For white-light emitting diode (WLED) applications, a green-to-orange emission nitridosilicate-based phosphor is created. The observed wide-band radiation in the green-orange range is caused by Eu2+ and Yb2+ at the trap point of a doubly doped SrSi2O2N2:Eu2+,Yb2+ (SSON:Eu,Yb) nitridosilicate phosphor. The green-color radiation’s decay duration was measured to validate the energy transfer among activator ions. The co-doping various ratios’ influence of activator ions on luminescence features was investigated. The resulting phosphor’s radiation is a function of the activator ion concentrations and raising the Yb2+ concentration causes red-color radiation to dominate the green radiation. To generate white illumination, the resulting phosphor was coupled with an InGaN blue-LED chip having a pumping wavelength of 450 nm. Two stages were taken to achieve hue balance management. Initially, the green to orange proportion was tuned by varying the Eu2+ and Yb2+ ions’ concentrations. At the second stage, the Commission Internationale de L’Eclairage, International Commission on Illumination (CIE) coordinates were changed from [0.2805; 0.2014] to [0.4071; 03789] by raising the amount of phosphor powder used. White illumination produced under optimal conditions has a hue rendering indicator of 89. The designed single-stage dual-hue-releasing nitridosilicate phosphor and blue-LED chip displayed remarkable hue steadiness over a wideband of forward-bias currents (100 to 500 mA at 3 V).
{"title":"Utilizing SrSi2O2N2:Eu2+,Yb2+ phosphor to achieve high hue rendering index and high hue stability","authors":"Ha Thanh Tung, Dieu An Nguyen Thi","doi":"10.11591/eei.v13i1.4724","DOIUrl":"https://doi.org/10.11591/eei.v13i1.4724","url":null,"abstract":"For white-light emitting diode (WLED) applications, a green-to-orange emission nitridosilicate-based phosphor is created. The observed wide-band radiation in the green-orange range is caused by Eu2+ and Yb2+ at the trap point of a doubly doped SrSi2O2N2:Eu2+,Yb2+ (SSON:Eu,Yb) nitridosilicate phosphor. The green-color radiation’s decay duration was measured to validate the energy transfer among activator ions. The co-doping various ratios’ influence of activator ions on luminescence features was investigated. The resulting phosphor’s radiation is a function of the activator ion concentrations and raising the Yb2+ concentration causes red-color radiation to dominate the green radiation. To generate white illumination, the resulting phosphor was coupled with an InGaN blue-LED chip having a pumping wavelength of 450 nm. Two stages were taken to achieve hue balance management. Initially, the green to orange proportion was tuned by varying the Eu2+ and Yb2+ ions’ concentrations. At the second stage, the Commission Internationale de L’Eclairage, International Commission on Illumination (CIE) coordinates were changed from [0.2805; 0.2014] to [0.4071; 03789] by raising the amount of phosphor powder used. White illumination produced under optimal conditions has a hue rendering indicator of 89. The designed single-stage dual-hue-releasing nitridosilicate phosphor and blue-LED chip displayed remarkable hue steadiness over a wideband of forward-bias currents (100 to 500 mA at 3 V).","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"25 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686512","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}
In this study, we used simulation to investigate the optimal working point of a surface acoustic wave-magnetostriction sensor by varying the thickness of the magnetic sensitive layer using the finite elements method. We evaluated the sensor’s sensitivity by simulating the responses at the optimal point and changing the thickness of the magnetic sensitive layer (h3). Additionally, we reduced the piezoelectric substrate thickness (h1) at the optimal point to determine the limit point of the center frequency (f0) and improve the sensor sensitivity for low magnetic field intensity measurements by performing a wavelength reduction (λ). For the simulation, we selected a delay-line FeNi/IDT/AlN structure with specific materials and electrode parameters. Our results show that the optimal structure of the sensor is at h1=400 μm, λ=40 μm, and h3=1,060 nm, with a maximum f0 of 140.38493 MHz and maximum surface acoustic wave velocity of 5,615.4 m/s. At this optimal structure, the sensitivity reaches the maximum value of 10.287 kHz/Oe with a working range from 0 to 89 Oe. We also found that reducing the piezoelectric substrate thickness to 35 μm significantly reduces the manufacturing and simulation time, but the frequency response cannot determine the center frequency.
