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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities
With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD technique exhibited a superior accuracy value of 99.47% over existing models under diverse measures.
Journal Article
Walrus : tusk, tusk
Introduces the physical characteristics, behavior, habitat, and migration of walruses, sea mammals that have flippers and tusks.
Book
Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response
Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure efficient resource management and operation. One of the primary challenges in managing ME-MGs is reducing operational costs and emissions while addressing uncertainties. This study investigates the optimization and energy management (EM) in ME-MGs through the application of the Multi-Objective Walrus Optimization Algorithm (MOWaOA) combined with fuzzy decision-making techniques. The main objective of the research is to minimize operational costs and emissions in the face of uncertain conditions. To achieve this goal, multiple scenarios were analyzed, including EM without considering demand response and electric vehicles, EM with the inclusion of these factors, and EM under uncertain conditions. The results demonstrated that integrating electric vehicles and demand response into microgrid EM led to a 15.6% reduction in operational costs and a 12.8% decrease in emissions compared to scenarios where these factors were excluded. Furthermore, when uncertainties were accounted for, operational costs increased by 2.1% and emissions rose by 1.2%. This increase emphasizes the significance of employing more precise management techniques and advanced strategies to effectively address uncertainties in ME-MGs.
Journal Article
Enhancing load frequency control and automatic voltage regulation in Interconnected power systems using the Walrus optimization algorithm
This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.
Journal Article
Exploring the world of seals and walruses
Introduces the physical characteristics, family life, and behaviors of seals and walruses.
Book
Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women
Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Therefore, the primary objective of this study is to design an expert machine learning (ML) model for the early diagnosis of PCOS based on initial symptoms and health indicators; two datasets were amalgamated and preprocessed to accomplish this goal, resulting in a new symptomatic dataset with 12 attributes. An ensemble learning (EL) model, with seven base classifiers, and a deep learning (DL) model, as the meta-level classifier, are proposed. The hyperparameters of the EL model were optimized through the nature-inspired walrus optimization (WaO), cuckoo search optimization (CSO), and random search optimization (RSO) algorithms, leading to the WaOEL, CSOEL, and RSOEL models, respectively. The results obtained prove the supremacy of the designed WaOEL model over the other models, with a PCOS prediction accuracy of 92.8% and an area under the receiver operating characteristic curve (AUC) of 0.93; moreover, feature importance analysis, presented with random forest (RF) and Shapley additive values (SHAP) for positive PCOS predictions, highlights crucial clinical insights and the need for early intervention. Our findings suggest that patients with features related to obesity and high cholesterol are more likely to be diagnosed as PCOS positive. Most importantly, it is inferred from this study that early PCOS identification without expensive tests is possible with the proposed WaOEL, which helps clinicians and patients make better informed decisions, identify comorbidities, and reduce the harmful long-term effects of PCOS.
Journal Article
Companion to the major motion picture Arctic tale
Follows the lives of Nanu the polar bear and Seela the walrus.
Book
A quasi-opposition learning and chaos local search based on walrus optimization for global optimization problems
The Walrus Optimization (WO) algorithm, as an emerging metaheuristic algorithm, has shown excellent performance in problem-solving, however it still faces issues such as slow convergence and susceptibility to getting trapped in local optima. To this end, the study proposes a novel WO enhanced by quasi-oppositional-based learning and chaotic local search mechanisms, called QOCWO. The study aims to prevent premature convergence to local optima and enhance the diversity of the population by integrating the quasi-oppositional-based learning mechanism into the original Walrus Optimization (WO) algorithm, thereby improving the global search capability and expanding the search range. Additionally, the chaotic local search mechanism is introduced to accelerate the convergence speed of WO. To test the capabilities, the QOCWO algorithm is applied to the 23 standard functions and compared with seven other algorithms. Furthermore, the Wilcoxon rank-sum test is utilized to evaluate the significance of the results, which demonstrates the superior performance of the proposed algorithm. To assess the practicality in solving real-world problems, the QOCWO is applied to two engineering design issues, and the results indicated that QOCWO achieved lower costs compared to other algorithms.
Journal Article