution for efficient and automated loose fruit collection in oil palm plantations. [ABSTRACT FROM AUTHOR]

The palm oil industry in Malaysia has undergone a significant transformation with the adoption of mechanized equipment to streamline the process of fresh fruit bunch (FFB) collection. Traditionally, manual labour-intensive techniques were employed, requiring workers to climb trees for bunch cutting and transportation. The introduction of mechanical buffaloes or harvesters equipped with hydraulic arms has revolutionized the FFB collection process. This paper explores the evolution of hydraulic arms, focusing on innovations such as articulated arms and computer-controlled robotic arms that enhance efficiency, precision, and safety. The replication of this technology through a robotic arm emphasizes data utilization, assembly, and functionality. Additionally, the study delves into the broader implications of mechanization in the palm oil industry, addressing challenges and opportunities for increased productivity and reduced labour costs. The adoption of automated harvesting techniques holds the potential to improve palm oil production, but careful consideration of specific farm needs, along with the advantages and challenges of mechanization, is essential in making informed decisions. [ABSTRACT FROM AUTHOR]

The efficiency of automated palm oil harvest collecting machines is a critical factor in the agricultural industry. This project delves into the essential process of calculating the arm capacity in these machines, which directly impacts their fruit collection capabilities. Through a combination of measurements and calculations, including factors like arm dimensions, fruit weight, and volume, this report equips industry professionals with valuable insights for selecting the most suitable equipment. It also explores the broader context of automated fruit harvesting technologies, highlighting their potential to revolutionize the agriculture sector. With a focus on precise calculations and an understanding of engineering principles, this project lays the foundation for improved operational efficiency and informed decision-making in the world of automated palm oil harvest collection. [ABSTRACT FROM AUTHOR]

This project delves into the groundbreaking technology of the automated harvest collecting machine, meticulously designed to enhance palm oil fruit collection and storage. It stands out for its precision in real-time fruit detection and optimized performance, reducing waste and maximizing agricultural profitability. The integrated detection system, featuring advanced camera detection and finely tuned algorithms, ensures accurate fruit counting and predictive storage capacity. The machine's algorithms process sensor data with exceptional accuracy, considering variables such as fruit size, shape, condition, and colour. The project highlights the continuous prediction process, with the machine adapting its strategies as it detects and counts more fruits. This innovation promises to reshape palm oil collection and processing, providing farmers with a powerful tool to enhance productivity and profitability. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to enhance the accuracy of identifying cardiac conditions through machine learning applied to medical images. To achieve this goal, a Novel Convolutional Neural Network (CNN) algorithm is employed and compared with the K-Nearest Neighbors (KNN) method. The research utilizes a medical dataset containing 304 heart images for diagnosing cardiac disorders. Among these, 242 images are allocated for training, while 60 are used for testing. Both CNN and KNN algorithms undergo training and testing with different data splits to assess their performance in cardiac diagnosis. The evaluation process reserves 20% of the images for testing and validation, while 80% are utilized for training, following clincalc.com standards (power=85%, α=0.05, N=10). Statistical analysis using an independent sample t-test with a significance level of 0.001 (p<0.05) indicates that the Novel CNN algorithm achieves a significantly higher accuracy of 98.04% compared to KNN's 90.95%. These results underscore the superior performance of the Novel CNN algorithm in cardiac diagnosis compared to the KNN approach. [ABSTRACT FROM AUTHOR]

In this project, five essential sections were developed, spanning from the investigative phase to implementation. Each segment, including data collection and transmission, cloud connectivity, AGV Wi-Fi vehicle, fertilizing mechanism, and data analytics, was meticulously crafted based on selected tools and methodologies. NodeMCU ESP8266 was chosen as the microcontroller for data collection and transmission, successfully executing tasks of receiving and transmitting data to the cloud server. Google Sheet was employed as the cloud service provider due to its suitability for the project, with advantages over other platforms discussed. The workflow of the Wi-Fi controlled vehicle and cloud connectivity was elucidated through block diagrams, and the decision tree function was detailed using flowcharts. The successful system implementation and results were showcased. For data analytics, Microsoft Power BI software was selected for its user- friendly interface. The connection between Google Sheet and Power BI was outlined in a block diagram, and step-by-step instructions for observing data analytics results were provided through flowcharts. The Power BI results and dashboards were presented in the system implementation section, emphasizing the comprehensive approach taken in this project. [ABSTRACT FROM AUTHOR]