{"title":"The effect of FeNi-AlN layer thickness on the response of magnetic SAW sensor by FEM simulation","authors":"Do Duy Phu, Hong Si Hoang, Le Van Vinh","doi":"10.11591/eei.v13i1.6312","DOIUrl":"https://doi.org/10.11591/eei.v13i1.6312","url":null,"abstract":"In this study, we used simulation to investigate the optimal working point of a surface acoustic wave-magnetostriction sensor by varying the thickness of the magnetic sensitive layer using the finite elements method. We evaluated the sensor’s sensitivity by simulating the responses at the optimal point and changing the thickness of the magnetic sensitive layer (h3). Additionally, we reduced the piezoelectric substrate thickness (h1) at the optimal point to determine the limit point of the center frequency (f0) and improve the sensor sensitivity for low magnetic field intensity measurements by performing a wavelength reduction (λ). For the simulation, we selected a delay-line FeNi/IDT/AlN structure with specific materials and electrode parameters. Our results show that the optimal structure of the sensor is at h1=400 μm, λ=40 μm, and h3=1,060 nm, with a maximum f0 of 140.38493 MHz and maximum surface acoustic wave velocity of 5,615.4 m/s. At this optimal structure, the sensitivity reaches the maximum value of 10.287 kHz/Oe with a working range from 0 to 89 Oe. We also found that reducing the piezoelectric substrate thickness to 35 μm significantly reduces the manufacturing and simulation time, but the frequency response cannot determine the center frequency.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"85 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687243","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}
Omar I. Dallal Bashi, Husamuldeen K. Hameed, Yasir Mahmood Al Kubaisi, Ahmad H. Sabry
While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.
{"title":"Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique","authors":"Omar I. Dallal Bashi, Husamuldeen K. Hameed, Yasir Mahmood Al Kubaisi, Ahmad H. Sabry","doi":"10.11591/eei.v13i1.5305","DOIUrl":"https://doi.org/10.11591/eei.v13i1.5305","url":null,"abstract":"While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139684526","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}
A. M. Alnasrawi, A. M. Alzubaidi, Ahmed Abdulhadi Al-Moadhen
Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.
{"title":"Improving sentiment analysis using text network features within different machine learning algorithms","authors":"A. M. Alnasrawi, A. M. Alzubaidi, Ahmed Abdulhadi Al-Moadhen","doi":"10.11591/eei.v13i1.5576","DOIUrl":"https://doi.org/10.11591/eei.v13i1.5576","url":null,"abstract":"Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"63 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687217","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}
In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.
