Search results for: corporate credit rating prediction

Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Vijay S. Katta, Vinod Kushwah, Rudraksh Tiwari, Mulayam Singh Gaur, Priti Dimri, Ashok Kumar Sharma

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The formation process of seismic activities because of abrupt slip on faults, tectonic plate moments due to accumulated stress in the Earth’s crust. The prediction of seismic activity is a very challenging task. We have studied the changes in surface latent heat temperatures which are observed prior to significant earthquakes have been investigated and could be considered for short term earthquake prediction. We analyzed the surface latent heat temperature (SLHT) variation for inland earthquakes occurred in Chamba, Himachal Pradesh (32.5 N, 76.1E, M-4.5, depth-5km) nearby the main boundary fault region, the data of SLHT have been taken from National Center for Environmental Prediction (NCEP). In this analysis, we have calculated daily variations with surface latent heat temperature (0C) in the range area 1⁰x1⁰ (~120/KM²) with the pixel covering epicenter of earthquake at the center for a three months period prior to and after the seismic activities. The mean value during that period has been considered in order to take account of the seasonal effect. The monthly mean has been subtracted from daily value to study anomalous behavior (∆SLHT) of SLHT during the earthquakes. The results found that the SLHTs adjacent the epicenters all are anomalous high value 3-5 days before the seismic activities. The abundant surface water and groundwater in the epicenter and its adjacent region can provide the necessary condition for the change of SLHT. To further confirm the reliability of SLHT anomaly, it is necessary to explore its physical mechanism in depth by more earthquakes cases.

Keywords: surface latent heat temperature, satellite data, earthquake, magnetic storm

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Lilian Kishimbo

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This study aims to examine Corporate Social Responsibility (CSR) practices of local large firms of East Africa region. In this study CSR is defined as all actions that go beyond obeying minimum legal requirements as espoused by other authors. Despite the increase of CSR literature empirical evidence clearly demonstrate an imbalance of CSR studies in the developing countries . Moreover, it is evident that most of the research on CSR in developing economies emerges from large fast-growing economies or BRICS members (i.e. Brazil, India, China and South Africa), and Indonesia and Malaysia and a further call for more research in Africa is particularly advocated. Taking Africa as an example, there are scanty researches on CSR practices, and the few available studies are mainly from Nigeria and South Africa leaving other parts of Africa for example East Africa underrepresented. Furthermore, in the face of globalization, experience shows that literature has focused mostly on multinational companies (MNCs) operating in either North-North or North-South and less on South-South indigenous local firms. Thus the existing literature in Africa shows more studies of MNCs and little is known about CSR of local indigenous firms operating in the South particularly in the East Africa region. Accordingly, this paper explores CSR practices of indigenous local large firms of East Africa region particularly Kenya and Tanzania with the aim of testing the hypothesis that do local firms of East Africa region engage in similar CSR practices as firms in other parts of the world?. To answer this question only listed local large firms were considered based on the assumption that they are large enough to engage. Newspapers were the main source of data and information collected was supplemented by business Annual Reports for the period 2010-2012. The research finding revealed that local firms of East Africa engage in CSR practices. However, there are some differences in the set of activities these firms prefers to engage in compared to findings from previous studies. As such some CSR that were given priority by firms in East Africa were less prioritized in the other part of the world including Indonesia. This paper will add knowledge to the body of CSR and experience of CSR practices of South-South indigenous firms where is evidenced to have a relative dearth of literature on CSR. Finally, the paper concludes that local firms of East Africa region engage in similar activities like other firms globally. But firms give more priority to some activities such education and health related activities. Finally, the study intends to assist policy makers at firm’s levels to plan for long lasting projects related to CSR for their stakeholders.

