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Development of a Web-based Application by Employing a Convolutional Neural Network (CNN) to Identify Pests and Diseases on Pakcoy (Brassica rapa subsp. chinensis) 基于卷积神经网络(CNN)的油菜病虫害识别应用的开发对)
Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-10-03 DOI: 10.47836/pjst.31.6.13
Achmad Zein Feroza, Nelly Oktavia Adiwijaya, Bayu Taruna Widjaja Putra
The development of Pakcoy cultivation holds good prospects, as seen from the demand for vegetable commodities in Indonesia. Its cultivation is consistently rising in terms of volume and value of vegetable imports. However, the cultivation process encounters multiple issues caused by pests and diseases. In addition, the volatile climate in Indonesia has resulted in uninterrupted pest development and the potential decline of Pakcoy’s productivity. Therefore, the detection system for pests and diseases in the Pakcoy plant is called upon to accurately and quickly assist farmers in determining the right treatment, thereby reducing economic losses and producing abundant quality crops. A web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101. A total of 1,226 images were used for training, validating, and testing the dataset to address the problem in this study. The dataset consisted of several plant conditions with leaf miners, cabbage butterflies, powdery mildew disease, healthy plants, and multiple data labels for pests and diseases presented in the individual image. The results show that the MobileNetV2 provides a minimum loss compared to GoogLeNet and ResNet-101 with scores of 0.076, 0.239, and 0.209, respectively. Since the MobileNetV2 architecture provides a good model, the model was carried out to be integrated and tested with the web-based application. The testing accuracy rate reached 98% from the total dataset of 70 testing images. In this direction, MobileNetV2 can be a viable method to be integrated with web-based applications for classifying an image as the basis for decision-making.
从印尼对蔬菜商品的需求来看,紫菜种植的发展前景良好。就蔬菜进口的数量和价值而言,其种植持续增长。然而,种植过程中遇到了病虫害引起的多重问题。此外,印度尼西亚多变的气候导致害虫不断发展,Pakcoy的生产力有可能下降。因此,需要Pakcoy植物病虫害检测系统准确、快速地帮助农民确定正确的处理方法,从而减少经济损失,生产出丰富的优质作物。一个基于web的应用程序与几个著名的卷积神经网络(CNN)合并,如MobileNetV2, GoogLeNet和ResNet101。为了解决本研究中的问题,总共使用了1226张图像来训练、验证和测试数据集。该数据集包括几种植物状况,包括采叶虫、卷心菜蝴蝶、白粉病、健康植物,以及单个图像中呈现的病虫害的多个数据标签。结果表明,与GoogLeNet和ResNet-101相比,MobileNetV2的损失最小,得分分别为0.076、0.239和0.209。由于MobileNetV2体系结构提供了一个很好的模型,因此将该模型与基于web的应用程序进行了集成和测试。在70张测试图像的数据集中,测试准确率达到98%。在这个方向上,MobileNetV2可以成为一种可行的方法,与基于web的应用程序集成,用于将图像分类作为决策的基础。
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引用次数: 0
Development of Sago-based Analog Rice Using Kansei and Value Engineering 利用感性和价值工程开发基于sago的模拟水稻
Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-10-03 DOI: 10.47836/pjst.31.6.17
Vioretta Putri Rizky Septiani, Mirwan Ushada, Suharno Suharno
This study used two product development methods: Kansei and value engineering. Kansei engineering was used to identify and translate consumer psychological impressions or feelings in the form of Kansei words to the design parameters, while value engineering was used to analyze the functional properties by considering cost, reliability, and performance. The consumers determined the priority attributes of analog rice products, namely a good taste, a fluffy and soft texture, as well as a bright color. Three alternative variations of the product development concept were formulated based on these priorities. The concept with the highest value was then concluded as an analog rice produced from 90% Sago flour and 10% MOCAF (Modified Cassava Flour) with a value of 1,131.