The presented study focuses on the development and integration of a smart fertilizing system, comprising data collection, cloud connectivity, AGV Wi-Fi vehicle, fertilizer mechanism, and data analytics. This chapter provides a detailed explanation of the integrated system, featuring prototype designs and circuit diagrams for enhanced comprehension. The circuit diagram delineates two key sections: plantation monitoring devices and smart fertilizing vehicles, elucidating activity sequences through block diagrams and flowcharts. Comprehensive testing results are presented to demonstrate the system's functionality, aiding in the identification of optimal fertilizing routes for the AGV Wi-Fi vehicle within the plantation. Furthermore, the integrated system is bolstered with cloud connectivity, enabling precise fertilizer dispensing based on nutrient levels in specific areas. This modification ensures comprehensive coverage of nutrient-deficient regions, enhancing the system's efficiency and feasibility in fertilizing all oil palm trees effectively. [ABSTRACT FROM AUTHOR]

This research focuses on palm fruit harvesting using data from Internet of Things (IoT) technologies. Alternatives to relying on error, one can might consider using sensors for tracking. However, advanced sensors can be expensive. Therefore, we are considering cheaper options with simplified sensors. One form detects movement, while the other detects pressure. These choices could simplify fruit counting without spending much. Our research involves past studies, understanding parts, and how they connect. [ABSTRACT FROM AUTHOR]

The effective use of modern technology is proving useful when compared to contemporary agricultural practices observed in areas such as Southeast Asia and the Middle East. This study investigates the palm oil business in Malaysia, an important contributor to the nation's economy, which must deal with a challenge faced by the Red Palm Weevil (RPW). The timely detection of Red Palm Weevil (RPW) infections has a major effect, and this study investigates a device that utilizes an accelerometer to detect vibrations induced by RPW. The results indicate that the above-mentioned strategy is effective in detecting RPW difficulties at an early stage, therefore delivering a lot of help to the farming industry and other related fields. [ABSTRACT FROM AUTHOR]

This research proposes an automated sensory system that uses the Blynk IoT platform to monitor the production of palm tree oil. The system incorporates temperature, humidity, and soil moisture sensors to gather vital environmental data. The Blynk platform receives this data wirelessly, allowing for real-time analysis and visualization. Using this approach gives palm tree growers the ability to maximize fertilizer and irrigation, improving the quantity and quality of their oil. This technology is beneficial for effective plantation management because of the Blynk platform's user-friendly interface and the expanding role of IoT in agriculture. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into a palm fruit ripeness detection system is the focus of this part of the paper. IoT's remote control and monitoring capabilities are pivotal for sensor-driven setups. In the palm oil industry, where conventional fruit ripeness assessment methods are inefficient, IoT emerges as a solution. Notably, platforms like Blynk and technologies like LoRaWAN are instrumental. Blynk facilitates streamlined device connectivity and management, while LoRaWAN's extensive coverage suits large agricultural areas. A practical prototype integrating Arduino and Node MCU ESP8266 showcases IoT's potential in optimizing agricultural practices. [ABSTRACT FROM AUTHOR]

This study focuses on using a special sensor to figure out if palm fruits are ready to be picked. Usually, people look at the color of the fruits or count how many have come loose from the tree to know if they're ripe. But because there aren't enough workers, sometimes the fruits become too ripe before they're picked. So, this study suggests using a color sensor to check the fruit's color and decide if it's ready. Other research like "Segregation of oil palm fruit ripeness using color sensor" (2022) used a color sensor and a small computer to sort ripe and not ripe palm fruits. Similarly, "Application of Colour Sensor in the Determination of Tomato Fruit Ripeness" (2019) made a tool to sort ripe and not ripe tomatoes using a color sensor and a small motor. "Information System Prototyping of Strawberry Maturity Stages" (2020) used a color sensor and a small computer to find out if strawberries are ripe and showed results on a webpage. This research could help palm oil farms work better by using color sensors to decide if fruits are ripe. [ABSTRACT FROM AUTHOR]