Keywords: Africa, corporate social responsibility, developing countries, indigenous firms, Kenya, Tanzania

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Authors: Biruhi Tesfaye, Avinash M. Potdar

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The laboratory test process to determine the California bearing ratio (CBR) of black cotton soils is not only overpriced but also time-consuming as well. Hence advanced prediction of CBR plays a significant role as it is applicable In pavement design. The prediction of CBR of treated soil was executed by Artificial Neural Networks (ANNs) which is a Computational tool based on the properties of the biological neural system. To observe CBR values, combined eggshell and waste glass was added to soil as 4, 8, 12, and 16 % of the weights of the soil samples. Accordingly, the laboratory related tests were conducted to get the required best model. The maximum CBR value found at 5.8 at 8 % of eggshell waste glass powder addition. The model was developed using CBR as an output layer variable. CBR was considered as a function of the joint effect of liquid limit, plastic limit, and plastic index, optimum moisture content and maximum dry density. The best model that has been found was ANN with 5, 6 and 1 neurons in the input, hidden and output layer correspondingly. The performance of selected ANN has been 0.99996, 4.44E-05, 0.00353 and 0.0067 which are correlation coefficient (R), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) respectively. The research presented or summarized above throws light on future scope on stabilization with waste glass combined with different percentages of eggshell that leads to the economical design of CBR acceptable to pavement sub-base or base, as desired.

Keywords: CBR, artificial neural network, liquid limit, plastic limit, maximum dry density, OMC

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Authors: Nastaran Moosavi, Mohammad Mokhtari

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Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: density, p-impedance, s-impedance, post-stack seismic inversion, pre-stack seismic inversion

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Authors: Sang Heon Han, Daejun Chang, Won Yang

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NOx reduction characteristics of syngas fuel were numerically investigated for the 2MW pilot scale heavy oil furnace of KITECH (Korea Institute of Industrial Technology). The secondary fuel and syngas was fed into the furnace with two purposes- partial replacement of main fuel and reburning of NOx. Some portion of syngas was fed into the flame zone to partially replace the heavy oil, while the other portion was fed into the furnace downstream to reduce NOx generation. The numerical prediction was verified by comparing it with the experimental results. Syngas of KITECH’s experiment, assumed to be produced from biomass, had very low calorific value and contained 3% hydrocarbon. This study investigated the precise behavior of NOx generation and NOx reduction as well as thermo-fluidic characteristics inside the furnace, which was unavailable with experiment. In addition to 3% hydrocarbon syngas, 5%, and 7% hydrocarbon syngas were numerically tested as reburning fuels to analyze the effect of hydrocarbon proportion to NOx reduction. The prediction showed that the 3% hydrocarbon syngas is as much effective as 7% hydrocarbon syngas in reducing NOx.

Keywords: syngas, reburning, heavy oil, furnace

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Authors: Mohammed El-Arbi Khalfallah, Mohamed Lakhdar Hadji

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Managing and controlling risk is a topic research in the world of finance. Before a risky situation, the stakeholders need to do comparison according to the positions and actions, and financial institutions must take measures of a particular market risk and credit. In this work, we study a model of risk measure in finance: Value at Risk (VaR), which is a new tool for measuring an entity's exposure risk. We explain the concept of value at risk, your average, tail, and describe the three methods for computing: Parametric method, Historical method, and numerical method of Monte Carlo. Finally, we briefly describe advantages and disadvantages of the three methods for computing value at risk.

Keywords: average value at risk, conditional value at risk, tail value at risk, value at risk

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Authors: Wang Chung-Kwei, Lo Kuo Ying

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This study is aimed to examine main and moderation effect of Chinese traditional social wisdom ‘Ren-qing Shi-kuo’ on the adjustment of insurance workers. Rationale: Ren-qing Shi-ku as a social wisdom has been emphasized and practiced by collective-oriented Chinese for thousand years. The concept of‘Ren-qing Shi-ku’includes values, beliefs and behavior rituals, which helps Chinese to cope with interpersonal conflicts in a sophisticated and closely tied collective society. Based on interview and literature review, we found out Chinese still emphasized the importance of ‘Ren-qing Shi-ku’. The concepts contains five factors, including ‘proper emotion display’, ‘social ritual abiding’, ‘ make empathetic concession’, ‘harmonious and proper behavior’ and ‘tolerance for the interest of the whole’. We developed an indigenous ‘Ren-qing Shi-ku’scale based on interview data and a survey on social worker students. Research methods: We conduct a dyad survey between 294 insurance worker and their supervisors. Insurance workers’ response on ‘Ren-qing Shi-ku,emotion labor, emotional exhaustion, work stress and load, work satisfaction and well-being were collected. We also ask their supervisors to rate these workers ‘empathy, social rule abiding, work performance, and Ren-qing Shi-ku performance. Results: Students’self-ratings on Ren-qing Shi-ku scale are positively correlated with rating from their supervisors on all above indexes. Workers who have higher Ren-qing Shi-ku score also have lower work stress and emotion exhaustion, higher work satisfaction and well-being, more emotion deep acting. They also have higher work performance, social rule abiding, and Ren-qing Shi-ku performance rating from their supervisor. The finding of this study suggested Ren-qing Shi-ku is an effective indicator on insurance workers ‘adjustment. Since Ren-qing Shi-ku is trainable, we suggested that Ren-qing Shi-ku training might be beneficial to service industry in a collective-oriented culture.

