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Technology and data analysis have evolved into a resource-rich tool for collecting, researching and comparing student achievement levels in the classroom. There are sufficient resources to discover student success through data analysis by routinely collecting extensive data on student behaviour and curriculum structure. Educational Data Mining (EDM), a method of data analysis in the learning environment, has emerged as an emerging trend in the development of educational data mining and analysis techniques. EDM aids in the comprehension of student behaviour as well as the factors that influence student behaviour and achievement. Student learning patterns, student culture, and instructional skills are all important factors in a successful study of EDM students. This study will look at how technology and data mining are used in the EDM environment and compare the results. We have used previous research to determine which method is best for observing the learning environment and what factors influence student academic performance. Two state-of-the-art models i.e. decision tree (classifier) and DBSCAN (clustering method) are used to predict the performance of an educational institute with higher accuracy.

Human-centered transportation (walking and biking) has been the cheapest, healthiest, and most convenient mode of transportation throughout history. In the new global economy, walking and biking have become common modes of transportation for low-income groups of people. Kabul is the biggest city in Afghanistan with scattered space organizations and currently is unfavorable for walking and biking due to insufficient attention to pedestrian and bicycle routes in city planning and poor road network and sidewalk conditions, which are among the issues that affect this 4-5 million population city. The purpose of this research is to analyze the current traffic situation in Kabul and identify the role and share of citizens' use of human-centered transportation (walking and biking) for transportation. This research also aims to investigate the relationship between the economic scope of low-income workers and the use of walking and biking for transportation. The statistical population of the current study was selected from three municipal districts as travel zones. Using cluster sampling, a sample participant of 929 people was obtained. It was observed that in the broad context, due to increasing cost and insufficient public transportation, low-income workers use bicycles and walking as a reliable mode of transportation. Finally, it is suggested that the spatial organization of Kabul is redefined and designed based on the new space organization, and the local organization and formulation of urban transportation strategies in urban strategic plans for pedestrian and bicycle transportation systems are strengthened, especially for roads leading to employment locations. Furthermore, in planning, priority is to be shifted to human-centered transportation (walking and biking).

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications. [ABSTRACT FROM AUTHOR]

Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to twodimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks. [ABSTRACT FROM AUTHOR]

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research. [ABSTRACT FROM AUTHOR]

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research. [ABSTRACT FROM AUTHOR]

This case report shows that pleural empyema limits the diagnostic significance of imaging techniques. Hereafter, we present the case of an 82-year-old patient with primary pericardial mesothelioma, which was veiled by a pleural empyema. The patient met the typical triad of signs of heart failure (dyspnea, lower leg oedema), pericardial effusion, and pericarditis. Echocardiography in the identification of pericardial mesotheliomas is low. In this case, the cardiac function could be imaged well, but the tumor could not be imaged. The CT showed a pericardial effusion and a pleural effusion. Here, the tumor could not be diagnosed either. Only the operation led to diagnosis. [ABSTRACT FROM AUTHOR]