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.