Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Determination of living quarters clutter for caregiver support
Get PDFProviding enough health caregivers due to an aging population has recently been challenging. To alleviate this problem, there's a growing demand for certain household monitoring tasks to be automated especially for elderly persons living independently to reduce the number of scheduled visits by caregivers. Moreover, gathering crucial data using AI technology about functional, cognitive, and social health status, is essential for monitoring daily physical activities at home. This paper proposes a system that determines a room's cleanliness (degree of clutter) to decide whether a caregiver visit is required. A Yolov5-based method is applied to recognize objects in the room including clothes, utensils, clothes, etc. However, due to background noise interference in the rooms and the insufficient feature extraction in YOLOv5, an improvement regime is proposed to improve the detection accuracy. The ECA (Efficient Channel Attention) is added to the network's backbone to focus on feature information, reducing the missed detection rate. The initial anchor box clustering algorithm is improved by replacing K-means with the K-means++ algorithm, enabling more effective adaptation to changing room views. The regression loss function EIoU (Enhanced Intersection over Union) is introduced to optimize the convergence speed and improve the accuracy. The room clutter is determined using set rules by comparing the detection results and prior information from the clean room using IOU. In 31 rooms, 9 subjects' evaluation was used to prove the effectiveness of the proposed system. Compared to the original Yolov5 algorithm, the method proposed in this paper achieved better performanc
Parametric Analysis of Climate Factors for Monthly Weather Prediction in Ghardaïa District Using Machine Learning-Based Approach: ANN-MLPs
Get PDFIn the rapidly developing field of smart cities, accurately predicting weather conditions plays a vital role in various sectors, including industry, tourism, agriculture, social planning, architecture, and economic development. Unfortunately, the instruments used (such as pyranometers, barometers, and thermometers) often suffer from low accuracy, high computational costs, and a lack of robustness. This limitation affects the reliability of weather predictions and their application across these critical areas. This study proposes artificial neural network-multilayer perceptrons (ANN-MLPs). A dataset of 480 data points was used, with 80% allocated for the training phase, 10% for the validation phase, and 10% for the testing phase. The best results were obtained with the structure 6-17-1 (6 inputs, 17 hidden neurons, and 1 output neuron) to predict weather condition data in the Ghardaïa district. Weather conditions parameters include air temperature, relative humidity, wind speed, and cumulative precipitation. Results showed that the most relevant input factors are, in order of importance: earth-sun distance (DT-S) with a relative importance (RI) of 31.10%, factor conversion (d) with an RI of 26.05%, and solar radiation (SR) with an RI of 16.26%. The contribution of the elevation of the sun (HI) has an RI of 13.29%. The optimal configuration includes seventeen neurons in the hidden layer with a logistic sigmoid activation function and a Levenberg–Marquardt learning algorithm, resulting in a root mean square error (RMSE) of 3.3043% and a correlation coefficient (R) of 0.9683. The proposed model can predict both short- and long-term climate factors such as solar radiation, air temperature, and wind energy in areas with similar conditions
Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller
Get PDFThe precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach employing the artificial rabbits optimizer (ARO) for tuning a PID controller with a filtering mechanism (PID-F) in aircraft pitch control systems. This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. Simulation results validate the stability and consistency of the proposed ARO/PID-F approach. Comparative analysis with various optimization algorithm-based controllers from the literature demonstrates the effectiveness of the proposed technique. Specifically, the ARO/PID-F controller exhibits a rapid response, zero overshoot, minimal settling time, and precise control during critical phases. The obtained results position the proposed methodology as a promising and innovative solution for optimizing aircraft pitch control systems, offering improved performance and reliability
A systematic review of the effectiveness of game-based learning in English language teaching
Get PDFThe pedagogical approach of using educational games has been highly effective in instructing non-native English speakers with English as a second or foreign language. The researchers systematically analyze the effectiveness of game-based learning (GBL) in English language teaching (ELT) by identifying and analyzing the game genres used in English language learning and the learning outcomes achieved through GBL. Using a keyword search methodology in the ScienceDirect database, 17 relevant studies were analyzed. The findings indicate that various game genres, including interactive, role-play, memory, simulation, and strategy games, significantly enhance vocabulary, grammar understanding, and overall engagement in ELT. The study concludes that GBL is a highly effective approach in improving English language proficiency among non-native speakers
Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures for Real World Problem Solving
Get PDFSimilarity measures (SMs) are fundamental in various applications, including identifying patterns within medical data and aiding pattern recognition (PR) by quantifying the likeness between different patterns. Moreover, they play a crucial role in real-world problems such as Multiple Criteria Decision Making (MCDM), where decision-makers assess and compare alternatives based on multiple criteria simultaneously. Moreover, Cosine similarity is a measurement that quantifies the similarity between two or more objects. This study presents a comprehensive exploration of Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures (IV IF CSMs) as a novel technique for assessing the degree of association between objects in realworld applications. By extending traditional cosine similarity measures (CSM) to interval-valued intuitionistic fuzzy sets (IV IFS), the proposed IV IF CSMs offer an effective framework for handling uncertainty, ambiguity, and imprecision in decision-making processes. The research demonstrates the practical utility of IV IF CSMs in addressing complex issues in PR, medical diagnosis (MD), and MCDM. In contrast to established methods like Singh’s, Xu’s, and Luo’s measures, our approach consistently generates higher similarity values, encompassing both membership (MF) and non-membership (NMF) with interval values
