The rising deployment of software in automation and the cognitive skills of machines indicate a machine revolution in modern human civilization. Thus, diagnosing and predicting software faults is crucial to software reliability. In this paper, we first preprocessed four real datasets offered by National Aeronautics and Space Administration with twenty-one features using the Synthetic Minority Oversampling Technique and Label Encoding techniques. Subsequently, we experimented with thirteen software fault diagnosis Machine Learning (ML) models, i.e ., Random Forest Regression, Linear Regression, Naïve Bayes, Decision Tree Classifier, Logistic Regression, KNeighbors Classifier, AdaBoost, Gradient Boosting Classifier, Gradient Boosting Regression, XGBR Regressor, XGBoost Classifier, Extra Trees Classifier and Support Vectors Machine after that, we compared each ML Model to select the best diagnostic model. Among them, XGBR outperformed, considering the accuracy, mean square error, and R ² score. We also used Explainable Artificial Intelligence (XAI), Local Interpretable Model (LIME), and SHapley Additive exPlanations (SHAP) to determine software fault features. We observed that Number of static invocations ( nosi ), Depth Inheritance Tree ( dit ), and Coupling Between Objects ( cbo ) features are the most affected software faults feature from datasets. For LIME, the average True positive of nosi is 40%, dit is 15%, and cbo is 20%; on the other hand, the SHAP average true positive value of nosi is 36%, cbo is 15%, and the norm true negative value of dit is 5%. Thus, LIME can afford the greatest impact on the model outcomes to identify features that are the most significant reasons for software defects.

Software defect identification (SDI) is a key part of improving the quality of software projects and lowering the risks that along with maintenance. It does identify the software defect causes that have not been reached yet to get sufficient results. On the other hand, many researchers have recently developed several models, including NN, ML, DL, advanced CNN, and LSTM, to enhance the effectiveness of defect prediction. Due to an insufficient dataset size, repeated investigations, and no longer appropriate baseline selection, the research on the CNN model was unable to produce reliable results. In addition, XAI a well-known explainability approach creates deep models in computer vision, as well as successfully handles the software defect prediction that is easy for humans to understand. To address these issues, firstly we have used SMOTE for preprocessing which was collected from the NASA repository; categorical and numerical data. Secondly, we have experimented with software defect prediction using 1D-CNN and 2D-CNN named lightweight CNN (LCNN). Subsequently, evaluation we have employed a 100-repetition holdout validation. For the cross-validation setup, we utilized the 1D-CNN model was $20\times 1$ , and for the 2D-CNN model, it was $4\times 5 \times 1$ . After that, the results of the experiment were compared and assessed in terms of accuracy, MSE, and AUC. The result shows that 2D-CNN shows 1.36% better contrast with 1D-CNN. Thirdly, we have conducted research on the identification of software defect features via LIME and SHAP in XAI stand as state-of-the-art techniques. However, we cannot use 2D-CNN because it involves more complex relationships, making it challenging to create transparent explanations. That is why we have realized that 1D-CNN will superior result to explain the root cause of software feature identifications. Finally, LIME provides accurate visualization of software defect features in contrast with SHAP, as well as it helps the stakeholders of the software industry easily find actual root causes of software defect identification.

Outlier reduction is crucial in computer science for improving data quality, analysis accuracy, and modeling robustness. Selection and modification of DBSCAN parameters are essential for optimal clustering accuracy and outlier detection. We developed an adaptive technique to minimize outliers in the DBSCAN algorithm using a linear congruential method (LCM) to determine values of Epsilon (Eps) and Min-Points (MinPts), known as modified DBSCAN (M-DBSCAN). To enhance the DBSCAN method, we create integer random numbers for MinPts (1--100) and floating numbers for Eps (0.1--1.5) using LCM. We adjusted parameter lists to reduce outliers based on MinPts and Eps values. We choose parameters based on dataset features and requirements, balancing clustering sensitivity and noise treatment. For experiment result analysis we use the Silhouette Score (SS) method. M-DBSCAN improved all cases and it has 50% poorer outlier accuracy than DBSCAN.

Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.

Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. In recent years, many researchers have introduced several models, including neural networks (NNs), machine learning (ML), and deep learning (DL), to identify the impact on student academic performance using a socimedevice. Here, we propose a comparative model named the MLRec model, where we assess how well different machine learning methods predict the dynamics of student life and provide a recommendation to society, parents, and academic advisors. Here, we have preprocessed our real dataset by various methods, which is collected from 10 schools and has 25 features totaling 275 instances from different districts of Bangladesh. After that, we applied 15 ML algorithms for training and testing. Then, we compared the algorithms using criteria such as accuracy, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R²), Explained Variance (EV), and Tweedie Deviance Score (D²). Subsequently, we selected the Extra Tree Classifier (ETC) algorithm based on its superior performance, achieving an accuracy of 86%, an MSE of 25%, and an EV of 40%. We also used Explainable AI (LIME and SHAP) techniques to visualize the root causes of social networks’ effects on students’ school performance. Our results show that using social media excessively adversely affects academic pursuits.

The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.