Al Mansour University College

Comorbidity, the simultaneous existence of multiple medical conditions in a patient, is a major challenge in healthcare. Comorbidity is highly threatening for healthcare systems, which requires innovative solutions over traditional methods. The medical field is challenged by accurately diagnosing these intertwined diseases of coexisting ailments and anticipating their rise. The current diagnostic approaches are time-consuming and inaccurate, hinder effective treatment, and delay accurate results for the patient. Artificial intelligence can provide an effective method for early prediction of comorbidity risks. In this study, various artificial intelligence models are used, and a clinical dataset of 271 patients is utilized to diagnose comorbidity. In which a hybrid diagnosis model is proposed based on the intersection between machine learning (ML) and feature selection techniques for the detection of comorbidity. Fuzzy decision by opinion score method is utilized as a sophisticated tool to select the most representative ML for prediction. Extensive simulation results showed an accuracy rate of 91.463 using AdaBoost ML. Furthermore, utilizing the fuzzy decision by opinion score technique, we were able to confirm that the best model using all features as well as the chi square and KBest features is the AdaBoost, which scored the smallest value of 0.204 and hence confirm that it is the best selected ML model for comorbidity.

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The world is witnessing a noticeable increase in financial exchange in digital currencies such as Bitcoin, Ethereum, and others, as transactions in electronic markets have begun to rise recently, which increases the difficulty of maintaining security and trust in decentralized financial systems that use distributed databases and the technologies that interact with them in Ethereum networks, blockchain, etc. This study presents a hybrid model based on the PyCaret library and includes 12 machine learning classifiers, with the aim of identifying fraudulent activities in Bitcoin transactions and enhancing the security of Ethereum networks and blockchain technology. The results reveal the effectiveness of different models in identifying fraudulent activities on the Ethereum network through a comprehensive performance comparison. The classifiers that showed the highest accuracy scores, which ranged from 0.9814 to 0.9862, were the Random Forest classifier, the visual gradient boosting machine, and the additive tree classifier. It is important to note that both Gradient Boosting Classifier and K Neighbors Classifier performed well, with accuracies above 0.96 and AUC scores above 0.99. However, some models, such as Naive Bayes, showed lower accuracy and AUC scores, suggesting that they have limitations in terms of accurately detecting fraudulent transactions. These results highlight the importance of choosing appropriate machine learning models for fraud detection tasks in general, with ensemble techniques such as Extra Trees and Random Forest showing great promise in this regard.

Abstract: Background: Climate change stands as one of the most critical global challenges with enormous implications. Since its science is well understood, efforts now focus on modeling and predicting its effects to mitigate or adapt to these. Deep learning, with its remarkable aptitude for data representation and analysis, is a promising candidate to enhance weather attack predictions on a global scale.Objective: This empirical study will assess their central tendencies and relationships for understanding the effectiveness of deep learning models in anticipation of climate change impacts. The paper investigates whether recently proposed models can provide better predictions than traditional techniques.Methodology: Authors utilize a detailed dataset of past climate data and its consequences This dataset is used to train and test deep learning architectures, such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN), but, for the first, comparing with traditional regression models.Results: The findings show that DL techniques are very effective in comparison to traditional methods when it comes to predicting the impacts of climate change. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have proven to be highly accurate at detecting complex relationships among climate factors and their impacts such as extreme weather events or sea level rise.Conclusion: The potential of deep learning approaches to improve our ability to model the consequences of climate change is substantial. Its forecasts have greater skill and the ability to inform policy and adaptation effectively. Given the continued acceleration of climate change, deploying advanced machine learning will be critical to maintaining a steady state.