Keywords: work stress, Ren-qing Shi-ku, emotional exhaustion, work satisfaction, well-being

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: G. Yakubova, A. Kavetskiy, S. A. Prior, H. A. Torbert

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In-situ soil carbon determination over large soil surface areas (several hectares) is required in regard to carbon sequestration and carbon credit issues. This capability is important for optimizing modern agricultural practices and enhancing soil science knowledge. Collecting and processing representative field soil cores for traditional laboratory chemical analysis is labor-intensive and time-consuming. The neutron-stimulated gamma analysis method can be used for in-situ measurements of primary elements in agricultural soils (e.g., Si, Al, O, C, Fe, and H). This non-destructive method can assess several elements in large soil volumes with no need for sample preparation. Neutron-gamma soil elemental analysis utilizes gamma rays issued from different neutron-nuclei interactions. This process has become possible due to the availability of commercial portable pulse neutron generators, high-efficiency gamma detectors, reliable electronics, and measurement/data processing software complimented by advances in state-of-the-art nuclear physics methods. In Pulsed Fast Thermal Neutron Analysis (PFTNA), soil irradiation is accomplished using a pulsed neutron flux, and gamma spectra acquisition occurs both during and between pulses. This method allows the inelastic neutron scattering (INS) gamma spectrum to be separated from the thermal neutron capture (TNC) spectrum. Based on PFTNA, a mobile system for field-scale soil elemental determinations (primarily carbon) was developed and constructed. Our scanning methodology acquires data that can be directly used for creating soil elemental distribution maps (based on ArcGIS software) in a reasonable timeframe (~20-30 hectares per working day). Created maps are suitable for both agricultural purposes and carbon sequestration estimates. The measurement system design, spectra acquisition process, strategy for acquiring field-scale carbon content data, and mapping of agricultural fields will be discussed.

Keywords: neutron gamma analysis, soil elemental content, carbon sequestration, carbon credit, soil gamma spectroscopy, portable neutron generators, ArcMap mapping

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Walter E. Allen, Robert D. Murray