本研究采用感性与价值工程两种产品开发方法。感性工程学是将消费者的心理印象或感受以感性词汇的形式识别并转化为设计参数,而价值工程学则是通过考虑成本、可靠性和性能来分析功能特性。消费者确定了模拟米产品的优先属性,即口感好,质地蓬松柔软,颜色鲜艳。基于这些优先级,制定了产品开发概念的三种可选变体。然后得出价值最高的概念是由90%西米粉和10% MOCAF(改性木薯粉)制成的模拟大米,其价值为1131。
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引用次数: 0
Polynomial Regression Calibration Method of Total Dissolved Solids Sensor for Hydroponic Systems 水培系统总溶解固体传感器的多项式回归校准方法
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.08
Ansar Jamil, Teo Sheng Ting, Z. Zainal Abidin, Maisara Othman, Mohd Helmy Abdul Wahab, Mohammad Faiz Liew Abdullah, M. J. Homam, Lukman Hanif Muhammad Audah, Shaharil Mohd Shah
Smart hydroponic systems have been introduced to allow farmers to monitor their hydroponic system conditions anywhere and anytime using Internet of Things (IoT) technology. Several sensors are installed on the system, such as Total Dissolved Solids (TDS), nutrient level, and temperature sensors. These sensors must be calibrated to ensure correct and accurate readings. Currently, calibration of a TDS sensor is only possible at one or a very small range of TDS values due to the very limited measurement range of the sensor. Because of this, we propose a TDS sensor calibration method called Sectioned-Polynomial Regression (Sec-PR). The main aim is to extend the measurement range of the TDS sensor and still provide a good accuracy of the sensor reading. Sec-PR computes the polynomial regression line that fits into the TDS sensor values. Then, it divides the regression line into several sections. Sec-PR calculates the average ratio between the polynomial regressed TDS sensor values and the TDS meter in each section. These average ratio values map the TDS sensor reading to the TDS meter. The performance of Sec-PR was determined using mathematical analysis and verified using experiments. The finding shows that Sec-PR provides a good calibration accuracy of about 91% when compared to the uncalibrated TDS sensor reading of just 78% with Mean Average Error (MAE) and Root Mean Square Error (RMSE) equal to 59.36 and 93.69 respectively. Sec-PR provides a comparable performance with Machine Learning and Multilayer Perception method.
智能水培系统已经引入,允许农民使用物联网(IoT)技术随时随地监测他们的水培系统状况。系统上安装了多个传感器,如总溶解固体(TDS)、营养水平和温度传感器。这些传感器必须校准,以确保正确和准确的读数。目前,由于传感器的测量范围非常有限,TDS传感器只能在一个或一个非常小的TDS值范围内进行校准。因此,我们提出了一种TDS传感器标定方法,称为分段多项式回归(sectionsed - polynomial Regression,简称Sec-PR)。主要目的是扩展TDS传感器的测量范围,同时仍然提供良好的传感器读数精度。Sec-PR计算符合TDS传感器值的多项式回归线。然后,将回归线划分为几段。Sec-PR计算多项式回归的TDS传感器值与每段TDS仪表之间的平均比率。这些平均比率值将TDS传感器读数映射到TDS仪表。通过数学分析确定了Sec-PR的性能,并进行了实验验证。结果表明,与未校准TDS传感器读数仅为78%相比,Sec-PR提供了约91%的良好校准精度,平均误差(MAE)和均方根误差(RMSE)分别为59.36和93.69。Sec-PR提供了与机器学习和多层感知方法相当的性能。
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引用次数: 0
Poultry Manure and its Contribution to Inflammation and Cancer Progression 家禽粪便及其对炎症和癌症进展的贡献
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.01
Ana Masara Ahmad Mokhtar, Brennan Tang Yet Shen, Azam Muzafar Ahmad Mokhtar, N. Salikin, Muaz Mohd ZAINI MAKHTAR, Fatin Nur Izzati Mohd Fadzil, Nur Azzalia Kamaruzaman, Muggunna Balasubramaniam
Indiscriminate manure disposal has been highlighted as a significant cause of environmental contamination due to the presence of various biological and chemical irritants. It includes pathogens, antibiotics, and organic pollutants, all of which have the potential to harm not only the environment but also human health. Several incidents have been reported, most notably among farmers and those living near the farms, as a result of air and water pollution caused by manure losses. Acute and chronic exposure to these hazards may result in a variety of health issues, including infection, inflammation, and even cancer. Despite this, humans are constantly exposed to these risk agents due to a lack of awareness of proper disposal methods and knowledge of the risk agents’ associations with diseases. Thus, the review discusses the potential health risk or diseases linked to poultry manure and recommends future measures to minimise the hazards to farmers’ health and the environment posed by their existing practices.
