In textile industries, a lot of wastewater are discharged which are one of the major environmental pollution problems, because they release undesirable dye effluents. Owing to re-dyeing procedures performed to meet customized color specifications, environmental pollution is a serious problem because of the emission of large volumes of wastewater. To solve the environmental problems caused by re-dyeing, the right-first-time (RFT) %, which is the rate at which the target quality is obtained with just one dyeing, must be increased by considering the dyeing conditions that affect product quality. Here, this study suggests a framework for cleaner production of textile dyeing process using novel exhaustion-rate meter (NERM) and multi-layer perceptron-based prediction model to solve the environmental problems caused by re-dyeing procedure by controlling the exhaustion-rate outliers. The proposed NERM measures the exhaustion-rate based on absorbance of the dyeing solution and is composed of measuring and analysis section. The dyeing solution absorbance is metered in the measuring component through a detector, which performs high-resolution measurement (0.3–1.5 nm full width at half maximum) via a 25-μm slit in the 200–1100-nm wavelength range; the absorbance is then converted to the exhaustion-rate based on Beer's law in the analysis section. Using the NERM, an exhaustion rate dataset according to the Na2SO4 and Na2CO3 consumption is acquired and a surrogate model that augments the exhaustion rate data is developed. The MLP-based prediction model is then developed using the augmented data to control the real-time exhaustion-rate outliers. As a results, the model performance as regards Na2SO4 and Na2CO3 prediction is indicated by R2 values of approximately 0.985 and 0.998, respectively, and root mean squared errors (RMSE) of approximately 1.477 and 1.000, respectively. In addition, the effectiveness of the proposed framework is demonstrated through application to several scenarios in which the real-time exhaustion rate outliers are detected.

In textile industries, a lot of wastewater are discharged which are one of the major environmental pollution problems, because they release undesirable dye effluents. Owing to re-dyeing procedures performed to meet customized color specifications, environmental pollution is a serious problem because of the emission of large volumes of wastewater. To solve the environmental problems caused by re-dyeing, the right-first-time (RFT) %, which is the rate at which the target quality is obtained with just one dyeing, must be increased by considering the dyeing conditions that affect product quality. Here, this study suggests a framework for cleaner production of textile dyeing process using novel exhaustion-rate meter (NERM) and multi-layer perceptron-based prediction model to solve the environmental problems caused by re-dyeing procedure by controlling the exhaustion-rate outliers. The proposed NERM measures the exhaustion-rate based on absorbance of the dyeing solution and is composed of measuring and analysis section. The dyeing solution absorbance is metered in the measuring component through a detector, which performs high-resolution measurement (0.3–1.5 nm full width at half maximum) via a 25-μm slit in the 200–1100-nm wavelength range; the absorbance is then converted to the exhaustion-rate based on Beer's law in the analysis section. Using the NERM, an exhaustion rate dataset according to the Na2SO4 and Na2CO3 consumption is acquired and a surrogate model that augments the exhaustion rate data is developed. The MLP-based prediction model is then developed using the augmented data to control the real-time exhaustion-rate outliers. As a results, the model performance as regards Na2SO4 and Na2CO3 prediction is indicated by R2 values of approximately 0.985 and 0.998, respectively, and root mean squared errors (RMSE) of approximately 1.477 and 1.000, respectively. In addition, the effectiveness of the proposed framework is demonstrated through application to several scenarios in which the real-time exhaustion rate outliers are detected.

Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on a strongly induced subhypergraph or edge cardinality, overlooking the importance of the frequency of co-occurrence. In this paper, we propose a new cohesive subgraph named (k,g)-core, which considers both neighbour and co-occurrence simultaneously. The (k,g)-core has various applications including recommendation system, network analysis, and fraud detection. To the best of our knowledge, this is the first work to combine these factors. We extend an existing efficient algorithm to find solutions for (k,g)-core. Finally, we conduct extensive experimental studies that demonstrate the efficiency and effectiveness of our proposed algorithm.

This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, our approach directly detects and visualizes abnormal movements from CCTV videos, particularly in narrow coastal areas. We evaluate the proposed method using real-world CCTV video data and demonstrate its effectiveness in detecting abnormal ship movements, offering promising results in real-world scenarios. The findings of this study have important implications to improve maritime safety and prevent accidents.