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Does Green Pay Less? Global Corporate Bond Evidence on Primary and Secondary Yields by El Kenawy, Youssef
Published 2026Subjects: “…Machine learning in finance Application of artificial intelligence methods to financial analysis and prediction. …”
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Page will reload when a filter is selected or excluded.- General 472 results 472
- Artificial Intelligence 302 results 302
- Access Artificial Intelligence and Machine Learning 171 results 171
- Artificial neural network 4 results 4
- Artificial Neural Networks 3 results 3
- Artificial Neural Network 2 results 2
- Academic performance 1 results 1
- Acceptability 1 results 1
- An experiment was conducted to compare the utilization of tephrosia candida and Leucaena leucocephala in mixtures with Panicum maximum as feed for small ruminants, using the artificial bag technique of feed evaluation. Three West African dwarf (WAD) sheep with rumen cannula were used for the experiment. T. candida was formulated into diets with P. maximum as diets A, B and C while L. leucocephala was formulated into diets with P. maximum as diets D, E and F in the ratio 3:1, 1:1 and 1:3 respectively for both legumes. The degradation characteristics indicated L. leucocephala – based diets as being more (P < 0.05) degradable in the rumen than the T. candida – based diets, with diet D having the highest potential degradability (a+b) value. 1 results 1
- Artificial Insemination by Donor (AID) 1 results 1
- Artificial Neural Network (ANN), 1 results 1
- Artificial Neural Network has been discovered as a better alternative to traditional models and that is why a model based on the Multilayer Perceptron algorithm was developed in this study. The appropriate number of hidden neurons that best modeled the academic performance of students was determined by the developed Network algorithm. Test data evaluation showed that Network Architecture 17-80 -1 was chosen among the numerous developed network architectures because of its model performances. The chosen network architecture gave the minimum value of Mean Square Error (MSE = 0.0718), minimum value of Network Information Criteria (NIC = 0.0743), maximum value of R- Square (R2=0.8975) and maximum value of Adjusted Network Information Criteria (ANIC= 0.8931). It was equally observed that there were patterns in the movement of hidden neurons against the model evaluation criteria. As the number of the hidden neurons appreciates the value of both MSE and NIC decreases down the plot, while that of ^-Square and ^MCvalues appreciate down the plot. The network was able to model the research problem with acceptable values judging from the model checking criteria considered in this work. Also the order of contribution of the predictor variables to the model was determined. 1 results 1
- Artificial Neural Networks (ANN) 1 results 1
- Artificial bag technique 1 results 1
- Artificial inoculation 1 results 1
- Artificial insemination 1 results 1
- Background: Artificial insemination by donor (AID) is specifically indicated in cases of incurable male infertility. Acceptability depends on perceptions largely influenced by religious and sociocultural perspectives. Male factor accounts for 20-50% of the causes of infertility and shows geographic variation in Nigeria. Method: A descriptive cross-sectional survey of all infertile women attending the gynecology clinic of the University College Hospital, Ibadan, between January and June 2014. 181 self-administered questionnaires were distributed to all consenting infertile women, however only 163 were suitable for analysis. Data analysis was descriptive and inferential at 95% confidence interval and a P value of less than 0.05 was considered statistically significant. Result: The mean duration of infertility was 5.7 ± 4.33 years. Fifty seven (35.0%) respondents were willing to accept artificial insemination by donor, while ninety three (57.1%) were unwilling to accept artificial insemination. Socio-cultural factor (48.1%) was the major reason for non-acceptability of artificial insemination by donor. Acceptability of AID was influenced by adequate knowledge about the procedure (P < 0.01). Sixty percent of the respondents had good knowledge and over half of them obtained the information from the news/print media. In this Study, acceptability of AID was not influenced by the age of the respondents, family structure, duration or type of infertility or educational status. (P > 0.05). Conclusion: This study revealed a low acceptance rate for Artificial insemination by donor. Providing information on AID as a treatment option during counseling and routine infertility management may be the needed drive to improve awareness and promote uptake when necessary. 1 results 1
