Modeling of the COVID-19 Cases in Gulf Cooperation Council Countries Using ARIMA and MA-ARIMA Models

Coronavirus disease 2019 (COVID-19) is still a great pandemic presently spreading all around the world. In Gulf Cooperation Council (GCC) countries, there were 1015269 COVID-19 confirmed cases, 969424 recovery cases, and 9328 deaths as of 30 Nov. 2020. This paper, therefore, subjected the daily reported COVID-19 cases of these three variables to some statistical models including classical ARIMA, kth SMA-ARIMA, kth WMA-ARIMA, and kth EWMA-ARIMA to study the trend and to provide the long-term forecasting of the confirmed, recovery, and death cases of the novel COVID-19 pandemic in the GCC countries. The data analyzed in this study covered the period starting from the first case of coronavirus reported in each GCC country to Jan 31, 2021. To compute the best parameter estimates, each model was fitted for 90% of the available data in each country, which is called the in-sample forecast or training data, and the remaining 10% was used for the out-of-sample forecast or testing data. The AIC was applied to the training data as a criterion method to select the best model. Furthermore, the statistical measure RMSE and MAPE were utilized for testing data, and the model with the minimum RMSE and MAPE was selected for future forecasting. The main finding, in general, is that the two models WMA-ARIMA and EWMA-ARIMA, besides the cubic and 4th degree polynomial regression, have given better results for in-sample and out-of-sample forecasts than the classical ARIMA models in fitting the confirmed and recovery cases while SMA-ARIMA and WMA-ARIMA were suitable to model the recovery and death cases in the GCC countries.

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Modeling of the COVID-19 Cases in Gulf Cooperation Council (GCC) countries using ARIMA and MA-ARIMA models.

10.1101/2021.05.27.21257916 ◽

2021 ◽

Author(s):

Hussein Eledum ◽

Rahamtalla Yagoub

Keyword(s):

Gulf Cooperation Council ◽

Training Data ◽

Parameter Estimates ◽

Arima Models ◽

Gcc Countries ◽

First Case ◽

Out Of Sample ◽

Long Term Forecasting ◽

Best Parameter ◽

Death Cases

Coronavirus disease 2019 (COVID-19) is still a great pandemic presently spreading all around the world. In Gulf Cooperation Council (GCC) countries, there were 1015269 COVID-19 confirmed cases, 969424 recovery cases, and 9328 deaths as of 30th Nov. 2020. This paper, therefore, subjected the daily reported COVID-19 cases of these three variables to some statistical models including classical ARIMA, kth SMA-ARIMA, kth WMA-ARIMA, and kth EWMA-ARIMA to study the trend and to provide the long-term forecasting of the confirmed, recovery, and death cases of the novel COVID-19 pandemic in the GCC countries. The data analyzed in this study covered the period starting from the first case of coronavirus reported in each GCC country to Nov 30, 2020. To compute the best parameter estimates, each model was fitted for 90% of the available data in each country, which is called the in-sample forecast or training data, and the remaining 10% was used for the out-of-sample forecast or testing model. The AIC was applied to the training data as a criterion method to select the best model. Furthermore, the statistical measure RMSE was utilized for testing data, and the model with the minimum AIC and minimum RMSE was selected. The main finding, in general, is that the two models WMA-ARIMA and EWMA-ARIMA, besides the cubic linear regression model have given better results for in-sample and out-of-sample forecasts than the classical ARIMA models in fitting the confirmed and recovery cases while the death cases haven't specific models.

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An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

ASME 2008 Noise Control and Acoustics Division Conference ◽

10.1115/ncad2008-73046 ◽

2008 ◽

Cited By ~ 1

Author(s):

Brian Bucci ◽

Jeffrey Vipperman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Bayesian Methods ◽

False Positive ◽

Impulse Noise ◽

False Negative ◽

Training Data ◽

Maximum Accuracy ◽

First Case ◽

Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

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A Comparison Between Backpropagation, Holt-Winter, and Polynomial Regression Methods in Forecasting Dog Bites Cases in Bali

Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) ◽

10.24843/jim.2021.v09.i03.p06 ◽

2021 ◽

Vol 9 (3) ◽

pp. 251

Author(s):

Gede Eridya Bayu ◽

I Ketut Gede Darma Putra ◽

Ni Kadek Dwi Rusjayanthi

Keyword(s):

