Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

Abstract Objective: The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Methods: 69 patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales (Kappa and Intraclass Correlation Coefficients), and reliability (Cronbach’s alpha and Guttman’s coefficient). We calculated the Comparative Fit Index and the Root Mean Squared Error associated with the proposed one-factor structure. Results: Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the Intraclass Correlation Coefficient for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F= 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and Root Mean Squared Error was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. Conclusion: The digital DARS is consistent in many respects to the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

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Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

Agrokémia és Talajtan ◽

10.1556/agrokem.60.2012.2.5 ◽

2012 ◽

Vol 61 (2) ◽

pp. 277-290 ◽

Cited By ~ 1

Author(s):

Ádám Csorba ◽

Vince Láng ◽

László Fenyvesi ◽

Erika Michéli

Keyword(s):

Organic Carbon ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Mean Squared Error ◽

Reflectance Spectroscopy ◽

Least Squares Regression ◽

Root Mean Squared Error ◽

Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

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Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

Jurnal Edukasi dan Penelitian Informatika (JEPIN) ◽

10.26418/jp.v4i1.25384 ◽

2018 ◽

Vol 4 (1) ◽

pp. 24

Author(s):

Imam Halimi ◽

Wahyu Andhyka Kusuma

Keyword(s):

Neural Network ◽

Data Mining ◽

Linear Regression ◽

Mean Squared Error ◽

Composite Index ◽

T Test ◽

Sliding Windows ◽

Root Mean Squared Error ◽

Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

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Two-Run Genetic Programming for Predicting Slump Flow of Concrete

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.590.321 ◽

2014 ◽

Vol 590 ◽

pp. 321-325

Author(s):

Li Chen ◽

Chang Huan Kou ◽

Kuan Ting Chen ◽

Shih Wei Ma

Keyword(s):

Nonlinear Systems ◽

Genetic Programming ◽

High Performance ◽

Mean Squared Error ◽

High Performance Concrete ◽

Local Optimum ◽

Root Mean Squared Error ◽

Squared Error ◽

Slump Flow ◽

Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

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Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America

Remote Sensing ◽

10.3390/rs12183098 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3098

Author(s):

Jongmin Park ◽

Barton A. Forman ◽

Rolf H. Reichle ◽

Gabrielle De Lannoy ◽

Saad B. Tarik

Keyword(s):

North America ◽

Brightness Temperature ◽

Mean Squared Error ◽

Transfer Model ◽

Vegetation Types ◽

Large Variability ◽

Root Mean Squared Error ◽

Squared Error ◽

L Band ◽

Soil Hydraulic

L-band brightness temperature (Tb) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in Tb observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, Tb estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily Tb observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed Tb were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed Tb with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled Tb due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

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Combined Multilateration with Machine Learning for Enhanced Aircraft Localization

Proceedings ◽

10.3390/proceedings2020059002 ◽

2020 ◽

Vol 59 (1) ◽

pp. 2

Author(s):

Benoit Figuet ◽

Raphael Monstein ◽

Michael Felux

Keyword(s):

Machine Learning ◽

Sensitivity Analysis ◽

Real World ◽

Mean Squared Error ◽

Accurate Estimate ◽

Regression Technique ◽

Gradient Boosting ◽

Root Mean Squared Error ◽

Squared Error ◽

Using Data

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.

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Forecasting Oil Price Using Web-based Sentiment Analysis

Energies ◽

10.3390/en12224291 ◽

2019 ◽

Vol 12 (22) ◽

pp. 4291 ◽

Cited By ~ 2

Author(s):

Lu-Tao Zhao ◽

Guan-Rong Zeng ◽

Wen-Jing Wang ◽

Zhi-Gang Zhang

Keyword(s):

Sentiment Analysis ◽

Mean Squared Error ◽

Oil Price ◽

Future Research ◽

Price Forecasting ◽

Web Based ◽

Market Sentiment ◽

Root Mean Squared Error ◽

Strong Intensity ◽

Squared Error

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.

