With the advent of Web 2.0, various platforms and tools have been developed to allow internet users to express their opinions and thoughts on diverse topics and occurrences. Nevertheless, certain users misuse these platforms by sharing hateful and offensive speeches, which has a negative impact on the mental health of internet society. Thus, the detection of offensive language has become an active area of research in the field of natural language processing. Rapidly detecting offensive language on the internet and preventing it from spreading is of great practical significance in reducing cyberbullying and self-harm behaviors. Despite the crucial importance of this task, limited work has been done in this field for nonEnglish languages such as Arabic. Therefore, in this paper, we aim to improve the results of Arabic offensive language detection without the need for laborious preprocessing or feature engineering work. To achieve this, we combine the bidirectional encoder representations from transformers (BERT) model model with a bidirectional gated recurrent unit (BiGRU) layer to further enhance the extracted context and semantic features. The experiments were conducted on the Arabic dataset provided by the SemEval 2020 Task 12. The evaluation results show the effectiveness of our model compared to the baseline and related work models by achieving a macro F1- score of 93.16%.
{"title":"Enhancing Arabic offensive language detection with BERT-BiGRU model","authors":"Rajae Bensoltane, Taher Zaki","doi":"10.11591/eei.v13i2.6530","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6530","url":null,"abstract":"With the advent of Web 2.0, various platforms and tools have been developed to allow internet users to express their opinions and thoughts on diverse topics and occurrences. Nevertheless, certain users misuse these platforms by sharing hateful and offensive speeches, which has a negative impact on the mental health of internet society. Thus, the detection of offensive language has become an active area of research in the field of natural language processing. Rapidly detecting offensive language on the internet and preventing it from spreading is of great practical significance in reducing cyberbullying and self-harm behaviors. Despite the crucial importance of this task, limited work has been done in this field for nonEnglish languages such as Arabic. Therefore, in this paper, we aim to improve the results of Arabic offensive language detection without the need for laborious preprocessing or feature engineering work. To achieve this, we combine the bidirectional encoder representations from transformers (BERT) model model with a bidirectional gated recurrent unit (BiGRU) layer to further enhance the extracted context and semantic features. The experiments were conducted on the Arabic dataset provided by the SemEval 2020 Task 12. The evaluation results show the effectiveness of our model compared to the baseline and related work models by achieving a macro F1- score of 93.16%.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"1 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Interval linear fractional programming problem (ILFPP) approaches uncertain-ties in real-world systems such as business, manufacturing, finance, and eco-nomics. In this study, we propose solving the interval linear fractional pro-gramming (ILFP) problem using interval arithmetic. Further, to construct the problem, a suitable variable transformation is used to form an equivalent ILP problem, and a new algorithm is depicted to obtain the optimal solution with-out converting the problem into its conventional form. This paper compares the range, solutions, and approaches of ILFP with fuzzy linear fractional pro-gramming (FLFP) in solving real-world optimization problems. The illustrated numerical examples show a better range of interval solutions on practical appli-cations of ILFPs and uncertain parameters.
{"title":"A study on the solution of interval linear fractional programming problem","authors":"Yamini Murugan, Nirmala Thamaraiselvan","doi":"10.11591/eei.v13i2.5978","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5978","url":null,"abstract":"Interval linear fractional programming problem (ILFPP) approaches uncertain-ties in real-world systems such as business, manufacturing, finance, and eco-nomics. In this study, we propose solving the interval linear fractional pro-gramming (ILFP) problem using interval arithmetic. Further, to construct the problem, a suitable variable transformation is used to form an equivalent ILP problem, and a new algorithm is depicted to obtain the optimal solution with-out converting the problem into its conventional form. This paper compares the range, solutions, and approaches of ILFP with fuzzy linear fractional pro-gramming (FLFP) in solving real-world optimization problems. The illustrated numerical examples show a better range of interval solutions on practical appli-cations of ILFPs and uncertain parameters.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"11 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Riyadh Ali, R. Antar, Abdul Ghani Abdulrazzaq Abdulghafoor
A multilevel inverter is an electrical device that converts a DC voltage into a higher AC voltage by generating a stepped waveform with several voltage levels. Unlike traditional inverters that produce a square wave or a pulse-width modulated (PWM) waveform with only two voltage levels, multilevel inverters can generate waveforms with three or more levels, resulting in reduced harmonic distortion, improved efficiency, and decreased electromagnetic interference. The design and control of multilevel inverters are active research areas that aim to enhance their performance, reliability, and scalability. In this research, a 31-level asymmetric cascaded multilevel inverter is suggested. The proposed multilevel inverter (MLI) system employs four photovoltaic cells as dc sources with structure of (1:2:4:8) Vdc. The system is modeled by MATLAB/Simulink and total harmonic distortion (THD) values of the output voltage and current are 1.106% for resistive load, and 1.35% and 0.403% for inductive load. These outcomes demonstrate the recommended circuit's efficacy and demonstrate its suitability for medium- and high-power applications.