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Many large transit agencies have adopted RFID technology and electronic automatic fare collection (AFC) or smart card systems, but small and rural agencies remain tied to obsolete manual, cash-based fare collection. Small countries or transit agencies can benefit from the implementation of smart card AFC technology with the promise of increased passenger convenience, added passenger satisfaction and improved agency efficiency. For transit agencies, it reduces revenue loss, improves passenger flow and bus stop data. For countries, further implementation into security, distribution of social services or currency transactions can provide greater benefits. However, small countries or transit agencies cannot afford expensive proprietary smart card solutions typically offered by the major system suppliers. Deployment of Contactless Fare Media System (CFMS) Standard eliminates the proprietary solution, ultimately lowering the cost of implementation. Acumen Building Enterprise, Inc. chose the Yuma County Intergovernmental Public Transportation Authority (YCIPTA) existing proprietary YCAT smart card system to implement CFMS. The revised system enables the purchase of fare product online with prepaid debit or credit cards using the Payment Gateway Processor. Open and interoperable smart card standards for transit have been developed. During the 90-day Pilot Operation conducted, the transit agency gathered the data from the bus AcuFare 200 Card Reader, loads (copies) the data to a USB Thumb Drive and uploads the data to the Acumen Host Processing Center for consolidation of the data into the transit agency master data file. The transition from the existing proprietary smart card data format to the new CFMS smart card data format was transparent to the transit agency cardholders. It was proven that open standards and interoperability design can work and reduce both implementation and operational costs for small transit agencies or countries looking to expand smart card technology. Acumen was able to avoid the implementation of the Payment Card Industry (PCI) Data Security Standards (DSS) which is expensive to develop and costly to operate on a continuing basis. Due to the substantial additional complexities of implementation and the variety of options presented to the transit agency cardholder, Acumen chose to implement only the Directed Autoload. To improve the implementation efficiency and the results for a similar undertaking, it should be considered that some passengers lack credit cards and are averse to technology. There are more than 1,300 small and rural agencies in the United States. This grows by 10 fold when considering small countries or rural locations throughout Latin American and the world. Acumen is evaluating additional countries, sites or transit agency that can benefit from the smart card systems. Frequently, payment card systems require extensive security procedures for implementation. The Project demonstrated the ability to purchase fare value, rides and passes with credit cards on the internet at a reasonable cost without highly complex security requirements.

Keywords: automatic fare collection, near field communication, small transit agencies, smart cards

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Authors: M. Karimpour, N. Elkhoury, L. Hitihamillage, S. Moridpour, R. Hesami

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There is a necessity among rail transportation authorities for a superior understanding of the rail track degradation overtime and the factors influencing rail degradation. They need an accurate technique to identify the time when rail tracks fail or need maintenance. In turn, this will help to increase the level of safety and comfort of the passengers and the vehicles as well as improve the cost effectiveness of maintenance activities. An accurate model can play a key role in prediction of the long-term behaviour of railroad tracks. An accurate model can decrease the cost of maintenance. In this research, the rail track degradation is predicted using an autoregressive moving average with exogenous input (ARMAX). An ARMAX has been implemented on Melbourne tram data to estimate the values for the tram track degradation. Gauge values and rail usage in Million Gross Tone (MGT) are the main parameters used in the model. The developed model can accurately predict the future status of the tram tracks.

Keywords: ARMAX, dynamic systems, MGT, prediction, rail degradation

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Authors: Surita Maini, Sanjay Dhanka

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Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: C. Recommandé, J. C. Blind, A. Clavel, R. Gourichon, V. Le Gal

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Simulations are developed in this paper with usual DSGE model equations. The model is based on simplified version of Smets-Wouters equations in use at European Central Bank which implies 10 macro-economic variables: consumption, investment, wages, inflation, capital stock, interest rates, production, capital accumulation, labour and credit rate, and allows take into consideration the banking system. Throughout the simulations, this model will be used to evaluate the impact of rate shocks recounting the actions of the European Central Bank during 2008.

Keywords: CC-LM, Central Bank, DSGE, liquidity shock, non-standard intervention

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Mohd Azman Abdul Latif, Erwan Basiron

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A flaw or drift from expected operational performance in one component (NAND, PMIC, controller, DRAM, etc.) may affect the reliability of the entire Solid State Drive (SSD) system. Therefore, it is important to ensure the required quality of each individual component through qualification testing specified using standards or user requirements. Qualification testing is time-consuming and comes at a substantial cost for product manufacturers. A highly technical team, from all the eminent stakeholders is embarking on reliability prediction from beginning of new product development, identify critical to reliability parameters, perform full-blown characterization to embed margin into product reliability and establish control to ensure the product reliability is sustainable in the mass production. The paper will discuss a comprehensive development framework, comprehending SSD end to end from design to assembly, in-line inspection, in-line testing and will be able to predict and to validate the product reliability at the early stage of new product development. During the design stage, the SSD will go through intense reliability margin investigation with focus on assembly process attributes, process equipment control, in-process metrology and also comprehending forward looking product roadmap. Once these pillars are completed, the next step is to perform process characterization and build up reliability prediction modeling. Next, for the design validation process, the reliability prediction specifically solder joint simulator will be established. The SSD will be stratified into Non-Operating and Operating tests with focus on solder joint reliability and connectivity/component latent failures by prevention through design intervention and containment through Temperature Cycle Test (TCT). Some of the SSDs will be subjected to the physical solder joint analysis called Dye and Pry (DP) and Cross Section analysis. The result will be feedbacked to the simulation team for any corrective actions required to further improve the design. Once the SSD is validated and is proven working, it will be subjected to implementation of the monitor phase whereby Design for Assembly (DFA) rules will be updated. At this stage, the design change, process and equipment parameters are in control. Predictable product reliability at early product development will enable on-time sample qualification delivery to customer and will optimize product development validation, effective development resource and will avoid forced late investment to bandage the end-of-life product failures. Understanding the critical to reliability parameters earlier will allow focus on increasing the product margin that will increase customer confidence to product reliability.

