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The Possibility Of Applying The New York Convention To Recognise And Enforce The Foreign Arbitration Awards In Petroleum Disputes In Iraq
Get PDFObjective: This study seeks to analyse how the New York Convention for the Recognition and Enforcement of Foreign Arbitration Awards can be applied to the recognition and enforcement of foreign arbitration awards in petroleum disputes in Iraq.
Theoretical framework: Iraq acceded to the New York Convention in 2021. Iraq placed the commercial reservation, meaning that the Convention applies only to differences considered commercial under Iraqi laws. There is a great jurisprudential difference in determining the legal nature of petroleum contracts in Iraq. Thus, the Convention may not apply to these differences resulting from the petroleum contracts in Iraq. So, several studies from the literature have been used as a theoretical basis for this research.
Method: This study is based on the doctrinal legal research methodology. Both primary and secondary data are used. Results and conclusion: The results indicate no specific rule in determining the nature of petroleum contracts. It is concluded that the Convention may not be applied to enforcing international arbitration awards in petroleum disputes in Iraq. Thus, it is necessary to reconsider the commercial reservation set by Iraq. Research implications: This study will help international oil companies know the extent of applying the New York Convention to the enforcement of international arbitration awards in Iraq.
Originality/value: Improving the legal system for enforcing arbitration awards will create a friendly environment for arbitration in Iraq, which will help attract investment in the oil field
Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study
Get PDFDengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce the robustness of existing detection models, which require a training phase and thus fail to detect previously unseen outbreak patterns. Consequently, the model fails to detect newly discovered outbreak patterns. This outcome leads to inaccurate decision-making and delays in implementing prevention plans. Anomaly detection and other detection-based problems have already been widely implemented with some success using danger theory (DT), a variation of the artificial immune system and a nature-inspired computer technique. Therefore, this study employed DT to develop a novel outbreak detection model. A Malaysian dengue profile dataset was used for the experiment. The results revealed that the proposed DT model performed better than existing methods and significantly improved dengue outbreak detection. The findings demonstrated that the inclusion of a DT detection mechanism enhanced the dengue outbreak detection model’s accuracy. Even without a training phase, the proposed model consistently demonstrated high sensitivity, high specificity, high accuracy, and lower false alarm rate for distinguishing between outbreak and non-outbreak instances
Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems
Get PDFThe robustness of some classical univariate classifiers is hampered if the data are contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a considerable sample size. The performance of the classification models can be easily biased by the outliers’ problems, of which the constructed model tends to be overfitted. Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. Meanwhile, for small sample sizes, the BC and PC methods are extremely susceptible to outliers. To overcome these two problems, we proposed two methods: the Smart Univariate Classifier (SUC) and the hybrid classifier. The latter is a combination of the SUC and the BC methods, known as the Smart Univariate Bayes Classifier (SUBC). The performance of the new classification methods was evaluated and compared with the conventional BC and PC methods using the OPEC as a benchmark value. To validate the performance of these classification methods, the Probability of Exact Classification (PEC) was compared with the OPEC value. The results showed that the proposed methods outperformed the conventional BC and PC methods based on the real data sets applied. Numerical results also revealed that the SUC method could solve the overfitting problem. The results further indicated that the two proposed methods were robust against outliers. Therefore, these new methods are helpful when practitioners are confronted with overfitting and data contamination problems
Time-Distributed Attention-Layered Convolution Neural Network with Ensemble Learning using Random Forest Classifier for Speech Emotion Recognition
Get PDFSpeech Emotion Detection (SER) is a field of identifying human emotions from human speech utterances. Human speech utterances are a combination of linguistic and non-linguistic information. Nonlinguistic SER provides a generalized solution in human–computer interaction applications as it overcomes the language barrier. Machine learning and deep learning techniques were previously proposed for classifying emotions using handpicked features. To achieve effective and generalized SER, feature extraction can be performed using deep neural networks and ensemble learning for classification. The proposed model employed a time-distributed attention-layered convolution neural network (TDACNN) for extracting spatiotemporal features at the first stage and a random forest (RF) classifier, which is an ensemble classifier for efficient and generalized classification of emotions, at the second stage. The proposed model was implemented on the RAVDESS and IEMOCAP data corpora and compared with the CNN-SVM and CNN-RF models for SER. The TDACNN-RF model exhibited test classification accuracies of 92.19 percent and 90.27 percent on the RAVDESS and IEMOCAP data corpora, respectively. The experimental results proved that the proposed model is efficient in extracting spatiotemporal features from time-series speech signals and can classify emotions with good accuracy. The class confusion among the emotions was reduced for both data corpora, proving that the model achieved generalization