This paper investigates the global use of palm oil, particularly in Malaysia and Indonesia. The palm oil industry faces losses due to a lack of workers causing delays in harvesting fresh fruit bunches (FFBs). To address this, a device is developed using falling fruits to gauge their ripeness. The literature review explores factors contributing to oil losses and how FFB ripeness impacts oil quality. It also examines mechanical fruit harvesting. The process of selecting materials and components for the prototype is explained. The prototype's implementation involves fixing it to palm trees to catch falling fruits. In conclusion, this research offers a solution to the problem of labour shortage-related losses in the palm oil industry by utilizing falling fruits to determine ripeness, potentially benefiting the industry's productivity and financial stability. [ABSTRACT FROM AUTHOR]

The research described in this paper outlines the comprehensive development process of a system spanning five key sections: data collection and transmission, cloud connectivity, AGV Wi-Fi vehicle, fertilizing mechanism, and data analytics. The investigation involved the careful selection of tools and techniques for each section, with a focus on NodeMCU ESP8266 as the microcontroller for data collection and transmission, utilizing various sensors. Google Sheet was chosen as the cloud service provider due to its suitability for the project, and a detailed comparison of platform advantages was provided. The workflow of the WIFI controlled vehicle and cloud connectivity was elucidated through block diagrams and flowcharts. The system implementation successfully integrated Google Sheet with Microsoft Power BI for data analytics, leveraging its user-friendly interface. The connection setup between Google Sheet and Power BI was illustrated, and a step-by-step guide for interpreting data analytics results was presented. The paper concludes by showcasing the implemented system and displaying the Power BI dashboard, demonstrating the practical application of the developed solution. [ABSTRACT FROM AUTHOR]

This project presents an integrated smart fertilizing system designed to tackle challenges in the oil palm industry. A thorough literature review explored key components such as data collection, cloud connectivity, AGV Wi-Fi vehicles, fertilization mechanisms, and data analytics. The methodology involved system integration, enhancement, and field testing in a palm estate, adhering to rigorous engineering standards, sustainability principles, and efficient project management. The project, costing RM245.30, successfully met its objectives during field tests, offering a promising solution for issues in the oil palm sector. [ABSTRACT FROM AUTHOR]

The oil palm plantation industry plays a pivotal role in Malaysia's economy, contributing significantly to global palm oil production and exports. However, the industry faces challenges related to labour-intensive harvesting, particularly the manual collection of loose oil palm fruits (LF). This manual process not only leads to reduced productivity but also poses health risks to workers. To address these issues, the development of an autonomous LF collector is proposed, aiming to reduce labour requirements and enhance collection efficiency. The automated LF collector incorporates a robotic arm for fruit picking, an LF detection system using image processing, a GPS-based human-follower vehicle, a back-to-home navigation system, and obstacle avoidance mechanisms. This innovation allows the LF collector to operate autonomously in oil palm plantations, thus relieving the workforce. The project details materials and components selection for the LF picker, providing insights into the practical implementation of this technology. By reducing the reliance on manual labour and improving LF collection, the proposed autonomous LF collector contributes to increasing overall palm oil production, ensuring the industry's sustainability and economic growth. [ABSTRACT FROM AUTHOR]

The farming industry has an important part in Malaysia's economy, making a substantial contribution to its gross domestic product (GDP) and contributing as a major source of employment for a significant section of the general population. However, the life expectancy of oil palm trees, which are necessary for the cultivation and extraction of palm oil, could be reduced due to insufficient soil conditions. A real-time smart agriculture system was developed with the aim of helping farmers. The system utilizes sensors and Wi-Fi connectivity to gather and transmit data regarding soil conditions to a cloud-based platform which can be accessed through an application for smartphones. This technology provides farmers with accurate data at all times, therefore enhancing their decision-making capabilities and expanding oil palm profitability. The research being investigated shows the possibility of larger implementation in the field of the agriculture industry. [ABSTRACT FROM AUTHOR]