Optimized Fault Detector Based Pattern Recognition Technique to Classify and Localize Electrical Faults in Modern Distribution Systems
Get PDFThis research presents a method that integrates artificial neural networks (ANN) and discrete wavelet transform (DWT) to identify and classify faults in large power networks, as well as to pinpoint the zones where these faults occur. The objective is to enhance reliability and safety by accurately detecting and categorizing electrical faults. To manage the computational demands of processing the extensive and complex data from the power system, the network is divided into optimal zones, each made visible for fault detection. Niche Binary particle swarm optimization (NBPSO) is employed to place the fault detectors (FD) in each zone. This allows for precise measurement of fault voltage and current phasors without significant cost. The ANN module is tasked with identifying the fault area and locating the exact fault within that zone, as well as classifying the specific type of fault. Discrete Wavelet Transform is used for feature extraction, and a phase locked loop (PLL) is used for load angle computation. The proposed method's validity has been tested on the IEEE-33 bus distribution network
The impact of human resource management, learning, and academic infrastructure in pre-service teachers on pedagogical competence: A multifaceted analysis
Get PDFThis study investigated the influence of the learning process, human resources, and infrastructure on the pre-service teachers' competencies, particularly their pedagogical competence as future professional teachers. This study employed survey research, a type of quantitative investigation. The study's population consisted of all 182 Pre-Service Teachers from the Indonesian Language and Literature Education Department, Mathematics, and English Education Department of the Teacher Professional Education Program, Faculty of Teacher Training and Education, Universitas Islam Malang. Primary and secondary data were the two data types used in this investigation: questionnaires and documentation. The main source of information was the assessment of the lecture process in Batches 1 and 2 of the Pre-Service Teacher of Teacher Professional Education Program. The course grades from LMS courses taken by pre-service PPG students made up the secondary data. Based on the results and discussion, it can be concluded that the learning process and human resources positively influenced the pre-service teacher's competence, especially in the pre-service teacher professional education program. Meanwhile, the data showed that academic infrastructure did not impact pre-service teacher competence
The effect of job stress, job satisfaction, and organizational commitment on employee turnover intention at Maya Ubud Resort & Spa
Get PDFIn maintaining potential human resources (HR), there is one obstacle that often occurs and is faced by companies, namely the desire to change jobs (turnover intention) which results in the employee's decision to leave his job. This study aims to examine and obtain empirical evidence of the effect of job stress, job satisfaction, and organizational commitment on turnover intention. The population in this study were all Maya Ubud Resort SPA employees, totaling 264 people. The sample used in this study amounted to 73 employees based on the Slovin formula method. The analysis technique used is the validity test, reliability test, and multiple linear regression analysis. The results showed that job stress has a positive and significant effect on turnover intention, job satisfaction has a negative and significant effect on turnover intention, and organizational commitment has a negative and significant effect on turnover intention
Social network usage, self-esteem, and irrational consumption online among chinese college students: a mediation model
Get PDFWith the development of e-commerce in recent years, online consumption and the resulting phenomenon of irrational consumption online have gradually attracted research attention. This paper explored to what extent self-esteem and social media usage influence Chinese young consumers’ irrational consumption behavior. A total of 1504 Chinese college students over 30 provinces were recruited by convenience sampling and answered an online survey between August 2019 and May 2020. The Bootstrap method was used to examine the mediating effects of excessive use of social media on the relationship between self-esteem and irrational consumer behaviors. Results showed that both self-esteem and social media usage have a direct and significant relationship with irrational consumption; and the degree of social media overuse mediates the relationship between self-esteem and irrational consumption. Such results imply that an individual's self-esteem can influence his/her irrational consumption behavior by influencing the extent of his/her social media usage. The findings have significant implications for developing programs that aim to promote healthy financial behavior among young people in the digital age. At the same time, the study also makes theoretical contributions to the field of consumer behavior in the cyber era
Analysis and Performance Validation of CRONE Controllers for Speed Control of a DC Motor
Get PDFIn recent decades controllers play a major role for an efficient control and reliable operation of an industrial process. Hence in this paper, a special kind of Commande Robuste d’Ordre Non Entier (CRONE) controller is designed for controlling the speed of DC Motor (DCM). The proper design procedure of two generations CRONE control strategies named as First-Generation CRONE (FGC) and Second-Generation CRONE (SGC) controllers are implemented through the transfer function of armature-controlled DCM. Simulations are performed on MATLAB software in order to investigate the servo responses of two designed CRONE controllers, besides the results are presented in terms of settling time(ts), rise time(tr), Integral Square Error (ISE) and Integral Absolute Error (IAE). In addition, the relay PI controller is designed and the simulation results are presented for comparison purpose. It is evident from the comparing results that the SGC controller is superior for effective process control