The Effects of Perceived Work Overload on Organizational Comitment on Employee Turnover Intention in Automotive Industry in Pahang
Get PDFEmployee turnover is a critical concern for organizations, as it can adversely affect service quality and incur substantial expenses. Drawing on the theoretical framework of social exchange theory, this study presents a model to examine the interconnections between turnover intentions, organizational commitment, and perceived task overload among employees in the automotive sector of Malaysia. Data was collected from 158 automotive industry professionals who voluntarily participated in the study. The proposed hypotheses were validated using structural equation modeling (SEM) via SmartPLS 4.0. The findings provide empirical support for the hypothesis that employees' perceptions of task overload directly influence their level of commitment to their respective organizations. Moreover, their perceived work overload, combined with organizational commitment and incentives, significantly impacts their turnover intentions, influencing their decision to leave their current positions. Surprisingly, organizational commitment was found to have no significant direct effect on turnover intention. This novel research offers a comprehensive model to manage turnover intention among automotive industry employees in Malaysia, utilizing organizational commitment as a mediating factor. The level of dedication demonstrated by employees towards their organizations plays a vital role in determining whether they will remain with the organization, even when they perceive their skills to be declining. These findings hold implications for both research and clinical practice, offering valuable insights for improving workforce readiness amidst the challenges posed by the industrial revolution 4.0
A Bibliometric Analysis on Trends, Directions and Major Players of International Relations Studies
Get PDFAs communications technology, air travel, and a complex international economy continue to make the world smaller, the importance of peaceful and cooperative relationships between nations increases. However, it is unclear to what extent research on international relations (IR) has expanded as a global discipline; narrated by balanced perspectives and provides an impact. This study conducts a bibliometric analysis of 4,986 documents related to IR as recorded in the Scopus database from 1913 to 2022. Specifically, this paper analyzes (a) the trends and developments; (b) influential documents and frequent keywords and (c) major players in terms of productive journals, authors and institutions in IR studies. This paper provides a new panoramic view through tables and science maps on the publication of IR studies. The findings show a gradual interest in the IR field before the Second World War and this accelerated during the mid-twentieth century. Political economy is gaining more importance and most publications centre on IR theories while discussing prevailing events affecting the world. However, the Western influence of IR is still primarily mainstream, where IR publications are mainly controlled by large Western publishers, influenced by Western authors affiliated with long-established Western institutions. Seemingly, the non-Western contributions to the IR field have yet to establish their own footing in the field despite much discussion about diversifying IR. This remains a challenge for non-Western scholars, journal publishers, and institutions seeking to contribute to the ongoing debate in the study of international relations
The Face and Content Validity of an Instrument for Measuring Financial Risk Tolerance
Get PDFAccurate evaluation of investment risk tolerance is critical in an investment decision-making process because a mismatch between the risk an investor could tolerate and the risk-return expectations could lead to frustration towards the actual financial gains or financial losses. This study aims to develop a valid instrument (or scale) for self-directed Malaysian investors to measure financial risk tolerance based on four main constructs, i.e., risk attitude, risk propensity, risk capacity, and financial literacy. An initial 36-item instrument was developed based on the assessment framework from Cordell (2001), which subsequently was examined by four lay experts for face validity. Consequently, according to Andrian et al. (2018), seven professional experts, comprising theoretical, industry practitioner, and psychometric experts, were involved in reviewing the relevancy of the content of each item towards measuring financial or investment risk tolerance level. As a result, the instrument is deemed to have a good face value, with 94.4% of the items rated highly at 4 or 3 (out of the maximum rating of 4) by lay experts. Out of the 36 items, only 5.56% are rated 1 or 2, and 16.7% of the items require revision in terms of their face value and clarity. The content validity exercise resulted in high scores of more than 0.83 cut scores based on Lynn (1986) for the scale content validity index (S-CVI), with nine items recorded as item content validity index (I-CVI) below 0.83. The S-CVI improved further to 0.90 after the removal of items with low I-CVI. The findings have also successfully produced a valid instrument that can measure the financial risk tolerance level of investors in Malaysia