This research introduces a novel approach to upper limb rehabilitation using non-immersive virtual reality (VR) games and trajectory-based analysis. The aim is to enhance post-stroke recovery by engaging patients in enjoyable and meaningful therapeutic activities. The non-immersive VR paradigm offers accessibility and ease of installation, leveraging the Kinect Xbox One and Unity game engine. Three distinct VR games were developed, each focusing on upper limb movements, and evaluated by fifteen participants. The games incorporated virtual hands, square-shaped targets, movement paths, and audio relaxation, creating an engaging environment. A Mean Absolute Trajectory Error (ATE) methodology was employed to assess hand movement accuracy compared to reference coordinates. Results revealed significant improvements, with 75% of participants demonstrating enhanced hand movements across sessions. Analysis indicated decreased ATE values between sessions, suggesting closer alignment between hand movements and reference paths. Participants reported satisfaction and expressed interest in recommending the games to other stroke patients. Notably, both male and female participants exhibited improved muscle activity and trajectory movements. Prospects include expanding the games to include multi-plane movements for a more immersive 3D experience. This research presents a promising direction for stroke rehabilitation, leveraging non-immersive VR games and trajectory analysis to create engaging and effective therapeutic interventions. [ABSTRACT FROM AUTHOR]

This research examines the effect of Polypropylene (P.P.) and Leucaenna Leucocephala (L. Leucocephala) filler compositions on the mechanical properties of biocomposite substrates, targeting microwave antenna applications. The study assessed substrate integrity from different filler concentrations at the Fibre and Biocomposite Centre (FIDEC), Banting, Selangor, Malaysia. By using a compressed blend of polypropylene and wood filler, the substrates were subjected to three-point flexural bending and tensile tests as a scope for mechanical testing in compliance with ASTM D638 and D790 standards. The findings underscored a direct correlation between wood filler quantity and tensile strength; substrates with reduced wood filler exhibited superior elongation and strain metrics. A comparison between PP100 and PB6040 substrates revealed a 21 percent difference in maximum load. Despite the variation in the wood filler, all substrates remained within the antenna's mechanical threshold. Flexural data reinforced these outcomes, with the PP100 substrate outperforming load capacity and the Modulus of Rupture (MOR). Conversely, the Modulus of Elasticity (MOE) data presented an inverse pattern, indicating substrates like PB6040 exhibited higher deformation resistance. The study concludes that environmentally sustainable substrate alternatives can uphold robust mechanical properties, positioning them as viable options for future telecommunications endeavours. [ABSTRACT FROM AUTHOR]

The photos taken by all, bring backs remarkable memories for everyone life. By keeping in mind, this research was focused to enhance the quality of an image, aiming to bring it as close to its original high-quality state. Advanced image processing techniques are employed to achieve this goal. This approach is particularly useful when dealing with degraded images that remain usable but require improvement to enhance their utility or presentation. To elevate the Naturalness Image Quality Evaluator (NIQE) score, we employ the Novel Generative Adversarial Network v3 (NGAN3) with Enhanced Super Resolution Generative Adversarial Networks (ESRGAN). Our study involved two distinct groups: Group 1, which utilized the NGAN3 algorithm, and Group 2, which employed the ESRGAN algorithm. The group sample size carefully determined using the Clincalc tool. This tool also facilitated the calculation of error rates, including an beta level of 0.2, alpha level of 0.05 with power of 0.05. In total, we analyzed 40 samples (20 per group). The NGAN3 algorithm yielded an average NIQE score of 4.46, while the ESRGAN algorithm produced an average NIQE score of 6.36. Notably, the ESRGAN algorithm demonstrated statistical significance with a significance value (P) of 0.001, as determined by a sample t-test. This result underscores the superiority of NGAN3 in terms of image quality. Furthermore, Novel GAN3 generates visually compelling and realistic images, surpassing the existing algorithm. Consequently, it holds the potential to enhance employment prospects by increasing the likelihood of securing well-paid jobs. [ABSTRACT FROM AUTHOR]