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Cross-Validation of Field-Based Assessments of Body Composition for Individuals with Down Syndrome

Adapted Physical Activity Quarterly ◽

10.1123/apaq.22.2.198 ◽

2005 ◽

Vol 22 (2) ◽

pp. 198-206 ◽

Cited By ~ 11

Author(s):

Phillip C. Usera ◽

John T. Foley ◽

Joonkoo Yun

Keyword(s):

Body Composition ◽

Down Syndrome ◽

Body Fat ◽

Cross Validation ◽

Mean Squared Error ◽

The Body ◽

Anthropometric Measurements ◽

Prediction Equations ◽

Root Mean Squared Error ◽

Squared Error

The purpose of this study was to cross-validate skinfold and anthropometric measurements for individuals with Down syndrome (DS). Estimated body fat of 14 individuals with DS and 13 individuals without DS was compared between criterion measurement (BOP POD®) and three prediction equations. Correlations between criterion and field-based tests for non-DS group and DS groups ranged from .81 – .94 and .11 – .54, respectively. Root-Mean-Squared-Error was employed to examine the amount of error on the field-based measurements. A MANOVA indicated significant differences in accuracy between groups for Jackson’s equation and Lohman’s equation. Based on the results, efforts should now be directed toward developing new equations that can assess the body composition of individuals with DS in a clinically feasible way.

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Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering

Jurnal Informasi & Teknologi ◽

10.37034/jidt.v3i4.151 ◽

2021 ◽

pp. 202-208

Author(s):

Daniel Theodorus ◽

Sarjon Defit ◽

Gunadi Widi Nurcahyo

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

User Experience ◽

Mean Squared Error ◽

Cosine Similarity ◽

Weighted Sum ◽

Root Mean Squared Error ◽

Squared Error

Industri 4.0 mendorong banyak perusahaan bertransformasi ke sistem digital. Machine Learning merupakan salah satu solusi dalam analisa data. Analisa data menjadi poin penting dalam memberikan layanan yang terbaik (user experience) kepada pelanggan. Lokasi yang diangkat dalam penelitian ini adalah PT. Sentral Tukang Indonesia yang bergerak dalam bidang penjualan bahan bangunan dan alat pertukangan seperti: cat, tripleks, aluminium, keramik, dan hpl. Dengan banyaknya data yang tersedia, menyebabkan perusahaan mengalami kesulitan dalam memberikan rekomendasi produk kepada pelanggan. Sistem rekomendasi muncul sebagai solusi dalam memberikan rekomendasi produk, berdasarkan interaksi antara pelanggan dengan pelanggan lainnya yang terdapat di dalam data histori penjualan. Tujuan dari penelitian ini adalah Membantu perusahaan dalam memberikan rekomendasi produk sehingga dapat meningkatkan penjualan, memudahkan pelanggan untuk menemukan produk yang dibutuhkan, dan meningkatkan layanan yang terbaik kepada pelanggan.Data yang digunakan adalah data histori penjualan dalam 1 periode (Q1 2021), data pelanggan, dan data produk pada PT. Sentral Tukang Indonesia. Data histori penjualan tersebut akan dibagi menjadi 80% untuk dataset training dan 20% untuk dataset testing. Metode Item-based Collaborative Filtering pada penelitian ini memakai algoritma Cosine Similarity untuk menghitung tingkat kemiripan antar produk. Prediksi score memakai rumus Weighted Sum dan dalam menghitung tingkat error memakai rumus Root Mean Squared Error. Hasil dari penelitian ini memperlihatkan rekomendasi top 10 produk per pelanggan. Produk yang tampil merupakan produk yang memiliki score tertinggi dari pelanggan tersebut. Penelitian ini dapat menjadi referensi dan acuan bagi perusahaan dalam memberikan rekomendasi produk yang dibutuhkan oleh pelanggan.