{"title":"Harmonics mitigation technique for asymmetrical multilevel inverter fed by photovoltaic sources","authors":"Ali Riyadh Ali, R. Antar, Abdul Ghani Abdulrazzaq Abdulghafoor","doi":"10.11591/eei.v13i2.6607","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6607","url":null,"abstract":"A multilevel inverter is an electrical device that converts a DC voltage into a higher AC voltage by generating a stepped waveform with several voltage levels. Unlike traditional inverters that produce a square wave or a pulse-width modulated (PWM) waveform with only two voltage levels, multilevel inverters can generate waveforms with three or more levels, resulting in reduced harmonic distortion, improved efficiency, and decreased electromagnetic interference. The design and control of multilevel inverters are active research areas that aim to enhance their performance, reliability, and scalability. In this research, a 31-level asymmetric cascaded multilevel inverter is suggested. The proposed multilevel inverter (MLI) system employs four photovoltaic cells as dc sources with structure of (1:2:4:8) Vdc. The system is modeled by MATLAB/Simulink and total harmonic distortion (THD) values of the output voltage and current are 1.106% for resistive load, and 1.35% and 0.403% for inductive load. These outcomes demonstrate the recommended circuit's efficacy and demonstrate its suitability for medium- and high-power applications.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. S. Aprillia, D. K. Silalahi, K. B. Adam, Putu Krishna Bhaskara Perteka Yuda
The use of rechargeable batteries for electrical energy storage requires a voltage balancing system. The voltage balancing system requires monitoring in the use of rechargeable batteries so that they can be utilized properly and can improve the electrical energy storage system. In this research, a monitored lead acid battery voltage balancing system was designed so that the management of the battery voltage balance level and the storage of electrical energy in rechargeable batteries can be stored and used optimally. In this research a series of lead acid battery voltage detection and power dissipation circuits were designed. The power dissipation circuit uses the controlled shunt resistor method which is used when the voltage of the lead acid battery being charged exceeds the maximum voltage. This method is easy to implement and can display the value of the lead acid battery voltage and other parameters, so that it can be monitored by the user. The results obtained show that the average voltage error for batteries 1 and 2 is 0.09% and 0.3% respectively. The power dissipation circuit can dissipate lead acid battery power above the reference voltage VREF=±7 V, with a balanced voltage of 6.8 V at 140 minutes and 160 minutes.
{"title":"Design of passive voltage balancer system for lead acid battery","authors":"B. S. Aprillia, D. K. Silalahi, K. B. Adam, Putu Krishna Bhaskara Perteka Yuda","doi":"10.11591/eei.v13i2.5806","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5806","url":null,"abstract":"The use of rechargeable batteries for electrical energy storage requires a voltage balancing system. The voltage balancing system requires monitoring in the use of rechargeable batteries so that they can be utilized properly and can improve the electrical energy storage system. In this research, a monitored lead acid battery voltage balancing system was designed so that the management of the battery voltage balance level and the storage of electrical energy in rechargeable batteries can be stored and used optimally. In this research a series of lead acid battery voltage detection and power dissipation circuits were designed. The power dissipation circuit uses the controlled shunt resistor method which is used when the voltage of the lead acid battery being charged exceeds the maximum voltage. This method is easy to implement and can display the value of the lead acid battery voltage and other parameters, so that it can be monitored by the user. The results obtained show that the average voltage error for batteries 1 and 2 is 0.09% and 0.3% respectively. The power dissipation circuit can dissipate lead acid battery power above the reference voltage VREF=±7 V, with a balanced voltage of 6.8 V at 140 minutes and 160 minutes.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"10 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Munsarif, Muhammad Sam'an, Andrian Fahrezi
Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.
{"title":"Convolution neural network hyperparameter optimization using modified particle swarm optimization","authors":"Muhammad Munsarif, Muhammad Sam'an, Andrian Fahrezi","doi":"10.11591/eei.v13i2.6112","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6112","url":null,"abstract":"Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"6 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Mei Leong, Mohd Hanafi Ahmad Hijazi, A. Saudi, Chin Kim On, Ching Fui Fui, H. Haviluddin
The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
{"title":"The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization","authors":"Jing Mei Leong, Mohd Hanafi Ahmad Hijazi, A. Saudi, Chin Kim On, Ching Fui Fui, H. Haviluddin","doi":"10.11591/eei.v13i2.5840","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5840","url":null,"abstract":"The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.