This study emphasis on enhancing the detection of air quality pollutants using Novel Convolutional Neural Networks (CNN) compared with Support Vector Machines (SVM). The research study consists of two groups, Novel CNN (N=10) and SVM (N=10), which are applied to the dataset of India Air Quality (Kaggle.com) with the total samples of 17481. The iteration for each group is 10 and was calculated using ClinCalc software with α= 0.05 and a pretest power of 0.8. The proposed Novel Convolutional Neural Networks Technique has the potential to improve air quality detection. Novel Convolutional Neural Networks (95.34%) has increased air quality detection accuracy over SVM (91.2010%) with a significant value of 0.068 (Independent sample t-test, p<0.05). This means that the two methods have a statistically significant difference. The study revealed that compared to the accuracy of SVM, Novel CNN is more accurate in detection of air quality pollutants. [ABSTRACT FROM AUTHOR]

This research aimed to predict flight ticket prices using a new XGBoost optimizer (NXGBRO), aiming for better accuracy than the traditional Ridge regression (RR). We compared the two methods using 40 datasets (20 each) and found that the NXGBRO improved overall system performance. To ensure a reliable setup, we employed the ClinCalc software and set specific parameters like alpha (0.5), G-Power (0.8), and a 95% Confidence Interval (CI). Notably, the NXGBRO attained an imposing 82.7%accuracy compared to Ridge regression's 64.4%. The Independent Samples T-Test (ISTT) is a statistical method used to compare the means of precisely two groups. In our case, it was employed to assess whether there is a significant difference in the performance of the NXGBRO compared to an alternative method. Here's what the results indicate significant difference (p-value = 0.000, p<0.05), confirming the NXGBRO's superiority. In conclusion, this study demonstrates that the NXGBRO not only establishes a connection with the RR but also surpasses it in prediction accuracy. [ABSTRACT FROM AUTHOR]

This study aims to assess the performance of Novel Resnet-50 compared to PSO (Particle Swarm Optimization) in predicting heart disease using audio signal-based heartbeat generation, with the goal of achieving higher accuracy. The innovative ResNet-50 and PSO models are used with a sample size of N=10. The experimental design specifies a pretest Gpower of 0.8, a significance level (alpha) of 0.05, and a confidence interval of 0.95. In terms of accuracy, the ResNet-50 model (96.40%) surpasses the PSO classifier (89.74%), as shown by a statistically significant p- value of 0.38. The statistical analysis utilizing the independent sample T-test demonstrates that there is no statistically significant difference between the two groups. The use of Novel ResNet-50 appears to improve the accuracy of the PSO classifier in this investigation. [ABSTRACT FROM AUTHOR]

The focus of this study lies in assessment of Hidden Markov Model in User Authentication and compared over accuracy of Support Vector Machine algorithm to increase the security of client authentication. This research study utilizes a dataset of 4000 tuples, known as the keystroke-dynamics dataset. The research project includes using two algorithms, Novel HMM (N=10) and SVM (N=10). ClinCalc software was used to calculate the iterations size for each group, which was set at 10 with a pretest power value of 0.8 and a significance threshold α=0.05. The independent sample t-test yielded a statistically significant result with a significance level of 0.000 (p<0.05), Indicates a statistically significant distinction between the two algorithms. Based on the results, the novel HMM method (84.76% accuracy) has significantly higher mean accuracy compared to SVM (71.61%). When compared to HMM performs significantly better than the Decision tree. [ABSTRACT FROM AUTHOR]

In contrast to the Linear Support Vector Machine (LSVM) algorithm, the objective about the investigation is to increase the accuracy rate of automatic speaker detection by applying a unique MLP Multilayer Perceptron algorithm. The National Centre for Human Language Technology (NCHLT) database provided the research dataset for this study, which had a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. Automatic speaker recognition is carried out using MLP and SVM, both of which have the same number of data samples (N=10). MLP, however, has a higher accuracy rate. In comparison to the system, the suggested Multilayer Perceptron gets a success rate of 95.66 percent. higher degree of precision compared to the Linear Support Vector Machine (LSVM). Based on a sample size of twenty (ten from Group 1 and ten from Group 2), the enhanced accuracy rate of automatic speaker recognition was calculated, and the result was an 88.90 percent success rate for the linear SVM classifier. This indicates a noteworthy distinction. The study's significance level was found to be p=0.029. In comparison to Linear Support Vector Machine (LSVM), the proposed Multilayer Perceptron (MLP) model offers a higher level of accuracy for the performance analysis of automatic speaker recognition. [ABSTRACT FROM AUTHOR]