当今时代，常见疾病的流行导致死亡人数不断增加。各种感染、病毒和其他病原体都可能导致这些疾病。有些疾病会引发肿瘤，严重威胁人类健康。肿瘤的形式多种多样，包括良性肿瘤、恶性肿瘤和恶性肿瘤，只有恶性肿瘤才会引发癌症。深度学习（DL）算法已成为检测人体内癌症的最有前途的方法之一。然而，现有模型因其局限性而备受批评，例如缺乏对大型数据集的支持，以及依赖于输入图像中数量有限的属性。为了解决这些局限性，并在整个人体中实现高效的癌症检测，我们提出了一种智能决策方法模型（IDMA）。IDMA 与预先训练的 VGG19 相结合，以改进训练。IDMA 分析卷积神经网络（CNN）层图像，寻找恶性肿瘤的迹象，并排除假阳性。灵敏度、精确度、召回率和 F1 分数等各种性能指标被用来评估系统的性能。经评估证明，所建议的系统优于当前的类似系统，在检测癌细胞方面达到了令人印象深刻的 98.67% 的准确率。
{"title":"A deep learning-based intelligent decision-making model for tumor and cancer cell identification","authors":"Putta Durga, Deepthi Godavarthi","doi":"10.11591/eei.v13i1.6469","DOIUrl":"https://doi.org/10.11591/eei.v13i1.6469","url":null,"abstract":"In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"58 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139685903","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}
Anis Hazirah 'Izzati Hasnu Al-Hadi, Siti Mariatul Hazwa Mohd Huzir, Amir Hussairi Zaidi, N. Ismail, Zakiah Mohd Yusoff, Mohamad Hushnie Haron, Mohd Nasir Taib
Aquilaria Malaccensis was found to generate agarwood. Because of its multiple benefits, agarwood essential oil, sometimes known as “black gold” is highly regarded universally. There is currently no accepted method for classifying various grades of agarwood essential oil. Due to the fact that the agarwood essential oil is assessed using a human sensory panel, the existing grading method is ineffective. Since different people may have different viewpoints on how to grade agarwood essential oil, it is not practical to apply the method universally. Several innovative methods for determining the classification of agarwood essential oil have been proposed and put into practise as a result of advanced technology. The study has constructed a pattern analysis on different grades of agarwood essential oil using 2D scatter plot. The results successfully indicate the scatter plots are scattered groupedly.
{"title":"Pattern analysis on Aquilaria Malaccensis using machine learning","authors":"Anis Hazirah 'Izzati Hasnu Al-Hadi, Siti Mariatul Hazwa Mohd Huzir, Amir Hussairi Zaidi, N. Ismail, Zakiah Mohd Yusoff, Mohamad Hushnie Haron, Mohd Nasir Taib","doi":"10.11591/eei.v13i1.5562","DOIUrl":"https://doi.org/10.11591/eei.v13i1.5562","url":null,"abstract":"Aquilaria Malaccensis was found to generate agarwood. Because of its multiple benefits, agarwood essential oil, sometimes known as “black gold” is highly regarded universally. There is currently no accepted method for classifying various grades of agarwood essential oil. Due to the fact that the agarwood essential oil is assessed using a human sensory panel, the existing grading method is ineffective. Since different people may have different viewpoints on how to grade agarwood essential oil, it is not practical to apply the method universally. Several innovative methods for determining the classification of agarwood essential oil have been proposed and put into practise as a result of advanced technology. The study has constructed a pattern analysis on different grades of agarwood essential oil using 2D scatter plot. The results successfully indicate the scatter plots are scattered groupedly.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"27 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686558","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}
Steganography is the process of hiding confidential information within non-secret multimedia such that the 3rd party cannot distinguish if there is a secret message in it or not. Whereas cryptography is the technique of using mathematical concepts to convert information into unreadable codes via a key. This paper will propose two approaches, lossless and lossy image steganography. Both of them will use cryptography and steganography based on three different chaotic maps to ensure information security. In the cryptography part, two chaotic maps will be used to encrypt the secret information, while in the steganography section, one chaotic map is used to embed the message. The secret information will be concealed in the least significant bits (LSBs) of the double-precision image’s pixels. The double precision image is a high-quality image and can be represented in 64 bits per pixel for grayscale images, leading to a very high redundant bit. Simulation results show a high embedding capacity of 60.938% and 400% for lossless and lossy approaches respectively with a peak signal to noise ratio (PSNR) reach of 69.964 dB. Furthermore, this system is extremely secure due to the use of 3 chaotic maps with key space 2448.