- Brown blotch 1 results 1
- Cathodic Protection 1 results 1
- Conflict 1 results 1
- Conflict is part of human social interaction, which may occur from a mere misunderstanding among groups of settlers. In recent times, advanced Machine Learning (ML) techniques have been applied to conflict prediction. Strategic frameworks for improving ML settings in conflict research are emerging and are being tested with new algorithm-based approaches. These developments have given rise to the need to develop a Deep Neural Network model that predicts conflicts. Hence, in this study, two Artificial Neural Network models were developed, the dataset which was extracted from https://www.data.worlduploaded by the Armed Conflict Location and Event Data Project (ACLED), in four separate CSV files (January 2015 to December 2018). The dataset for the year 2015 has 2697 instances and 28 features, for 2016 was 2233 with the same feature, for 2017 has 2669 instances with the same features, and 2018 has 1651 instances. After the development of the models: the baseline Artificial Neural Network achieved an accuracy of 95% and a loss of 5% on the training data and an accuracy of 90% and 10% loss on the test set. The Deep Neural Network Model achieved 98% accuracy and 2% loss on the training set, with 89% accuracy and 11% loss on the test set. It was concluded that to further improve the prediction of conflict, there is a need to address the issue of the dataset, in developing a better and more robust model. 1 results 1
- Continuous wavelet transform 1 results 1
- Cowpea 1 results 1
- Day of the year 1 results 1
- Deep Neural Network 1 results 1
- Degradation characteristics 1 results 1
- Deposition 1 results 1
- Disease 1 results 1
- Drilling cost 1 results 1
- Drilling operation in the oil and gas industry takes most of the well cost and how fast the drilling bit penetrate and bore formation is termed the rate of penetration (ROP). Since most of the cost incurred during drilling is related to the drilling operations, three is need not only to drill carefully, but also to optimize the drilling process. A lot of parameters are related to the rate of penetration which are actually interdependent on each other. This makes it difficult to predict the influence of every single parameter. Drilling optimization techniques have been used recently to reduce drilling operation cost. There are different approaches to optimizing the cost of drilling oil and gas wells, some of which include static and /or real time optimization of drilling parameters. A potential area for optimization of drilling cost is through bit run in the well but this is particularly difficult due to its significance in both drilling time and bit cost. In this sense, as a particular bit gets used, it gets dull as its footage increases, resulting from the reduction in the bit penetration rate. The reduction in penetration rate increases total drill time. In order to optimize bit cost, it is desirable to find a trade-off between the two by a bit change policy. This study is aimed at minimizing drilling time by use of artificial intelligent for the bit program. Data obtained from a well in the Niger delta region of Nigeria was used in this study and the cost of optimization modelled as a Marcov decision process where the intelligent agent was to learn the optimal timings for bit change by reinforcement policy Iteration learning. This study was able to achieve its objectives as the reinforcement learning optimization process performed very well with time as the computer agent was able to figure out how to improve drilling cost over time. Better results could be obtained with a better hardware and increased training time. 1 results 1
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- Discover Artificial Intelligence (Springer) 146 results 146
- Advanced Intelligent Systems 96 results 96
- CAAI Transactions on Intelligence Technology 46 results 46
- Journal of Artificial Intelligence and Soft Computing Research 30 results 30
- Distill 25 results 25
- Journal of Machine Learning Research (JMLR) 25 results 25
- Autonomous Intelligent Systems 22 results 22
- Journal of Artificial General Intelligence 20 results 20
- Applied AI Letters 16 results 16
- International Journal of Intelligent Systems and Applications 12 results 12
- Journal of Intelligent Systems : Theory and Applications 9 results 9
- Journal of Artificial Societies and Social Simulation 7 results 7
- Advances in Artificial Intelligence Research 6 results 6
- International Journal of Machine Learning and Applications 6 results 6
- International Journal of Advanced Research in Artificial Intelligence 4 results 4
- Global Perspectives on Artificial Intelligence 2 results 2
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