Keywords: e2e reliability prediction, SSD, TCT, solder joint reliability, NUDD, connectivity issues, qualifications, characterization and control

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Authors: Hussaini Bala

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There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

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Authors: Disha Batra

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With the advent of economic reforms in India in 1992, economic journalism in India has undergone a sea change along with the rise in the Indian economy. Squeezing out of economic news stories (economy-in-general) over business (individual corporate stories) and financial (financial and equity markets) news stories have been done and are still underway. The objective of the study is to explore how economic journalism – news stories about macroeconomic issues or economy-in-general has changed over the past three decades with the emergence of LPG (Liberalisation, Privatisation, and Globalisation) policies in India. The purpose of the study is to examine to what extent business and financial news are constricting economic news which is done by analysing news stories and content of business papers. The study is based on the content analyses of the top three Indian business dailies as per IRS (Indian Readership Survey) 2017. The parametric analysis of the different parameters (source of information, sub-topic, a dominant source in economic news, layout and framing, etc.) has been done in order to come across with the distinct adaptations and modifications by these dailies. The paper significantly dwells upon the thematic analysis of these newspapers in order to explore and find out the coverage given to various sub-themes of EBF (economic, business, and financial) journalism. The study revealed that stories concerning broader issues about the economy which are likely to be of public concern had been dropped. The paper further indicates an upward trend for the stories concerning individual corporate, equity, and financial markets. Findings of the study raise concern over the indicated disparity between economic and business news stories which may further limit the information that people need in order to make well-versed decisions.

Keywords: business-papers, business news, economic news, financial news

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Pouira Askary

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Indeed, in our globalized world which is facing with various international crises, the transnational corporations and other business enterprises have the capacity to foster economic well-being, development, technological improvement and wealth, as well as causing adverse impacts on human rights. The UN Human Rights Council declared that although the primary responsibility to protect human rights lie with the State but the transnational corporations and other business enterprises have also a responsibility to respect and protect human rights in the framework of corporate social responsibility. In 2011, the Human Rights Council endorsed the Guiding Principles on Business and Human Rights, a set of guidelines that define the key duties and responsibilities of States and business enterprises with regard to business-related human rights abuses. In UN’s view, the Guiding Principles do not create new legal obligations but constitute a clarification of the implications of existing standards, including under international human rights law. In 2014 the UN Human Rights Council decided to establish a working group on transnational corporations and other business enterprises whose mandate shall be to elaborate an international legally binding instrument to regulate, in international human rights law, the activities of transnational corporations and other business enterprises. Extremely difficult task for the working group to codify a legally binding document to regulate the behavior of corporations on the basis of the norms of international law! Concentration of this paper is on the origins of those human rights applicable on business enterprises. The research will discuss that the social and ethical roots of the CSR are much more institutionalized and elaborated than the legal roots. Therefore, the first step is to determine whether and to what extent corporations, do have an ethical responsibility to respect human rights and if so, by which means this ethical and social responsibility is convertible to legal commitments.

