Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1795 - e1795

Опубликована: Янв. 18, 2024

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution the ever-increasing demands of world. However, shift toward renewable is not without challenges. While reliable means storage that can be converted into usable energy, are dependent on external factors used for generation. Efficient often relying batteries have limited number charge cycles. A robust efficient system forecasting power generation from sources help alleviate some difficulties associated with transition energy. Therefore, this study proposes attention-based recurrent neural network approach generated sources. To networks make accurate forecasts, decomposition techniques utilized applied time series, modified metaheuristic introduced optimized hyperparameter values networks. This has been tested two real-world datasets covering both solar wind farms. The models by metaheuristics were compared those produced other state-of-the-art optimizers terms standard regression metrics statistical analysis. Finally, best-performing model was interpreted using SHapley Additive exPlanations.

Язык: Английский

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Ethics and responsible AI deployment

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Март 27, 2024

As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for AI systems safeguard individual while complying with standards. By taking multidisciplinary approach, research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and guidelines. The study concludes these algorithms effectively enhance protection balancing utility of to protect data. emphasises importance comprehensive approach combines technological innovation strategies harness power in way respects protects privacy.

Язык: Английский

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IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm

Electronics, Год журнала: 2024, Номер 13(6), С. 1053 - 1053

Опубликована: Март 12, 2024

The most significant threat that networks established in IoT may encounter is cyber attacks. commonly encountered attacks among these threats are DDoS After attacks, the communication traffic of network can be disrupted, and energy sensor nodes quickly deplete. Therefore, detection occurring great importance. Considering numerous network, analyzing data through traditional methods become impossible. Analyzing this a big environment necessary. This study aims to analyze obtained dataset detect using deep learning algorithm. conducted PySpark with Apache Spark Google Colaboratory (Colab) environment. Keras Scikit-Learn libraries utilized study. ‘CICIoT2023’ ‘TON_IoT’ datasets used for training testing model. features reduced correlation method, ensuring inclusion tests. A hybrid algorithm designed one-dimensional CNN LSTM. developed method was compared ten machine algorithms. model’s performance evaluated accuracy, precision, recall, F1 parameters. Following study, an accuracy rate 99.995% binary classification 99.96% multiclassification achieved dataset. In dataset, success 98.75% reached.

Язык: Английский

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The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere

The Science of The Total Environment, Год журнала: 2024, Номер 929, С. 172195 - 172195

Опубликована: Апрель 15, 2024

Язык: Английский

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A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 25, 2025

Breast cancer is one of the most aggressive types cancer, and its early diagnosis crucial for reducing mortality rates ensuring timely treatment. Computer-aided systems provide automated mammography image processing, interpretation, grading. However, since currently existing methods suffer from such issues as overfitting, lack adaptability, dependence on massive annotated datasets, present work introduces a hybrid approach to enhance breast classification accuracy. The proposed Q-BGWO-SQSVM utilizes an improved quantum-inspired binary Grey Wolf Optimizer combines it with SqueezeNet Support Vector Machines exhibit sophisticated performance. SqueezeNet's fire modules complex bypass mechanisms extract distinct features images. Then, these are optimized by Q-BGWO determining best SVM parameters. Since current CAD system more reliable, accurate, sensitive, application advantageous healthcare. was evaluated using diverse databases: MIAS, INbreast, DDSM, CBIS-DDSM, analyzing performance regarding accuracy, sensitivity, specificity, precision, F1 score, MCC. Notably, CBIS-DDSM dataset, achieved remarkable results at 99% 98% 100% specificity in 15-fold cross-validation. Finally, can be observed that designed model excellent, potential realization other datasets imaging conditions promising. novel outperforms state-of-the-art offers accurate reliable detection, which essential further healthcare development.

Язык: Английский

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Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations

Applied Soft Computing, Год журнала: 2023, Номер 146, С. 110659 - 110659

Опубликована: Июль 29, 2023

Язык: Английский

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Marine Vessel Classification and Multivariate Trajectories Forecasting Using Metaheuristics-Optimized eXtreme Gradient Boosting and Recurrent Neural Networks

Applied Sciences, Год журнала: 2023, Номер 13(16), С. 9181 - 9181

Опубликована: Авг. 11, 2023

Maritime vessels provide a wealth of data concerning location, trajectories, and speed. However, while these are meticulously monitored logged to maintain course, they can also meta information. This work explored the potential data-driven techniques applied artificial intelligence (AI) tackle two challenges. First, vessel classification was through use extreme gradient boosting (XGboost). Second, trajectory time series forecasting tackled long-short-term memory (LSTM) networks. Finally, due strong dependence AI model performance on proper hyperparameter selection, boosted version well-known particle swarm optimization (PSO) algorithm introduced specifically for tuning hyperparameters models used in this study. The methodology real-world automatic identification system (AIS) both marine forecasting. Boosted PSO (BPSO) compared contemporary optimizers showed promising outcomes. XGBoost tuned using attained an overall accuracy 99.72% problem, LSTM mean square error (MSE) 0.000098 prediction challenge. A rigid statistical analysis performed validate outcomes, explainable principles were determined best-performing models, gain better understanding feature impacts decisions.

Язык: Английский

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Parkinson’s Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm

Опубликована: Янв. 18, 2024

Parkinson's disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way curing damage caused by disease, patient's life quality improved diagnosed and properly on time. The role artificial intelligence (AI) in medicine increasing. Deep learning algorithms may utilized automatically detect freezing gait episodes. This study focused diagnosis based disturbances which affected this disease. A hybrid deep machine AI solution employs gated recurrent unit (GRU) neural network optimized a swarm between crayfish optimization algorithm firefly has been proposed. proposed compared other high-performing establish objective grounds for comparison. framework results overall best performance confirms made improvements. best-constructed model attained an accuracy 87.08%.

Язык: Английский

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AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(6), С. 3585 - 3617

Опубликована: Март 26, 2024

Язык: Английский

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Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 14, 2025

Smart devices are enabled via the Internet of Things (IoT) and connected in an uninterrupted world. These pose a challenge to cybersecurity systems due attacks network communications. Such have continued threaten operation end-users. Therefore, Intrusion Detection Systems (IDS) remain one most used tools for maintaining such flaws against cyber-attacks. The dynamic multi-dimensional threat landscape IoT increases Traditional IDS. focus this paper aims find key features developing IDS that is reliable but also efficient terms computation. Enhanced Grey Wolf Optimization (EGWO) Feature Selection (FS) implemented. function EGWO remove unnecessary from datasets intrusion detection. To test new FS technique decide on optimal set based accuracy achieved feature taking filters, recent approach relies NF-ToN-IoT dataset. selected evaluated by using Random Forest (RF) algorithm combine multiple decision trees create accurate result. experimental outcomes procedures demonstrate capacity recommended classification methods determine Analysis results presents performs more effectively than other techniques with optimized (i.e., 23 out 43 features), high 99.93% improved convergence.

Язык: Английский

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