Lithology classification is crucial for efficient and sustainable resource exploration in the oil and gas industry. Missing values in well-log data, such as Gamma Ray (GR), Neutron Porosity (NPHI), Bulk Density (RHOB), Deep Resistivity (RS), Delta Time Compressional (DTCO), Delta Time Shear (DTSM), and Resistivity Deep (RD), significantly affect machine learning classification accuracy. This study applied three algorithms, extreme gradient boosting (XGBoost), K-nearest neighbours (KNN), and the artificial neural network (ANN), to handle missing values in well-log datasets, particularly datasets with extreme missing data (30 %). Results indicated that XGBoost was the most efficient and accurate, especially for RHOB, NPHI, DTCO, and DTSM, with the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The ANN also performed effectively, particularly on the GR, RS, and RD features, after the use of preprocessing techniques such as isolation forest and bias correction. However, the ANN can suffer from overfitting and requires large datasets for optimal performance. In contrast, KNN struggled with missing-not-at-random (MNAR) data due to its reliance on the k parameter and distance metric, making it less effective in mapping missing data relationships. • Missing values in well-log data can hinder lithology classification accuracy for efficient resource exploration in the oil and gas industry. • This research aims to address the problem of missing values in well-log datasets by applying machine learning algorithms such as XGBoost, ANN, and KNN to enhance classification performance. • XGBoost demonstrated superior performance in handling extreme missing data (30 %) in well-log datasets. ANN was effective but prone to overfitting for small datasets, while KNN struggled with missing-not-at-random (MNAR) data due to limitations in its distance-based approach.

The detection of leaks in time series flow systems is crucial for efficient and integrated industrial processes. This is especially true when daily demand patterns differ, as this results in fluctuations in the snapshots of water consumption that are commonly used as the basis for placing sensors to detect leaks. This paper introduces a novel method in which the genetic algorithm (GA) is applied to find optimal sensor locations and to enhance the accuracy of leak detection in time series flow data. The method consists of two steps. Firstly, the GA is used to identify the optimal sensor locations using a specific fitness function that accounts for flow patterns, system topology, and leak characteristics. The novelty of the proposed method lies in the weighting scheme of the fitness function, which takes into consideration the frequency of events and the magnitude of leaks at potential locations. Secondly, the selected sensor locations are integrated with an advanced time series data analysis to locate leaks. In this technique, the most consistently performing locations are dynamically selected over time, allowing the model to adapt to varying conditions to maintain optimal sensor placement. Experiments were conducted on a simulated time series flow system with known leak scenarios to evaluate the performance of the proposed method. The results demonstrated the superiority of our GA-based sensor placement strategy in terms of leak detection accuracy and efficiency compared to other methods.•We developed a model called GA-Sense for sensor placement strategy by considering flow patterns to maximize leak detection and localization capabilities.•GA-Sense uses time series data to find strategic sensor locations to identify abnormal flow patterns indicative of leaks.•This approach enhances the accuracy and efficiency of leak detection and localization compared to alternative methods.

Video streams can come from various sources, such as surveillance cameras, live events, drones, and video-sharing platforms. Video stream mining is challenging due to the extensive resources needed to analyze and extract useful information from continuous video data streams. This situation could result in overwhelmed resources, which causes the system to stall. One of the ways to suffice the requirement is to provide larger resources, which leads to more costs. This research develops a data stream mining called the Resource-Aware Video Streaming (RAViS) framework to adapt to the limited resources (a Raspberry Pi) to run an object detection system using the YOLO algorithm. We validate the framework by capturing video streaming to simulate data streams. The video frames are processed using a deep-learning model to recognize the presence of a person(s) in a room. The RAViS framework adapts the object detection system to the availability of Raspberry Pi resources, such as CPU, RAM, and internal storage. The adaptation aims to increase the availability of resources to perform object detection of streamed video. The experimental results indicate that the RAViS framework can adapt the detection system to resource availability while maintaining accuracy.