The research aims to enhance the accuracy of phishing site detection in a cloud setting by employing innovative convolutional neural networks over traditional linear regression. The study involved comparing the performance of Novel Convolution Neural Network and Linear Regression algorithms using a sample size of 10, determined through a sample size calculation with a G-power of 0.8, alpha of 0.05, beta of 0.2, and a confidence interval of 95.52%. The Web page Phishing Detection Dataset, comprising 11,430 entries, was employed to identify phishing attacks, a prevalent method for acquiring confidential information from internet users. The findings revealed that the Novel Convolution Neural Network (95.52%) outperformed the Linear Regression (92.14%) algorithm significantly in detecting phishing sites. The Independent sample T-test exhibited a p-value of 0.003, below the significance level of 0.05, indicating a statistically significant difference between the study groups. In the context of cloud-based phishing site detection, Novel Convolution Neural Networks demonstrate superior accuracy compared to Linear Regression. [ABSTRACT FROM AUTHOR]

The primary objective of the study is to compare a Novel Elastic Net approach with the Support Vector Machine Technique to identify narcissistic personality disorder. The g-power value is 80%, datasets from various online sources with continuing reviews a limit of 0.05%, certainty span 95%, mean and standard deviation were iterated ten times to collect data for the unique elastic net approach and support vector machine technology. Elastic Net Algorithm predictability is 86%, whereas Support Vector Machine predictability is 81%. The difference in the predictions is statistically significant at p = 0.001, (p<0.05). This indicates both of the methods differ statistically significant in a meaningful way. When compared to Support vector machine technology, the novel elastic net technique provides improved accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study focusing on Off-Shore Wind Turbines (OWTs) and the development of a real-time performance monitoring system designed using LabVIEW. OWTs represent a significant avenue for harvesting renewable energy, particularly in coastal regions with consistent wind patterns. The system aims to continuously assess and manage the efficiency and functionality of these turbines for maintenance, research, and improvement purposes. The document outlines the structure and operation of OWTs, emphasizing their significance as a renewable energy source and the necessity for monitoring their performance due to their complex mechanical nature. It introduces a monitoring system architecture using LabVIEW's graphical user interface (GUI), employing block diagrams and real-time data processing capabilities to oversee critical parameters such as power output, status of individual turbines, pricing structures, and abnormal conditions detection. Furthermore, it describes the system's functionalities, including power distribution to a target city, standby power supply integration, mechanisms for purchasing/selling excess power, and backup systems to ensure uninterrupted power supply. The system's adaptive nature enables automatic adjustments based on power levels, temperature effects on transmission losses, and timely alerts for maintenance and critical power supply scenarios. The paper concludes by highlighting successful system operations, including user interface interactions, power generation, distribution, and the generation of event reports for comprehensive data analysis. This study provides valuable insights into the efficient monitoring and management of OWTs, ensuring their sustained and reliable performance in generating renewable energy. [ABSTRACT FROM AUTHOR]

The progress of high-speed machinery has led to an accelerated component movement. Unfortunately, most manufacturing procedures yield surfaces that lack ideal geometric precision and surface quality. Each method is tailored to produce specific geometric surfaces and addresses distinct irregularities, necessitating meticulous application within a given production sequence. This study commences by elucidating industrial procedures for gauging the exact smoothness and roughness of finished surfaces. The present research undertook an analysis and comparison of surface finish effects on brass, considering varying speeds, depths of cut, and feed rates in the lathe turning process. Afterward, the samples underwent processing using a surface finish machine to determine the most effective surface finish. This involved synthesizing various surface parameters such as speed, feed, and depth of cut to achieve optimal results. The study encompassed rigorous conditions, including exposure to dust, hydrocarbon solutions, harsh chemicals, and solvents, aimed at enhancing the mechanical properties of metals. [ABSTRACT FROM AUTHOR]