由于存在各种生物和化学刺激物,无差别的粪便处理已被强调为环境污染的一个重要原因。它包括病原体、抗生素和有机污染物,所有这些都有可能危害环境,也会危害人体健康。据报道,由于粪便损失造成的空气和水污染,发生了几起事件,其中最明显的是农民和农场附近的居民。急性和慢性暴露于这些危害可能导致各种健康问题,包括感染、炎症,甚至癌症。尽管如此,由于缺乏对适当处置方法的认识和对风险物剂与疾病的关系的了解,人类仍不断接触这些风险物剂。因此,该审查讨论了与禽粪有关的潜在健康风险或疾病,并建议未来采取措施,尽量减少现有做法对农民健康和环境造成的危害。
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引用次数: 0
Effect of Lysine and Poultry Slaughterhouse by Product Meal on Growth Performance, Feed Efficiency, and Blood Profile of Sangkuriang Catfish (Clarias gariepinus var. Sangkuriang) 赖氨酸和家禽屠宰场副产品粕对桑库里良鲇鱼生长性能、饲料效率和血液特征的影响
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.07
D. Rachmawati, T. Elfitasari, I. Samidjan, Putut Har Riyadi, Dewi Nurhayati
The increasing demand for livestock and poultry feeds results in the lack of fish meals (FM). Poultry slaughterhouse by-product (PSB) is one promising strategy due to its high protein content despite the limited content of lysine. Thus, supplementing lysine in dietary fish feed is necessary. The present study aimed to investigate how different lysine doses in feed with PSB and FM as animal protein sources affected protein digestibility, feed utilization, growth, hematology, and body composition of Sangkuriang catfish (Clarias gariepinus var. Sangkuriang). Sangkuriang catfish at the grow-out stage (15.54±0.17 g/fish) were used. The fish were fed six experimental diets with similar protein and energy content but different lysine levels at 1.25%, 1.75%, 2.25%, 2.75%, 3.25%, and 3.75%/kg (treatments 1 to 6). The addition of lysine to feed had a significant (P<0.05) effect on protein digestibility (ADCp), efficiency of feed utilization (EFU), and relative growth rate (RGR) of Sangkuriang catfish at a grow-out stage but had no significant (P>0.05) effect on survival rate, hematology, and nutrient content. The optimal doses of dietary lysine with PSB and FM to improve ADCp, EFU, and RGR of Sangkuriang catfish were 2.59%, 2.63%, and 2.62%/kg diet, respectively. However, the supplementation of PSB in experimental diets had no significant effect on glucose, triglyceride, total protein, urea, calcium, magnesium, albumin, globulin, hemoglobin, hematocrit, phosphorous, and mean corpuscular hemoglobin concentration (MCHC). The lysine addition in feed formulated with PSB and FM could improve the growth performance and increase the feed digestibility of Sangkuriang catfish at the grow-out stage.
