137 research outputs found
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
DĂ©tection des Ă©carts de tendance et analyse prĂ©dictive pour le traitement des flux dâĂ©vĂ©nements en temps rĂ©el
Les systĂšmes dâinformation produisent diffĂ©rents types de journaux dâĂ©vĂ©nements. Les donnĂ©es historiques contenues dans les journaux dâĂ©vĂ©nements peuvent rĂ©vĂ©ler des informations importantes sur lâexĂ©cution dâun processus mĂ©tier. Le volume croissant de ces donnĂ©es collectĂ©es, pour ĂȘtre utile, doit ĂȘtre traitĂ© afin dâextraire des informations pertinentes. Dans de nombreuses situations, il peut ĂȘtre souhaitable de rechercher des tendances dans ces journaux. En particulier, les tendances calculĂ©es par le traitement et lâanalyse de la sĂ©quence dâĂ©vĂ©nements gĂ©nĂ©rĂ©s par plusieurs instances du mĂȘme processus servent de base pour produire des prĂ©visions sur les exĂ©cutions actuelles du processus. Lâobjectif de cette thĂšse est de proposer un cadre gĂ©nĂ©rique pour lâanalyse des tendances sur ces flux dâĂ©vĂ©nement, en temps rĂ©el. En premier lieu, nous montrons comment des tendances de diffĂ©rents types peuvent ĂȘtre calculĂ©es sur des journaux dâĂ©vĂ©nements en temps rĂ©el, Ă lâaide dâun cadre gĂ©nĂ©rique appelĂ© workflow de distance de tendance. De multiples calculs courants sur les flux dâĂ©vĂ©nements sâavĂšrent ĂȘtre des cas particuliers de ce flux de travail, selon la façon dont diffĂ©rents paramĂštres de flux de travail sont dĂ©finis. La suite naturelle de lâanalyse statique des tendances est lâusage des algorithmes dâapprentissage. Nous joignons alors les concepts de traitement de flux dâĂ©vĂ©nements et dâapprentissage automatique pour crĂ©er un cadre qui permet le calcul de diffĂ©rents types de prĂ©dictions sur les journaux dâĂ©vĂ©nements. Le cadre proposĂ© est gĂ©nĂ©rique : en fournissant diffĂ©rentes dĂ©finitions Ă une poignĂ©e de fonctions dâĂ©vĂ©nement, plusieurs types de prĂ©dictions diffĂ©rents peuvent ĂȘtre calculĂ©s Ă lâaide du mĂȘme flux de travail de base. Les deux approches ont Ă©tĂ© mises en oeuvre et Ă©valuĂ©es expĂ©rimentalement en Ă©tendant un moteur de traitement de flux dâĂ©vĂ©nements existant, appelĂ© BeepBeep. Les rĂ©sultats expĂ©rimentaux montrent que les Ă©carts par rapport Ă une tendance de rĂ©fĂ©rence peuvent ĂȘtre dĂ©tectĂ©s en temps rĂ©el pour des flux produisant jusquâĂ des milliers dâĂ©vĂ©nements par seconde
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
Infectious diseases remain among the top contributors to human illness and
death worldwide, among which many diseases produce epidemic waves of infection.
The unavailability of specific drugs and ready-to-use vaccines to prevent most
of these epidemics makes the situation worse. These force public health
officials and policymakers to rely on early warning systems generated by
reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics
can assist stakeholders in tailoring countermeasures, such as vaccination
campaigns, staff scheduling, and resource allocation, to the situation at hand,
which could translate to reductions in the impact of a disease. Unfortunately,
most of these past epidemics exhibit nonlinear and non-stationary
characteristics due to their spreading fluctuations based on seasonal-dependent
variability and the nature of these epidemics. We analyse a wide variety of
epidemic time series datasets using a maximal overlap discrete wavelet
transform (MODWT) based autoregressive neural network and call it EWNet model.
MODWT techniques effectively characterize non-stationary behavior and seasonal
dependencies in the epidemic time series and improve the nonlinear forecasting
scheme of the autoregressive neural network in the proposed ensemble wavelet
network framework. From a nonlinear time series viewpoint, we explore the
asymptotic stationarity of the proposed EWNet model to show the asymptotic
behavior of the associated Markov Chain. We also theoretically investigate the
effect of learning stability and the choice of hidden neurons in the proposal.
From a practical perspective, we compare our proposed EWNet framework with
several statistical, machine learning, and deep learning models. Experimental
results show that the proposed EWNet is highly competitive compared to the
state-of-the-art epidemic forecasting methods
Efficiency and Optimization of Buildings Energy Consumption: Volume II
This reprint, as a continuation of a previous Special Issue entitled âEfficiency and Optimization of Buildings Energy Consumptionâ, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption
Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective
Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for both short-term and long-term forecasting? How can a denoising mechanism enhance prediction accuracy? How can robust machine learning models be built with limited data? How can out-of-distribution traffic data be used to improve the generalizability of prediction models? Based on extensive experiments, we propose a novel traffic forecast/prediction framework and associated models that integrate outlier management and noise reduction strategies, outperforming traditional machine learning models. Additionally, we suggest a transfer learning-based framework combined with a data augmentation technique to provide robust solutions with smaller datasets. Lastly, we propose a hybrid model with signal decomposition techniques to enhance model generalization for out-of-distribution data samples. We also brought the issue of cyber threats as part of our forecast research, acknowledging their substantial influence on traffic unpredictability and forecasting challenges. Our thesis presents a detailed exploration of cyber-attack detection, employing methods that have been validated using multiple benchmark datasets. Initially, we incorporated ensemble feature selection with ensemble classification to improve DDoS (Distributed Denial-of-Service) attack detection accuracy with minimal false alarms. Our research further introduces a stacking ensemble framework for classifying diverse forms of cyber-attacks. Proceeding further, we proposed a weighted voting mechanism for Android malware detection to secure Mobile Cyber-Physical Systems, which integrates the mobility of various smart devices to exchange information between physical and cyber systems. Lastly, we employed Generative Adversarial Networks for generating flow-based DDoS attacks in Internet of Things environments. By considering the impact of cyber-attacks on traffic volume and their challenges to traffic prediction, our research attempts to bridge the gap between traffic forecasting and cyber security, enhancing proactive management of networks and contributing to resilient and secure internet infrastructure
Stock price predictive analysis : An application of hybrid barnacles mating optimizer with artiïŹcial neural network
Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next dayâ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%
Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments
Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attackâs influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments
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