Data Mining ◽

Error Rate ◽

Polynomial Regression ◽

Training Data ◽

Preventive Action ◽

Dog Bite ◽

Dog Bites ◽

Regression Methods ◽

Testing Data ◽

Case Data

Rabies is a zoonotic disease that is usually transmitted to humans through animal bites. It can cause severe damage to the central nervous system and is generally fatal. Dog bite cases are considered the leading cause of rabies transmission in Bali. The government's preventive action is expected to reduce the problem of increasing the number of dog bite cases so that it does not spread quickly and cause casualties. Data mining is an attempt to extract knowledge from a set of data. The use of data mining in this study is to forecast the number of dog bite cases in Bali. Forecasting predicts what will happen in the future based on relevant data in the past and placing it in a mathematical model. Data mining methods used in forecasting dog bite cases are backpropagation, holt-winters, polynomial regression methods. This forecasting aims to help the government predict dog bite cases in the coming period to prepare appropriate countermeasures. Forecasting is done using data on bite cases every month in Bali province for five years, from 2015 to 2019. Dog bite case data is divided into four datasets for each attribute, namely data on the number of dog bite cases, the number of vaccinations, the number of male deaths, the number of female deaths. The four datasets are divided into training data and testing data to share 80% training data and 20% testing data. The results obtained are that the backpropagation method is better at predicting dog bite case data with an average MAPE error rate of 11.59%, while the holt-winters method has an average MAPE error rate of 16.05%, and the polynomial regression method has an average MAPE error rate of 19.91% Keywords : Dog Bites, Rabies, Forecasting, Backpropagation, Holt-Winter, Polynomial Regression

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An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666201207154641 ◽

2020 ◽

Vol 13 ◽

Author(s):

Jianfeng Jiang

Keyword(s):

Fault Diagnosis ◽

Fractal Analysis ◽

Analog Circuit ◽

Training Data ◽

Support Vector ◽

Pass Filter ◽

Multiple Kernel ◽

Testing Data ◽

Circuit Fault Diagnosis ◽

Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

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Challenges and Prospects of Using Treated Wastewater to Manage Water Scarcity Crises in the Gulf Cooperation Council (GCC) Countries

Water ◽

10.3390/w12071971 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1971

Author(s):

Asad Sarwar Qureshi

Keyword(s):

Sustainable Use ◽

Integrated Approach ◽

Gulf Cooperation Council ◽

Treated Wastewater ◽

Regional Security ◽

Forage Crops ◽

Effective Response ◽

Groundwater Levels ◽

Gcc Countries ◽

The World

The Gulf Cooperation Council (GCC) countries are located in the driest part of the world with an annual per capita water availability of 500 m3 compared to the world average of 6000 m3. Agricultural water demand, which is more than 80% of the total water consumption, is primarily met through the massive exploitation of groundwater. The enormous imbalance between groundwater discharge (27.8 billion m3) and recharge (5.3 billion m3) is causing the excessive lowering of groundwater levels. Therefore, GCC countries are investing heavily in the production of nonconventional water resources such as desalination of seawater and treated wastewater. Currently, 439 desalination plants are annually producing 5.75 billion m3 of desalinated water in the GCC countries. The annual wastewater collection is about 4.0 billion m3, of which 73% is treated with the help of 300 wastewater treatment plants. Despite extreme water poverty, only 39% of the treated wastewater is reused, and the remaining is discharged into the sea. The treated wastewater (TWW) is used for the landscape, forestry, and construction industries. However, its reuse to irrigate food and forage crops is restricted due to health, social, religious, and environmental concerns. Substantial research evidence exists that treated wastewater can safely be used to grow food and forage crops under the agroclimatic conditions of the GCC countries by adopting appropriate management measures. Therefore, GCC countries should work on increasing the use of TWW in the agriculture sector. Increased use of TWW in agriculture can significantly reduce the pressure on freshwater resources. For this purpose, a comprehensive awareness campaign needs to be initiated to address the social and religious concerns of farming communities and consumers. Several internal and external risks can jeopardize the sustainable use of treated wastewater in the GCC countries. These include climate change, increasing costs, technological and market-driven changes, and regional security issues. Therefore, effective response mechanisms should be developed to mitigate future risks and threats. For this purpose, an integrated approach involving all concerned local and regional stakeholders needs to be adopted.

