The largest cause of mortality worldwide is heart disease, and early identification is critical in limiting disease development. Early approaches for detecting cardiovascular illnesses assisted in determining the progressions that should have happened in high-risk persons, reducing their risks. The major goal is to save lives by recognising anomalies in cardiac circumstances, which will be performed by identifying and analysing raw data produced from cardiac information. Machine learning can provide an efficient method for making decisions and creating accurate forecasts. Machine learning techniques are being used extensively in the medical business. A unique machine learning technique is provided in the proposed study to predict cardiac disease. The planned study made advantage of open source heart disease dataset from kaggle. Hybrid algorithms for machine learning prediction are the logical mixture of many previous methodologies designed to improve efficiency and produce improved outcomes. The presented work introduces a hybrid method that employs the notion of categorization for prediction analysis. We used real patient data to build a hybrid technique to predicting cardiac disease. KNN and SVM classification techniques were utilized in this paper. Jupyter Notebook is used to implement this hybrid method. A hybrid technique outperforms other algorithms in the prediction analysis of heart disease.

Streamflow forecasting is highly crucial in the domain of water resources. For this study, we coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit streamflow at short-term (T0.33 and T0.66), intermediate-term (T1), and long-term (T2, T4, and T8) monthly intervals. Streamflow forecasts are uncertain due to stochastic disturbances caused by variations in snow-melting routines and local orography. To remedy this situation, decomposition by WT was undertaken to enhance the associative relation between the input and target sets for ANN to process. For ANN modeling, cross-correlation was used to guide input selection. Corresponding to six intervals, nine configurations were developed. Short-term intervals performed best, especially for T0.33; intermediate intervals showed decreasing performance. However, interestingly, performance regains back to a decent level for long-term forecasting. Almost all the models underestimate high flows and slightly overestimate low- to intermediate-flow conditions. At last, inference implicitly implies that shorter forecasting benefits from extrapolated trends, while the good results of long-term forecasting is associated to a larger recurrent pattern of the Gilgit River. In this way, weak performance for intermediate forecasting could be attributed to the insufficient ability of the model to capture either one of these patterns.

Cryptocurrency is a popular digital currency due to its security and peer-to-peer transferability. Predicting cryptocurrency prices is crucial for investors and traders to make informed decisions on buying, selling, or holding cryptocurrencies based on their expected value, potential risks, and returns. This study aims to identify the optimal model for predicting the prices of cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), using Deephaven for Data curation. The study involves extracting data from both cryptocurrencies by Deephaven and selecting the most correlating parameters through time lag adjustment. We use correlating cryptocurrency data to train models, such as Artificial Neural Networks (ANN), Long-Short Term Memories (LSTM), and Gated Recurrent Units (GRU). Where the trial-and-error technique was applied for selecting optimized hyper-parameters for each model. The models are then evaluated by statistical evaluators, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), separately for training and testing datasets. For Bitcoin, the results showed that the LSTM model outperform ANN and GRU models in both training and testing data with MAE, RMSE, and MAPE average values of 0.079, 1.16, and 0.0006, respectively. While for Ethereum, the results also revealed that LSTM model performance is superior with MAE, RMSE, and MAPE average values of 0.0025, 0.124 and 0.0002, respectively. While GRU (MAE 0.012, RMSE 0.117, MAPE 0.002) performs robustly against ANN (MAE 0.035, RMSE 0.149, MAPE 0.003) model.

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.

Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.

Accurate hydrological simulations comply with the water (sixth) Sustainable Development Goals (SDGs). The study investigates the utility of artificial neural networks and support vector regressors, as well as the post-simulation bias treatment of these simulations in the Swat River basin, Pakistan. For this, climate variables were lag adjusted for the first time, then cross-correlated with the flow to identify the most associative delay time. In sensitivity analysis, seven combinations were selected as input with suitable hyperparameters. For SVR, grid search cross-validation was used for determining the optimal set of hyperparameters, while for ANN, neurons and hidden layers were optimized by trial and error. We ran the model by using optimized hyperparameter configurations and input combinations. In comparison to SVRs (Root mean square error (RMSE) 34.2; mean absolute error (MAE) 3.0; CC 0.91) values, respectively, ANN fits the observations better than SVR with (RMSE 11.9; MAE 1.14; CC 0.99). Linear bias-corrected simulations greatly improved ANN performance (RMSE 3.98; MAE 0.625; CC 0.99), while the improvement was slight in the case of SVR (RMSE 35; MAE 0.58; CC 0.92). On a seasonal scale, bias-corrected simulations remedy the low- and high-flow seasonal discrepancies. Flow duration analysis results reveal deviation at low- and high-flow conditions by models, which were then reconciled by applying bias corrections.