Darknet is commonly known as the epicenter of illegal online activities. An analysis of darknet traffic is essential to monitor real-time applications and activities running over the Darknet. Recognizing network traffic bound to unused Internet addresses has become undeniably significant for identifying and examining malicious activities on the internet. Since there are no authentic hosts or devices in an unused address block, any observed network traffic must be the aftereffect of misconfiguration from spoofed source addressed and other frameworks that monitor unused address space. However, the recent advancements in artificial intelligence allow digital systems to detect and identify darknet traffic autonomously. In this paper, we propose a generalized approach for darknet traffic detection and categorization using Deep Learning. We examine the state-of-the-art complex dataset, which provides excessive information about the darknet traffic and perform data preprocessing. Next, we analyze diverse feature selection techniques to select optimal features for darknet traffic detection and categorization. We apply fine-tuned machine learning (ML) algorithms which include Decision Tree (DT), Gradient Boosting (GB), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGB) on selected features and compare the performance. Next, we apply modified Convolution-Long Short-Term Memory (CNN-LSTM) and Convolution-Gradient Recurrent Unit (CNN-GRU) deep learning techniques to recognize the network traffic more accurately. The results demonstrate that the proposed approach outperforms the existing approaches by yielding the maximum accuracy of 96% of darknet traffic detection and 89% of darknet traffic categorization through XGB as a feature selection approach and CNN-LSTM a recognition model.

The success of data mining learned rules highly depends on its actionability: how useful it is to perform suitable actions in any real business environment. To improve rule actionability, different researchers have initially presented various Data Mining (DM) frameworks by focusing on different factors only from the business domain dataset. Afterward, different Domain-Driven Data Mining (D3M) frameworks were introduced by focusing on domain knowledge factors from the context of the overall business environment. Despite considering these several dataset factors and domain knowledge factors in different phases of their frameworks, the learned rules still lacked actionability. The objective of our research is to improve the learned rules’ actionability. For this purpose, we have analyzed: (1) what overall actions or tasks are being performed in the overall business process, (2) in which sequence different tasks are being performed, (3) under what certain conditions these tasks are being performed, (4) by whom the tasks are being performed (5) what data is provided and produced in performing these tasks. We observed that the inclusion of rule learning factors only from dataset or from domain knowledge is not sufficient. Our Process-based Domain-Driven Data Mining-Actionable Knowledge Discovery (PD3M-AKD) framework explains its different phases to consider and include additional factors from five perspectives of the business process. This PD3M-AKD framework is also in line with the existing phases of current DM and D3M frameworks for considering and including dataset and domain knowledge accordingly. Finally, we evaluated and validated our case study results from different real-life scenarios from education, engineering, and business process domains at the end.

Autism spectrum disorder is an inherited long‐living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches.

Predictive analytics has become an essential area of research in health informatics. The availability of multi-source and multi-modal data in healthcare has made the disease prediction, diagnosis, and medication process more effective and reliable. However, the analysis and decision making have become challenging task, particularly when data is in multiple formats and from different sources. In this study, different frameworks have been proposed to handle multi-nature data at different levels for predictive analytics. Dimensionality reduction techniques have been applied to extract relevant features to enhance the analysis. To improve the performance of predictive analytics at different fusion levels, the potential benefits of multi-modal data have been discussed. Moreover, notable improvement in prediction accuracy has been observed through experimental evaluation of the proposed frameworks. Furthermore, the issues which have been found during dimension reduction and fusion approaches have also been highlighted.

Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel‐dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus‐growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network‐based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.

Time series analysis is a critical task across various scientific and industrial domains, enabling the extraction of valuable insights from temporal data. High dimensionality of time series data can lead to computational inefficiencies and increased complexity. Dimensionality reduction techniques play a crucial role in handling the high-dimensional nature of time series data essential information while reducing computational complexity. This study explores the impact of various dimensionality reduction techniques and machine learning models on enhancing the accuracy and efficiency of time series datasets. The effectiveness of different dimensionality reduction methods is evaluated based on their ability to simplify data while preserving essential features. Subsequently, several machine learning models—such as Autoregressive Integrated Moving Average, k-Nearest Neighbors, Random Forest, Least Absolute Shrinkage and Selection Operator, and Extreme Gradient Boosting are applied to the transformed data. The findings reveal that the choice of dimensionality reduction technique significantly influences the performance of these models. Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. These results highlight the importance of selecting an appropriate combination of dimensionality reduction and machine learning techniques based on the specific characteristics of the time series data. This can contributes to a deeper understanding of how these methods can be effectively integrated, thereby enhancing decision-making in areas such as finance, meteorology, and operational planning.