牲畜和家禽饲料需求的增加导致鱼粉(FM)的缺乏。家禽屠宰场副产品(PSB)由于其蛋白质含量高,尽管赖氨酸含量有限,是一种有前途的策略。因此,在鱼饲料中添加赖氨酸是必要的。本研究旨在探讨以PSB和FM为动物蛋白质源的饲料中不同赖氨酸剂量对桑库里良鲶鱼蛋白质消化率、饲料利用率、生长、血液学和体组成的影响。选用生长期桑古良鲶鱼(15.54±0.17 g/条)。饲喂蛋白质和能量含量相近,赖氨酸水平分别为1.25%、1.75%、2.25%、2.75%、3.25%和3.75%/kg的6种试验饲料(处理1 ~ 6)。饲料中添加赖氨酸对鱼的成活率、血液学指标和营养成分含量有显著(P0.05)的影响。饲粮中添加PSB和FM能提高桑库良鲶鱼ADCp、EFU和RGR的最佳剂量分别为2.59%、2.63%和2.62%/kg。然而,在试验饲粮中添加PSB对葡萄糖、甘油三酯、总蛋白、尿素、钙、镁、白蛋白、球蛋白、血红蛋白、红细胞压积、磷和平均红细胞血红蛋白浓度(MCHC)无显著影响。在PSB和鱼粉配制的饲料中添加赖氨酸可以改善桑库里梁鲶鱼生长性能,提高其饲料消化率。
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引用次数: 0
Integrating Ice Protection and Noise Abatement Systems for Aircraft Application: A Review 集成防冰降噪系统在飞机上的应用综述
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.02
F. R. Munas, Yu Kok Hwa, Norwahida Yusoff, Abdul Majeed Muzathik, Mohd Azmi Ismail
Aircraft icing remains a key aviation hazard as the global fleet of aircraft in various sectors continues to expand, posing a serious threat to flight safety. As previously stated, the growth of this type of aircraft has been accompanied by an increase in noise levels, and aircraft is reportedly the second most bothersome noise source after traffic. However, integrating an acoustic liner with anti-icing techniques on the leading edge of a nacelle would not efficiently eliminate forward radiated noise and improve the thermal performance of the anti-icing system. Hence, it is of the utmost importance to research the integration of ice protection and noise abatement systems for aircraft applications. This review discusses the integration of ice accretion and noise abatement systems in aircraft applications. The prominence of this review is to explain significant features such as ice protection systems, Computational Fluid Dynamics in ice protection, noise abatement systems, and the integration of ice protection systems and noise abatement systems wherever they are described.
随着全球各个领域的飞机不断扩大,飞机结冰仍然是一个主要的航空危害,对飞行安全构成严重威胁。如前所述,这类飞机的增长伴随着噪音水平的提高,据报道,飞机是仅次于交通的第二大噪声源。然而,在机舱前缘集成声学衬垫和防冰技术并不能有效消除前向辐射噪声,也不能改善防冰系统的热性能。因此,研究飞机防冰降噪系统的集成是至关重要的。本文综述了飞机增冰降噪系统的综合应用。这篇综述的重点是解释重要的特征,如冰保护系统,冰保护中的计算流体动力学,降噪系统,以及冰保护系统和降噪系统的集成,无论它们在哪里被描述。
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引用次数: 0
Magnetic Susceptibility and Hydrogen Cyanide Levels as Proxy Indicator for Gold Mining Pollution in River Sediment 磁化率和氰化氢水平作为河流沉积物金矿开采污染的替代指标
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.03
Siti Zulaikah, Arif Juliansyah, Muhammad Fathur Rouf Hasan, Bambang Heru Iswanto, M. Mariyanto, Ardyanto Tanjung, S. Bijaksana, Ann Marie Hirt
Sumbawa’s Kuris River is one of the rivers contaminated by the island’s traditional gold mine. In order to detect contaminant levels, we examine the magnetic susceptibility, HCN levels, and the heavy metal contents on the river’s surface sediment. Environmental pollution has been widely assessed using a combination of magnetic properties and geochemical analysis. The goals of this research are to discover how magnetic susceptibility (χ) can be used as a first-order proxy for pollution. The relation between susceptibility and HCN is of particular interest, as this is a major contaminant associated with gold mining. The surface sediment samples were collected at ten different locations along the rivers. The magnetic susceptibility was determined using the Bartington MS2B, and the hydrogen cyanide (HCN) concentration was determined using Argentometric titration. The element content was determined by an Atomic Absorption Spectrometer (AAS). The low-frequency magnetic susceptibility (χlf) ranges from 71 to 115×10-8 m3/kg, with an average of 97×10-8 m3/kg, and the χfd(%) analysis ranges from 2% to 4%. The presence of spherical iron oxides, which are indicative of combustion byproducts, was also confirmed by SEM. The samples have low magnetic susceptibility but high levels of Hg and HCN. AAS results showed high Fe, Zn, and Cu concentrations in river sediments, with more variable concentrations of Hg, Mn, As, Cr, and Au. Because Fe, Cu, As, Hg, and HCN have a significant Pearson’s correlation with χfd(%), this parameter can be a useful indicator for contamination caused by gold mining waste.