{"title":"Solar power forecasting model as a renewable generation source on virtual power plants","authors":"Suwarno Suwarno, Doni Pinayungan","doi":"10.11591/eei.v13i2.5870","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5870","url":null,"abstract":"This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the in-verter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers.
{"title":"An enhanced control scheme for multifunctional grid connected PV system using fuzzy and predictive direct power control","authors":"B. Boukezata, A. Chaoui, J. Gaubert, O. Boutalbi","doi":"10.11591/eei.v13i2.5660","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5660","url":null,"abstract":"This paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the in-verter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"4 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Sulistiani, A. Syarif, K. Muludi, Warsito Warsito
Narcissistic personality disorder (NPD) is a personality disorder that affects various aspects of life, including relationships, employment, school, and finances. Persons with NPD usually feel unhappy and disappointed when no one helps them and is not praised for their achievements. Diagnosing narcissism is generally done using a screening test that consumes time and costs a lot. This research aims to evaluate the performance of several feature selection (FS) approaches on machine learning (ML) techniques (support vector machine (SVM), random forest classifier (RFC), and Naive Bayes). Three scenarios of FS (all features, the information gain technique and the gain ratio (GR) feature technique) are used for each ML method. Several experiments using the benchmark narcissistic disorder dataset have been done. It adopts the k-fold cross-validation (10-fold cross-validation) strategy. We evaluate the method’s performance by measuring its accuracy, error rate, and processing time. It is shown that the RFC GR strategy gives the best performance with an accuracy of 100%.
自恋型人格障碍（NPD）是一种影响生活各个方面的人格障碍，包括人际关系、就业、学业和财务。自恋型人格障碍患者通常会在没有人帮助他们或他们的成就得不到赞扬时感到不开心和失望。诊断自恋一般需要通过筛查测试，耗时费钱。本研究旨在评估几种特征选择（FS）方法在机器学习（ML）技术（支持向量机（SVM）、随机森林分类器（RFC）和奈夫贝叶斯）上的性能。每种 ML 方法都使用了三种 FS 方案（所有特征、信息增益技术和增益比（GR）特征技术）。使用基准自恋障碍数据集进行了多次实验。它采用了 k 倍交叉验证（10 倍交叉验证）策略。我们通过测量其准确率、错误率和处理时间来评估该方法的性能。结果表明，RFC GR 策略的准确率为 100%，性能最佳。
{"title":"Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder","authors":"H. Sulistiani, A. Syarif, K. Muludi, Warsito Warsito","doi":"10.11591/eei.v13i2.6717","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6717","url":null,"abstract":"Narcissistic personality disorder (NPD) is a personality disorder that affects various aspects of life, including relationships, employment, school, and finances. Persons with NPD usually feel unhappy and disappointed when no one helps them and is not praised for their achievements. Diagnosing narcissism is generally done using a screening test that consumes time and costs a lot. This research aims to evaluate the performance of several feature selection (FS) approaches on machine learning (ML) techniques (support vector machine (SVM), random forest classifier (RFC), and Naive Bayes). Three scenarios of FS (all features, the information gain technique and the gain ratio (GR) feature technique) are used for each ML method. Several experiments using the benchmark narcissistic disorder dataset have been done. It adopts the k-fold cross-validation (10-fold cross-validation) strategy. We evaluate the method’s performance by measuring its accuracy, error rate, and processing time. It is shown that the RFC GR strategy gives the best performance with an accuracy of 100%.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahmood A. Al-Shareeda, A. Alsadhan, Hamzah H. Qasim, S. Manickam
The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.
物联网（IoT）研究设想了一个世界，在这个世界里，普通物体都与互联网相连，并从周围环境中交易、存储、处理和收集数据。由于其固有的资源限制，物联网设备通常无法直接承载应用服务，尽管它们在促进数据供应以实现电子服务方面的重要性与日俱增。由于雾计算（FC）可以与集中式云系统一起生存和工作，并将后者扩展到网络边缘，因此它可能是摆脱这些限制的合适范例。本文回顾了物联网在应用、设计参数和 FC 方面的概况。同时，本文从通信、安全、数据质量、传感和执行管理、编码、分析和决策等方面介绍了物联网雾计算（FC-IoT）的架构。此外，本综述还介绍了 FC-IoT 的几个特点和面临的挑战。最后，还讨论了本文的开放性问题。
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