Keywords: CSR, ethics, international law, human rights, development, sustainable business

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Authors: Irfan Ameer

Abstract:

Bribery control in developing countries is the biggest challenge for multinational enterprises (MNEs). Bribery practices are socially embedded and institutionalized, and therefore may achieve collective legitimacy in the society. MNEs often have better and strict norms, codes and standards about such corrupt practices. Bribery in B2B sales relationships has been researched but studies focusing on the role of firm in controlling bribery are scarce. The main objective of this paper is to explore MNEs strategies to control bribery in an environment where bribery is institutionalized. This qualitative study uses narrative approach and focuses on key events, actors and their role in controlling bribery in B2B sales relationships. The context of this study is pharmaceutical industry of Pakistan and data is collected through 23 episodic interviews supported by secondary data. The Corporate social responsibility (CSR) literature e.g. CSR three domain model and CSR pyramid is used to make sense of MNEs strategies to control bribery in developing countries. Results show that MNEs’ bribery control strategies are rather emerging based on the role of some key stakeholders and events which shape bribery strategies. Five key bribery control strategies were found through which MNEs can control both demand and supply side of bribery: bribery related codes development; bribery related codes implementation; focusing on competitive advantage; find mutually beneficial ethical solution; and collaboration with ethical stakeholders. The results also highlight the problems associated with each strategy. Study is unique in a sense that it focuses on stakeholders having unethical interests and provides guidelines to MNEs in controlling bribery practices in B2B sales relationships.

Keywords: bribery, developing countries, CSR, narrative research, B2B sales, MNEs

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Authors: Arun Goel

Abstract:

Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper, attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly and Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly and Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicates the improvement in the performance of SVM (Poly and Rbf) in comparison to dimensional form of scour.

Keywords: modeling, pier scour, regression, prediction, SVM (Poly and Rbf kernels)

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Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

Abstract:

Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.

Keywords: recurrent neural networks, global solar radiation, multi-layer perceptron, gradient, root mean square error

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Authors: Silke Bustamante, Andrea Pelzeter, Andreas Deckmann, Rudi Ehlscheidt, Franziska Freudenberger

Abstract:

Many companies increasingly seek to enhance their attractiveness as an employer to bind their employees. At the same time, corporate responsibility for social and ecological issues seems to become a more important part of an attractive employer brand. It enables the company to match the values and expectations of its members, to signal fairness towards them and to increase its brand potential for positive psychological identification on the employees’ side. In the last decade, several empirical studies have focused this relationship, confirming a positive effect of employees’ CSR perception and their affective organizational commitment. The current paper aims to take a slightly different view by analyzing the impact of another factor on commitment: the weighted employee’s satisfaction with the employer CSR. For that purpose, it is assumed that commitment levels are rather a result of the fulfillment or disappointment of expectations. Hence, instead of merely asking how CSR perception affects commitment, a more complex independent variable is taken into account: a weighted satisfaction construct that summarizes two different factors. Therefore, the individual level of commitment contingent on CSR is conceptualized as a function of two psychological processes: (1) the individual significance that an employee ascribes to specific employer attributes and (2) the individual satisfaction based on the fulfillment of expectation that rely on preceding perceptions of employer attributes. The results presented are based on a quantitative survey that was undertaken among employees of the German service sector. Conceptually a five-dimensional CSR construct (ecology, employees, marketplace, society and corporate governance) and a two-dimensional non-CSR construct (company and workplace) were applied to differentiate employer characteristics. (1) Respondents were asked to indicate the importance of different facets of CSR-related and non-CSR-related employer attributes. By means of a conjoint analysis, the relative importance of each employer attribute was calculated from the data. (2) In addition to this, participants stated their level of satisfaction with specific employer attributes. Both indications were merged to individually weighted satisfaction indexes on the seven-dimensional levels of employer characteristics. The affective organizational commitment of employees (dependent variable) was gathered by applying the established 15-items Organizational Commitment Questionnaire (OCQ). The findings related to the relationship between satisfaction and commitment will be presented. Furthermore, the question will be addressed, how important satisfaction with CSR is in relation to the satisfaction with other attributes of the company in the creation of commitment. Practical as well as scientific implications will be discussed especially with reference to previous results that focused on CSR perception as a commitment driver.

Keywords: corporate social responsibility, organizational commitment, employee attitudes/satisfaction, employee expectations, employer brand

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

Abstract:

As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: apartment housing, machine learning, multi-objective optimization, performance prediction

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