松巴哇的库里斯河是被岛上传统金矿污染的河流之一。为了检测污染物水平,我们检查了磁化率、HCN水平和河流表面沉积物的重金属含量。环境污染已广泛使用磁性和地球化学分析相结合的方法进行评估。本研究的目标是发现磁化率(χ)如何可用作污染的一阶代理。易感性与HCN之间的关系特别令人感兴趣,因为这是与金矿开采有关的主要污染物。地表沉积物样本是在河流沿线的十个不同地点收集的。磁化率采用巴丁顿MS2B测定,氰化氢(HCN)浓度采用阿根廷滴定法测定。用原子吸收光谱仪(AAS)测定了元素的含量。低频磁化率(χ f)范围为71 ~ 115×10-8 m3/kg,平均值为97×10-8 m3/kg, χf(%)分析范围为2% ~ 4%。通过扫描电镜也证实了球形氧化铁的存在,这是燃烧副产物的指示。样品磁化率低,但Hg和HCN含量高。原子吸收光谱结果显示,河流沉积物中铁、锌和铜的浓度较高,Hg、Mn、As、Cr和Au的浓度变化较大。由于Fe, Cu, As, Hg和HCN与χfd(%)具有显著的Pearson相关,因此该参数可以作为金矿开采废物污染的有用指标。
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引用次数: 0
Effect of Scaling the Electrostatic Interactions on the Free Energy of Transfer of Azurin from Water to Lipid Membrane Determined by Coarse-Grained Simulations 通过粗粒度模拟确定静电相互作用对蓝蛋白从水到脂质膜转移自由能的影响
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.06
D. Fitrasari, A. Purqon, S. Suprijadi
Azurin protein potentially plays an important role as an anti-cancer therapeutic agent, particularly in treating breast cancer in experiments and showing without having a negative effect on normal cells. Although the interaction mechanism between protein and lipid membrane is complicated, it can be modeled as protein-lipid interaction. Since the all-atom (AA) model simulation is cost computing, we apply a coarse-grained (CG-MARTINI) model to calculate the protein-lipid interaction. We investigate the binding free energy value dependency by varying the windows separation and electrostatic scale parameters. After scaling the electrostatic interactions by a factor of 0.04, the best result in terms of free energy is -140.831 kcal/mol, while after window-separation optimization, it reaches -71.859 kcal/mol. This scaling was necessary because the structures from the CG MARTINI model have a higher density than the corresponding all-atom structures. We thus postulate that electrostatic interactions should be scaled down in this case of CG-MARTINI simulations.
