Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC's biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.

Neurodegenerative conditions are defined by the progressive deterioration and death of nerve cells in the core neural system. Most neurodegenerative conditions are not curable. While there have been significant improvements and techniques used to treat these diseases early diagnosis continues to play a crucial role in the entire approach. Conditions are often diagnosed only once they start negatively impacting the daily life of those affected. Early detection and timely preventative treatment can help improve patient subjective well-being. This study examines the application of a non-invasive gait analysis technique for the detection of Parkinson’s disease. Publicly available data collected from patients suffering from Parkinson’s along with control groups is utilized and combined with long-short-term neural networks to construct models capable of detecting signs on Parkinson’s disorder. However, because of the significant reliance of models on appropriate parameters selection, metaheuristic algorithms are used to fine tune the selection process, and a modified variation of the strongly founded PSO algorithm was proposed. Several contemporary optimizers are compared based on their ability to optimize model performance. This suggested approach achieved the superior outcomes with an accuracy of 89.92%. The constructed models have been evaluated to determine feature importance using game theory based methods.

Abstract The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade‐off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer‐aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper‐parameters and training to deliver a desired result. This paper proposes a swarm intelligence‐based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification‐based mechanism, has been proposed and applied to both feature selection and hyper‐parameter optimization of two machine learning models—XGBoost and extreme learning machine. The proposed approach has been tested on four real‐world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT‐IoT, and UNSW‐NB 15. Achieved results have been compared to the results of eight other cutting‐edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics—accuracy, precision, recall, and f1‐score.

Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two-layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cutting-edge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.

Abstract This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ( $$R^2$$ R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach.

Energy forecasting plays an important role in effective power grid management. The widespread adoption of emerging technologies and the increased reliance on renewable sources of energy have created a need for a robust and accurate system for energy forecasting. This demand is becoming increasingly relevant due to the ongoing 2022 energy crisis. Modern power systems are very complex with many complicated correlations between various forecasting factors and parameters. Furthermore, renewable energy is often dependent on weather conditions, which complicates the process of forecasting. This work presents a novel artificial intelligence (AI) driven energy forecasting tuned deep learning framework. By formatting predictors as a time series, two variations of recurrent neural networks (RNN)s have been implemented: long-short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. However, both approaches present several hyperparameters that require adequate tuning to attain desirable performance. Therefore, this work also proposes an improved version of a well know swarm intelligence algorithm, the sine cosine algorithm (SCA), tasked with tackling hyperparameter tuning for both approaches. To demonstrate the improvements made, three datasets have been constructed for evaluation from publicly available real-world data that contain relevant solar, wind, and power-grid load parameters alongside weather data. The proposed metaheuristic algorithm has been subjected to a comparative analysis with several contemporary metaheuristic algorithms to showcase the improvements made. The introduced metaheuristic demonstrated the best performance with a mean square error (MSE) rate for solar generation of only 0.0132 with LSTM methods and 0.0134 with GRU. Similar performance was observed for wind power generation forecasting with a MSE of 0.00292 with LSTM and 0.00287. When tackling power grid load forecasting a median MSE of 0.0162 was attained with LSTM and 0.01504 with GRU. Therefore there is great potential for tackling these tasks using the proposed approach. The best-performing models have been analyzed using SHapley Additive exPlanations (SHAP) to determine the factors that have the highest influence on energy generation and demand.

In the last few decades, the World Wide Web has become a necessity that offers numerous services to end users. The number of online transactions increases daily, as well as that of malicious actors. Machine learning plays a vital role in the majority of modern solutions. To further improve Web security, this paper proposes a hybrid approach based on the eXtreme Gradient Boosting (XGBoost) machine learning model optimized by an improved version of the well-known metaheuristics algorithm. In this research, the improved firefly algorithm is employed in the two-tier framework, which was also developed as part of the research, to perform both the feature selection and adjustment of the XGBoost hyper-parameters. The performance of the introduced hybrid model is evaluated against three instances of well-known publicly available phishing website datasets. The performance of novel introduced algorithms is additionally compared against cutting-edge metaheuristics that are utilized in the same framework. The first two datasets were provided by Mendeley Data, while the third was acquired from the University of California, Irvine machine learning repository. Additionally, the best performing models have been subjected to SHapley Additive exPlanations (SHAP) analysis to determine the impact of each feature on model decisions. The obtained results suggest that the proposed hybrid solution achieves a superior performance level in comparison to other approaches, and that it represents a perspective solution in the domain of web security.

A Boolean function$f:\{0,1\}^{n}\mapsto \{0,1\}$is a monotone Boolean function (MBF) of$n$variables if for each pair of vectors$x,y\in \{0,1\}^{n}$from$x\leqslant y$follows$f(x)\leqslant f(y)$. Two MBFs are considered equivalent if one of them can be obtained from the other by permuting the input variables. Let$d_{n}$be the number of MBFs of$n$variables (which is known as Dedekind number) and let$r_{n}$be a number of non-equivalent MBFs of$n$variables. The numbers$d_{n}$and$r_{n}$have been so far calculated for$n\leqslant 8$, and$n\leqslant 7$, respectively. This paper presents the calculation of$r_{8}=1 392 195 548 889 993 358$. Determining Dedekind numbers and$r_{n}$is a long-standing problem.

The number Unof equivalence classes of Boolean functions of n variables and the number Vnof equivalence classes of vectorial Boolean functions of n variables under the action of four groups of transformations are considered. The four groups are the group Sn' of permutations of variables, the group Gnof permutations and complementations, the linear group GL(n, 2) and the affine group AGL(n, 2). Harrison obtained cycle indexes for these groups and the expressions for Unand Vnin terms of the corresponding cycle index. He also tabulated the numbers Un, Vnand the cycle indexes for n≤6 for Sn' and Gn, and for n≤5 for GL(n, 2) and AGL(n, 2). This bound was only recently slightly exceeded. Fripertinger implemented computation of cycle indexes for GL(n, q) and AGL(n, q); if q = 2 this implementation works for about n 5≤21. By introducing appropriate precomputed tables, we reduced the cycle index computation to evaluation of a sum over partitions of n for all the four groups. Using this more efficient procedure, we obtained values of Un, Vnand the explicit cycle index expressions for larger values of n.

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications including military, environment monitoring, surveillance and body area sensor networks. One of the biggest challenges each wireless sensor network has to handle is the network lifetime maximization. To achieve this, numerous clustering algorithms have been created, with the goal to improve energy consumption throughout the network by balancing the energy consumption overall nodes. All clustering algorithms incorporate load balancing to achieve energy efficiency. One of the basic and most important algorithms in use is LEACH. Swarm intelligence metaheuristics have already been applied in solving numerous problems of wireless sensor networks, including lifetime optimization, localization and many other NP hard problems with promising results, as can be seen in the literature overview. In the research proposed in this paper, an improved version of the firefly algorithm has been applied to improve the network lifetime. The firefly algorithm was used to help in forming the clusters and selection of the cluster head. Additionally, we have evaluated the performance of the improved firefly algorithm by comparing it to the LEACH, basic firefly algorithm and particle swarm optimization, that were all tested on the same network infrastructure model. Conducted simulations have proven that our proposed metaheuristic achieves better and more consistent performance than other algorithms.

In this paper we describe a virtual 3D lab created using Open Wonderland to enable simultaneous execution of different computer science simulator modules in a collaborative, immersive workspace. Simulators were divided into functional modules that can be reused and combined for different teaching topics based on statistics of their usage. We discuss some important issues in our platform setup, describe our learning environment and logging module we developed in order to track system usage. Preliminary usability testing confirms the efficiency of this approach.