Parkinson’s disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

Forecasting is defined as the process of estimating the change in uncertain situations. One of the most vital aspects of many applications is temperature forecasting. Using the Daily Delhi Climate Dataset, we utilize time series forecasting techniques to examine the predictability of temperature. In this paper, a hybrid forecasting model based on the combination of Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables (SARIMAX) was created to accomplish accurate forecasting for the temperature in Delhi, India. The range of the dataset is from 2013 to 2017. It consists of 1462 instances and four features, and 80% of the data is used for training and 20% for testing. First, the WD decomposes the non-stationary data time series into multi-dimensional components. That can reduce the original time series’ volatility and increase its predictability and stability. After that, the multi-dimensional components are used as inputs for the SARIMAX model to forecast the temperature in Delhi City. The SARIMAX model employed in this work has the following order: (4, 0, 1). (4, 0, [1], 12). The experimental results demonstrated that WD-SARIMAX performs better than other recent models for forecasting the temperature in Delhi city. The Mean Square Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and determination coefficient ($R^2$) of the proposed WD-SARIMAX model are 2.8, 1.13, 0.76, 1.67, 4.9, and 0.91, respectively. Furthermore, the WD-SARIMAX model utilized the proposed to forecast the temperature in Delhi over the next eight years, from 2017 to 2025.

Soil erosion, the degradation of the earth’s surface through the removal of soil particles, occurs in three phases: dislocation, transport, and deposition. Factors such as soil type, assembly, infiltration, and land cover influence the velocity of soil erosion. Soil erosion can result in soil loss in some areas and soil deposition in others. In this paper, we proposed the Random Search-Random Forest (RS-RF) model, which combines random search optimization with the Random Forest algorithm, for soil erosion prediction. This model helps to better understand and predict soil erosion dynamics, supporting informed decisions for soil conservation and land management practices. This study utilized a dataset comprising 236 instances with 11 features. The target feature’s class label indicates erosion (1) or non-erosion (−1). To assess the effectiveness of the classification techniques employed, six evaluation metrics, including accuracy, Matthews Correlation Coefficient (MCC), F1-score, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC), were computed. The experimental findings illustrated that the RS-RF model achieved the best outcomes when compared with other machine learning techniques and previous studies using the same dataset with an accuracy rate of 97.4%.

The world is experiencing exponential growth in the use of SCADA systems in many industrial fields. The increased and considerable growth in information and communication technology has been forcing SCADA organizations to shift their SCADA systems from proprietary technology and protocol-based systems into internet-based ones. This paradigm shift has also increased the risks that target SCADA systems. To protect such systems, a risk management process is needed to identify all the risks. This study presents a detailed investigation on twenty-one scientific articles, guidelines, and databases related to SCADA risk identification parameters and provides a comparative study among them. The study next proposes a comprehensive risk identification model for SCADA systems. This model was built based on the risk identification parameters of ISO 31000 risk management principles and guidelines. The model states all risk identification parameters, identifies the relationships between those parameters, and uses a hierarchical-based method to draw complete risk scenarios. In addition, the proposed model defines the interdependency risk map among all risks stated in the model. This risk map can be used in understanding the evolution of the risks through time in SCADA systems. The proposed model is then transformed into a benchmark database containing 19,163 complete risk scenarios that can affect SCADA systems. Finally, a case study is presented to demonstrate one of the usages of the proposed model and its benchmark database. This case study provides 306 possible attack scenarios that Hacktivist can use to affect SCADA systems.

Background: Children with Down syndrome (DS) often experience cognitive and adaptive challenges that affect their ability to acquire and retain critical life skills, including those needed for effective response during emergencies. Traditional training methods used to prepare children for crises are frequently static, noninteractive, and insufficiently tailored to the unique learning profiles of children with DS. These limitations contribute to reduced engagement, poor knowledge retention, and inadequate real-world preparedness. Recent advancements in game-based learning, particularly serious games, have demonstrated potential for enhancing education and skill development among individuals with cognitive impairments. Objective: This study aimed to design, implement, and evaluate Risk Resist, an adaptive serious game developed to improve emergency preparedness in children with DS. The game incorporates a dynamic difficulty adjustment algorithm that personalizes the learning experience by dynamically modifying game difficulty based on real-time behavioral performance metrics. The study also assessed whether this adaptive game-based learning approach leads to superior learning gains and engagement compared to conventional teacher-led training. Methods: A quasi-experimental, between-group design was used with 18 children diagnosed with DS, aged 8 to 12 years. Participants were randomly assigned to either an experimental group (n=9), which played Risk Resist, or a control group (n=9), which received traditional instruction on emergency scenarios. Learning outcomes were assessed using pre- and postintervention knowledge tests composed of 5 emergency-related questions. Engagement levels were measured through a structured 5-point Likert scale questionnaire completed by observing teachers. The game used a machine learning–driven dynamic difficulty adjustment model, specifically a Random Forest Regressor, which adjusted difficulty in response to individual performance indicators such as success rate, response time, and behavioral patterns during gameplay. Results: The experimental group achieved significantly higher learning gains (mean 3.6, SD 0.5) than the control group (mean 2.0, SD 0.5; P<.001). Engagement levels were also significantly greater in the game-based group (mean 4.54, SD 0.4) compared to the control group (mean 4.01, SD 0.32; P=.002). A strong positive correlation was identified between engagement and learning gain (r=0.85; P<.001), indicating that higher engagement contributed directly to improved knowledge acquisition. Conclusions: The results support the effectiveness of Risk Resist in enhancing both engagement and learning outcomes for children with DS. The integration of adaptive difficulty algorithms provides a personalized, responsive learning experience, positioning serious games as a viable and impactful tool for emergency preparedness training in special education settings.

This study addresses the vast amount of information held by the banking sector, especially regarding opportunities in tourism development, production, and large residential projects. With advancements in information technology and databases, data mining has become essential for banks to optimally utilize available data. From January 2023 to July 2024, data from the Egyptian Arab Land Bank (EALB) were analyzed using data mining techniques, including rough set theory and the Weka version 3.0 program. The aim was to identify potential units for targeted marketing, improve customer satisfaction, and contribute to sustainable development goals. By integrating sustainability principles into financing approaches, this research promotes green banking, encouraging environmentally friendly and socially responsible investments. A survey of EALB customers assessed their interest in purchasing homes under the real estate financing program. The results were analyzed with GraphPad Prism version 9.0, with 95% confidence intervals and an R-squared value close to 1, and we identified 13 units (43% of the total units) as having the highest marketing potential. This study highlights data mining’s role in enhancing marketing for the EALB’s residential projects. Combining sustainable financing with data insights promotes green banking, aligning with customer preferences and boosting satisfaction and profitability.