Azurin蛋白作为一种抗癌治疗剂具有潜在的重要作用,特别是在治疗乳腺癌的实验中,并且显示对正常细胞没有负面影响。虽然蛋白与脂质膜的相互作用机制复杂,但可以用蛋白-脂质相互作用来建模。由于全原子(AA)模型模拟是成本计算,我们采用粗粒度(CG-MARTINI)模型来计算蛋白质-脂质相互作用。我们通过改变窗距和静电尺度参数来研究束缚自由能值的依赖关系。将静电相互作用按0.04的因子进行缩放后,自由能的最佳结果为-140.831 kcal/mol,而窗分离优化后的自由能达到-71.859 kcal/mol。这种缩放是必要的,因为CG MARTINI模型的结构比相应的全原子结构具有更高的密度。因此,我们假设在CG-MARTINI模拟的这种情况下,静电相互作用应该按比例缩小。
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引用次数: 0
Recognition of Fruit Types from Striking and Flicking Sounds 从敲击和轻弹声中识别水果类型
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.04
Rong Phoophuangpairoj
This paper proposes a method to recognize fruits whose quality, including their ripeness, grades, brix values, and flesh characteristics, cannot be determined visually from their skin but from striking and flicking sounds. Four fruit types consisting of durians, watermelons, guavas, and pineapples were studied in this research. In recognition of fruit types, preprocessing removes the non-striking/non-flicking parts from the striking and flicking sounds. Then the sequences of frequency domain acoustic features containing 13 Mel Frequency Cepstral Coefficients (MFCCs) and their 13 first- and 13 second-order derivatives were extracted from striking and flicking sounds. The sequences were used to create the Hidden Markov Models (HMMs). The HMM acoustic models, dictionary, and grammar were incorporated to recognize striking and flicking sounds. When testing the striking and flicking sounds obtained from the fruits used to create the training set but were collected at different times, the recognition accuracy using 1 through 5 strikes/flicks was 98.48%, 98.91%, 99.13%, 98.91%, and 99.57%, respectively. For an unknown test set, of which the sounds obtained from the fruits that were not used to create the training set, the recognition accuracy using 1 through 5 strikes/flicks were 95.23%, 96.82%, 96.82%, 97.05%, and 96.59%, respectively. The results also revealed that the proposed method could accurately distinguish the striking sounds of durians from the flicking sounds of watermelons, guavas, and pineapples.
本文提出了一种识别水果质量的方法,包括成熟度、等级、糖度值和果肉特征,这些质量不能从外观上确定,而是从撞击和弹跳的声音中确定。以榴莲、西瓜、番石榴、菠萝四种水果为研究对象。在识别水果类型时,预处理从敲击和轻弹声音中去除非敲击/非轻弹部分。然后从敲击声和轻弹声中提取包含13个Mel倒频系数(MFCCs)及其13个一阶导数和13个二阶导数的频域声学特征序列。这些序列被用来创建隐马尔可夫模型(hmm)。HMM声学模型、词典和语法被结合起来识别敲击和轻弹的声音。对不同时间采集的用于创建训练集的水果击打和弹击声音进行测试,1 ~ 5次击打/弹击的识别准确率分别为98.48%、98.91%、99.13%、98.91%和99.57%。对于未知测试集,其中未用于创建训练集的水果声音,使用1到5次击打/轻击的识别准确率分别为95.23%,96.82%,96.82%,97.05%和96.59%。结果还表明,该方法可以准确区分榴莲的敲击声和西瓜、番石榴、菠萝的轻弹声。
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引用次数: 0
Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data 基于机器学习算法的智能系统在检测海藻含水率去除参数中的大数据
IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pub Date : 2023-09-08 DOI: 10.47836/pjst.31.6.09
Olayemi Joshua Ibidoja, Fam Pei Shan, Mukhtar Eri Suheri, Jumat Sulaiman, Majid Khan Majahar Ali
The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.
决定海藻含水率去除的参数在海藻研究中是必要的,因为它们可以降低成本,提高海藻的质量和数量。在海藻的干燥过程中,涉及到许多干燥参数,因此很难找到一个可以确定干燥参数的模型。本研究使用机器学习算法比较海藻的大数据性能。为了实现这一目标,使用了四种机器学习算法,如bagging、boosting、support vector machine和random forest,从v-GHSD (v-Groove Hybrid Solar dry)获得的数据中确定重要参数。采用平均绝对百分比误差(MAPE)和决定系数(R2)对模型进行评价。在大数据中,由于大量的变量和参数超过了观测值的数量,因此变量选择的重要性再怎么强调也不为过。它可以降低模型的复杂性,避免维数的困扰,降低成本,去除不相关的变量,提高精度。共有435个干燥参数决定了含水率的去除,每个算法分别选择15、25、35和45个显著参数。随机森林45个最高变量重要性的MAPE和R-Square分别为2.13和0.9732。它表现最好,误差最小,r平方最高。这些结果表明,随机森林是确定去除水分的关键干燥参数的最佳算法。
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引用次数: 0
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