The application of sensing mechanism and soft robotics for controlling a pneumatic actuator-soft robotic gripper was designed and evaluated. The main objective of this project is to design a soft robot gripper that can handle various products in terms of geometries and materials that enable to solve issues faced by industry mainly on the customized products which handling is undefined. By making use of the control system with the aids of sensors and pneumatic actuator, the gripper is able to grasp accurately on the grasped object with different grasping points. The mentioned parameters had been evaluated by reflecting the implication of flexible manufacturing system (FMS) and work in progress (WIP) through grasping different objects that consist of different shapes and material. The result showed that the developed soft robot gripper is able to grasp different objects appropriately. [ABSTRACT FROM AUTHOR]

Kidney is a vital part of the body's metabolic process and a key toxic waste removal mechanism. The blood filtering procedure known as hemodialysis is performed in individuals with severe renal failure. By pushing a patient's blood against a fluid combination called dialysate, a dialysis machine performs hemodialysis. This study seeks to build a prototype design of a Hemodialysis Dialysate Temperature Control System to lessen the risk of patients experiencing temperature shock during hemodialysis treatment since the dialysis machine has problems maintaining the proper temperature of dialysate. To achieve a body temperature of (36⁰C) for patients, two different temperatures of the dialysate were combined with various flow rates. One temperature of the dialysate was maintained below body temperature (35⁰C), and the other was kept above human body temperature (37⁰C). The results show the desired output temperature of 36⁰C is achievable by using fuzzy logic-based controller. The system will give an approximate constant output of desired temperature with a time taken of 670 seconds where the output volume for each circulation is 50 ml/s. The hemodialysis dialysate temperature control system prototype with the implement of fuzzy logic shows that the system consumes time for heating but is able to give a close by to constant desired output temperature. [ABSTRACT FROM AUTHOR]

Exoskeleton suit is one of the technological aids to bring benefits to disability and rehabilitation and industrial worker, especially of manually material handling at the factory floor. The repetitive motion during the operation caused different types of injuries. Physiological assessment towards industrial exoskeleton suit is getting the attention in order the suit is truly able to aid the workers in the challenging working environment. This paper presents a developed exoskeleton suit to allow users to operate it regardless the body size or weight with a substantial comfortable level. The testing result revealed the developed suit not only fulfills the physiological aspect assessment, but the load lifting and EMG sensor signal capturing performance were consistent as well. The Finite Element Analysis (FEA) result also verified the mechanical design is below the yield stress during load lifting operation. [ABSTRACT FROM AUTHOR]

Material handling system (MHS) and flexible manufacturing system (FMS) able to produce highly accurate machines to automate a production system. This paper presented an auto-sizing side guard system for belt conveyor, to handle various sizes and the scenarios of the item on the conveyor. The system was modelled by using SolidWorks and validated through collision detection and SolidWorks Motion. The remote GUI allowed users to monitor and simulate different conditions of the items been transported. The system also equipped with historical data storage which served for future tracking, analysis or even prediction. The system validation result showed a high accuracy data capturing and handling capability, with maximum and minimum percentages of error as 0.2% and 2.7% respectively. The fast response time of the system also revealed that the production efficiency will not be affected or become a hindrance. The Digital Twin (DT), which is one of the important elements in Industry Revolution 4.0 (IR 4.0), also been discussed. [ABSTRACT FROM AUTHOR]

The application of image processing and Neural Network for controlling an actuator or robot was developed and evaluated. The main objective of the system is to establish or create a system for the disability to use facial expressions to control an actuator to assist their daily activities which the group of people easily struggling with. By making use and capturing of the open and closed eyes and mouth motion, the data able to "train" Convolution Neural Network (CNN) models and Mouth Aspect Ratio (MAR) value. All the mentioned parameters had been evaluated together with a new proposed technique, which is named as "quantity" method. The result showed that the developed system is able to capture the actual and true expression of an individual accurately. [ABSTRACT FROM AUTHOR]