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Correcting the Bias of the Root Mean Squared Error of Approximation under Missing Data

10.31234/osf.io/8etxa ◽

2018 ◽

Author(s):

Cailey Elizabeth Fitzgerald ◽

Ryne Estabrook ◽

Daniel Patrick Martin ◽

Andreas Markus Brandmaier ◽

Timo von Oertzen

Keyword(s):

Missing Data ◽

Structural Equation ◽

Goodness Of Fit ◽

Mean Squared Error ◽

Equation Modeling ◽

Fit Indices ◽

Root Mean Squared Error ◽

Squared Error ◽

Acceptable Model ◽

Downward Bias

Missing data are ubiquitous in both small and large datasets. Missing data may come about as a result of coding or computer error, participant absences, or it may be intentional, as in planned missing designs. We discuss missing data as it relates to goodness-of-fit indices in Structural Equation Modeling (SEM), specifically the effects of missing data on the Root Mean Squared Error of Approximation (RMSEA). We use simulations to show that naive implementations of the RMSEA have a downward bias in the presence of missing data and, thus, overestimate model goodness-of-fit. Unfortunately, many state-of-the-art software packages report the biased form of RMSEA. As a consequence, the community may have been accepting a much larger fraction of models with non-acceptable model fit. We propose a bias-correction for the RMSEA based on information-theoretic considerations that take into account the expected misfit of a person with fully observed data. This results in an RMSEA which is asymptotically independent of the proportion of missing data for misspecified models. Importantly, results of the corrected RMSEA computation are identical to naive RMSEA if there are no missing data.

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PEMODELAN SEBARAN SEDIMEN TERSUSPENSI DAMPAK PENAMBANGAN TIMAH DI PERAIRAN BANGKA

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.29244/jitkt.v12i2.27875 ◽

2020 ◽

Vol 12 (2) ◽

pp. 353-368

Author(s):

Aditya Pamungkas ◽

Semeidi Husrin

Keyword(s):

Flow Model ◽

Mean Squared Error ◽

Total Suspended Solid ◽

Suspended Solid ◽

Root Mean Squared Error ◽

Squared Error ◽

Mud Transport

Pulau Bangka merupakan penambangan timah lepas pantai terbesar di Indonesia yang dilakukan oleh perusahan dan masyarakat, baik legal maupun ilegal. Hal ini menjadi mengkhawatirkan terutama akan dampaknya terhadap peningkatan sedimentasi, seperti wilayah Teluk Kelabat yang direncanakan menjadi kawasan konservasi. Tujuan penelitian ini adalah mengetahui kondisi hidro-oseanografi dan sebaran Total Suspended Solid (TSS) akibat penambangan timah di perairan Bangka terutama di Teluk Kelabat. Metode yang digunakan adalah analisis pemodelan numerik dengan menggunakan software MIKE21 untuk memperoleh data hidro-oseanografi dan model sebaran TSS dengan sumber TSS dari tiap-tiap lokasi Izin Usaha Pertambangan (IUP) melalui modul Flow Model Flexibel Mesh (FM) dan Mud Transport (MT). Verifikasi hasil model diperoleh korelasi sebesar 0,9435 dengan Root Mean Squared Error (RMSE) sebesar 0,1611 untuk pasang-surut. Tailing penambangan timah lepas pantai akan menyebabkan tingginya sebaran TSS di perairan Bangka, terutama aktivitas penambangan di perairan yang dangkal (<10 m) dan dekat pesisir (<2 mil). Sebaran TSS di perairan Bangka akan dominan terbawa ke arah Selat Bangka. Pada Teluk Kelabat, TSS bernilai sekitar 0-25 mg/L dan menyebar ke seluruh wilayah yang dapat mencapai radius sekitar 16 mil. Hasil penelitian ini diharapkan dapat menjadi rekomendasi bagi stakeholder seperti penyusunan Rencana Zonasi Pesisir Dan Pulau-Pulau Kecil (RZWP3K) di Bangka Belitung.

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