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Bridging the digital divide through ICT

Information Development ◽

10.1177/0266666913489987 ◽

2013 ◽

Vol 30 (4) ◽

pp. 358-365 ◽

Cited By ~ 5

Author(s):

Adboulaye Kaba ◽

Raed Said

Keyword(s):

Economic Growth ◽

Information Technology ◽

Digital Divide ◽

Significant Relationship ◽

Gulf Cooperation Council ◽

Arab Countries ◽

Government Support ◽

Global Information ◽

Gcc Countries ◽

Ict Infrastructure

Bridging the gap of the digital divide can play an important role in education, employment and economic growth of any country. The present study attempts to examine and analyze the digital divide status of the Gulf Cooperation Council (GCC) countries compared with countries of the Association of Southeast Asian Nations (ASEAN) and other Arab countries. It uses 19 indicators of four factors adapted from The Global Information Technology Report 2009–2010 to measure the digital divide. Findings of the study indicated that GCC countries have a better ICT infrastructure than the ASEAN and other Arab countries. Similarly, the results of the study revealed that GCC nations have more ICT users than the ASEAN and other Arab countries. However, the study found no significant differences among these groups of countries in regard to government support and usage of ICT. Findings of the Analysis of Variance (ANOVA) show that, across the three groups of countries, the influence of ICT infrastructure is consistently significant in narrowing the digital divide. The regression results also prove a significant relationship between government support for ICT and government usage of ICT.

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Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.2042 ◽

2011 ◽

Vol 189-193 ◽

pp. 2042-2045 ◽

Cited By ~ 1

Author(s):

Shang Jen Chuang ◽

Chiung Hsing Chen ◽

Chien Chih Kao ◽

Fang Tsung Liu

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Gaussian Distribution ◽

Test Pattern ◽

Recognition Rate ◽

Hopfield Neural Network ◽

Training Data ◽

New Method ◽

Gaussian Filter ◽

Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

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Board gender diversity and firms' social engagement in the Gulf Cooperation Council (GCC) countries

Equality Diversity and Inclusion An International Journal ◽

10.1108/edi-02-2021-0041 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mohammad Jizi ◽

Rabih Nehme ◽

Cynthia Melhem

Keyword(s):

Social Engagement ◽

Generalized Least Squares ◽

Governance Structure ◽

Gulf Cooperation Council ◽

Foreign Direct Investments ◽

Corporate Boards ◽

Female Representation ◽

Content Type ◽

Gcc Countries ◽

Thomson Reuters

PurposeThe Gulf Cooperation Council (GCC) countries form a unique socioeconomic environment that makes the conclusions of the prior literature not likely to be applicable. GCC countries have huge oil reserves, yet they are aiming at reducing oil dependency through enhancing transparency, increasing foreign direct investments and reforming their governance structure. Their firms are mainly family owned and have low female representation in leadership positions. The study seeks to fill a literature gap by providing a business case supporting the call for gender diverse boards for better governance.Design/methodology/approachThe study examines a sample of GCC-listed firms for the years 2009–2018. Three measures are used to proxy for firm social engagement, namely, CSR strategy score, environmental, social and governance (ESG) disclosure score and social pillar score. To ensure whether the presence of women on board or the number of women on board is influential on social engagements, the authors use the existence of women on board and the percentage of women on board variables. Data are collected using Thomson Reuters, and generalized least squares (GLS) panel data regression is used to estimate relationships.FindingsThe authors find that female representation on GCC corporate boards is increasing, yet in a slow path. The reported results support the role of women on boards in prompting firms' social agenda and enhancing the level of sustainability reporting. The results also show that female board representation supports the implementation of climate change policy, business ethics policy and health and safety policy.Originality/valueThe paper evidence the add value of women participation on GCC corporate boards in enhancing boards' functionality and governance. The empirical findings encourage firms and policymakers in the GCC countries to increase the share of females on corporate boards to improve firms' citizenship and facilitate attracting foreign investors.

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Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

Journal of Applied Electrical Engineering ◽

10.30871/jaee.v4i2.1300 ◽

2020 ◽

Vol 4 (2) ◽

pp. 24-29

Author(s):

Adlian Jefiza ◽

Indra Daulay ◽

Jhon Hericson Purba

Keyword(s):

Real Time ◽

Root Mean Square ◽

Nearest Neighbors ◽

Training Data ◽

Mean Square ◽

K Nearest Neighbors ◽

Testing Data ◽

Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

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Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021854719 ◽

2021 ◽

Vol 8 (5) ◽

pp. 929

Author(s):

Hurriyatul Fitriyah ◽

Rizal Maulana

Keyword(s):

Learning Algorithm ◽

Color Space ◽

Outdoor Environment ◽

Training Data ◽

Distance Transform ◽

Validation Data ◽

Hsv Color Space ◽

Testing Data ◽

Artificial Neural Network Ann ◽

Center Distance

Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah Rectangularity, Edge-to-Center distances function, dan Distance Transform function. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara offline. Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%. AbstractWeed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15% as validation data, and 15% as testing data).

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