{"title":"High capacity double precision image steganography based on chaotic maps","authors":"Salwan Fadhel Al Rubaie, M. Al-Azawi","doi":"10.11591/eei.v13i1.6055","DOIUrl":"https://doi.org/10.11591/eei.v13i1.6055","url":null,"abstract":"Steganography is the process of hiding confidential information within non-secret multimedia such that the 3rd party cannot distinguish if there is a secret message in it or not. Whereas cryptography is the technique of using mathematical concepts to convert information into unreadable codes via a key. This paper will propose two approaches, lossless and lossy image steganography. Both of them will use cryptography and steganography based on three different chaotic maps to ensure information security. In the cryptography part, two chaotic maps will be used to encrypt the secret information, while in the steganography section, one chaotic map is used to embed the message. The secret information will be concealed in the least significant bits (LSBs) of the double-precision image’s pixels. The double precision image is a high-quality image and can be represented in 64 bits per pixel for grayscale images, leading to a very high redundant bit. Simulation results show a high embedding capacity of 60.938% and 400% for lossless and lossy approaches respectively with a peak signal to noise ratio (PSNR) reach of 69.964 dB. Furthermore, this system is extremely secure due to the use of 3 chaotic maps with key space 2448.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"30 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686618","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}
Siti Mariatul Hazwa Mohd Huzir, Anis Hazirah 'Izzati Hasnu Al-Hadi, Amir Hussairi Zaidi, N. Ismail, Zakiah Mohd Yusoff, Mohamad Hushnie Haron, A. Almisreb, Mohd Nasir Taib
The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
{"title":"Pre-processing technique of Aquilaria species from Malaysia for four different qualities","authors":"Siti Mariatul Hazwa Mohd Huzir, Anis Hazirah 'Izzati Hasnu Al-Hadi, Amir Hussairi Zaidi, N. Ismail, Zakiah Mohd Yusoff, Mohamad Hushnie Haron, A. Almisreb, Mohd Nasir Taib","doi":"10.11591/eei.v13i1.5577","DOIUrl":"https://doi.org/10.11591/eei.v13i1.5577","url":null,"abstract":"The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"62 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686791","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}
Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.
{"title":"Prediction of ionospheric total electron content data using NARX neural network model","authors":"Nayana Shenvi, H.G. Virani","doi":"10.11591/eei.v13i1.6506","DOIUrl":"https://doi.org/10.11591/eei.v13i1.6506","url":null,"abstract":"Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"6 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687180","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}
Eyad M. Hamad, S. Alabed, Amer Alsaraira, O. Saraereh
The requirement for a secure emergency communication system has become imperative in tandem with the industrial revolution. Additionally, the development of technology has led to increasingly robust penetration techniques that pose a threat to communication system security, leaving data vulnerable to unwanted third parties. This paper introduces a novel, powerful security approach that ensures a secure emergency communication system. Moreover, this research focuses on several cryptographic techniques among various symmetric and asymmetric ciphers, including advanced encryption standards, substitution, and transposition. The article presents an affordable and secure communication system that can transmit data over long distances with low power consumption using long-range technology. This system features a unique function that transmits updated locations, directing rescuers to the designated location.
{"title":"Implementing and developing multi-stage cryptography technique for low-cost long-range communication system","authors":"Eyad M. Hamad, S. Alabed, Amer Alsaraira, O. Saraereh","doi":"10.11591/eei.v13i1.6989","DOIUrl":"https://doi.org/10.11591/eei.v13i1.6989","url":null,"abstract":"The requirement for a secure emergency communication system has become imperative in tandem with the industrial revolution. Additionally, the development of technology has led to increasingly robust penetration techniques that pose a threat to communication system security, leaving data vulnerable to unwanted third parties. This paper introduces a novel, powerful security approach that ensures a secure emergency communication system. Moreover, this research focuses on several cryptographic techniques among various symmetric and asymmetric ciphers, including advanced encryption standards, substitution, and transposition. The article presents an affordable and secure communication system that can transmit data over long distances with low power consumption using long-range technology. This system features a unique function that transmits updated locations, directing rescuers to the designated location.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"98 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139684782","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}
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