Hybrid optimization and predictive modeling of mechanical properties in treated natural palm fibers for high-performance biocomposites

With rapid internet advancements, the impact of phishing attacks, frequency, and severity are escalating.Phishing attempts to mimic the websites of official businesses, including financial institutions, banks, government offices, and e-commerce platforms.Phishing attacks on websites target at tricking users of the internet into disclosing personal data, such as financial data, login credentials.Accordingly, enhancing an effective phishing diagnosis system is important to guarantee cryptocurrency transactions' security and reliability.The practical path in diagnosing phishing attacks is applying ML methods.For decreasing phishing attacks' diagnosis error, a 2-stage strategy is shown here.In this paper, we present a novel hybrid framework for improving phishing detection by including advanced feature preprocessing, hybrid optimization, and various attention mechanisms into deep learning models.Our method uses two benchmark datasets (PhiUSIIL and Tan) to compare the performance of LSTM, CNN, and MLP architectures.Our method uses two benchmark datasets (PhiUSIIL and Tan) to compare the performance of LSTM, CNN, and MLP architectures.We create an upgraded Hunger Games Search (HGS) algorithm to extract the most discriminating elements and integrate them with CNN-based visual representations.To improve model interpretability and focus, we use Self-Attention for LSTM (to capture sequential dependencies), Channel Attention in CNN (to detect phishingrelevant patterns in picture representations), and Channel Attention in MLP (for feature refinement).A hybrid technique is used to tune hyperparameters, combining Harris Hawks Optimization (HHO) and the Tree-structured Parzen Estimator (TPE), allowing for more robust model generalization.Our technique outperforms baseline models, achieving an accuracy of 99.95% on the PhiUSIIL dataset and 97.00% on the Tan dataset.Evaluation criteria such as F1-Score, Precision, Recall, and ROC-AUC demonstrate the suggested method's usefulness and generalizability.Our experimental results indicate significant improvements in phishing diagnostic accuracy, highlighting the efficacy of several strategies in real-world apps.

In power electronics, the alleviation of the converter losses is the major target to accomplish higher efficiency and lower thermal stress that can pave the way to lifetime enhancement of devices. Hence, this paper intends to implement a novel variable switching frequency system for switching loss minimisation in a 3-phase Voltage Source Inverter (VSI). Here, the count of commutations is minimised by varying the switching frequency over the fundamental period. Here, the switching loss is reduced by optimising the modulation index and the reference angle of VSI with a novel hybrid optimisation model. The proposed novel hybrid optimisation model is constructed by hybridising the concept of Group search Algorithm (GSO) and Rider Optimisation Algorithm (ROA) and hence referred to as Bypass updated Group search Algorithm (BU-GSO). Finally, the performance of BU-GSO is evaluated over the traditional models in terms of convergence analysis, Total Harmonic Distortion (THD) as well. Moreover, the evaluation is accomplished with inductive load variation and restive load variation, respectively.

Public transport is amongst critical infrastructures in modern cities, especially megacities, home to millions of people. The reliability of these systems is highly crucial for both citizens and service providers. If service providers overlook system reliability, a considerable amount of expenses will be wasted. Several factors such as vehicle failure, accident, lack of budget weather factors, and traffic congestion cause unreliability, among which vehicle failure plays a prominent role. The brake system is the most vulnerable and vital component of a public transportation bus. Brake reliability depends on driver’s expertise, component quality, passenger loading, line situation, etc. Driver’s expertise and components’ quality are the most important factors for brake system reliability. This study aims to implement a hybrid machine learning and optimization model to minimize the total investment and reliability-related costs in a bus rapid transit (BRT) system. A regression analysis method is proposed to capture the main attributes of a joint brake system, including the level of education, training, and drivers’ experience. The failure rate is modeled as a linear function of ETE and the quality of brake system subcomponents using a Lasso regression model. MILP optimization is then provided for optimizing the total expected costs for a bus rapid transit (BRT) system. Furthermore, a practical case is studied to investigate whether this optimization can reduce costs. The results confirm the efficiency of the hybrid optimization approach.

Hybrid Optimization Algorithm for VM Migration in Cloud Computing

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

A novel squirrel-cat optimization based optimal expansion planning for distribution system

An optimal hybrid emission control system in a multiple compliance period model

Sustainable water management through rainwater harvesting proves essential for Chennai's urban areas because they continuously face water scarcity together with flooding issues. The research presents EnviroHarvest-OptiNet as a combined optimization and predictive modeling tool that evaluates rainwater harvesting's environmental consequences. The system combines Geospatial Information System (GIS) mapping with hybrid optimization through Genetic Algorithm + Particle Swarm Optimization methods to select sites and XGBoost and LSTM machine learning algorithms for automated groundwater recharge prediction. The research analyzes ten rainwater harvesting sites that achieved an average optimization success rate of 0.97. The groundwater recharge prediction proficiently works through an ensemble model and XGBoost <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{R}^{2}=0.9652)$</tex> along with LSTM <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{R}^{2}=0.9784)$</tex> to achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}=$</tex> 0.9911. Research into centralized versus decentralized management systems demonstrates that central approaches maintain sustainability at 0.99 yet decentralized methods have a sustainability level of 0.95. A thorough environmental impact assessment outputs a sustainability score of 0.9814 which supports the effectiveness of rainwater harvesting systems for water preservation and flood management and natural ecosystem revival. Through the proposed framework policymakers obtain data-based decisions which advance their capabilities to strengthen water security and urban resilience.

To replicate marine impact forces, this thesis proposes using a rheological damper as the drop tower impact surface, leveraging its semi-active behaviour. For such application, several preparatory steps are necessary. Therefore, the scope of this work includes damper characterization, inverse model development, impact dynamics derivation, and control algorithm design. Damper characterization employs parametric and non-parametric modeling, which are applied to both MR and ER dampers, providing insights into their hysteresis and operational behavior. For parametric modeling, a novel hybrid optimization algorithm is introduced, combining PSO and L-BFGS-B gradient-based optimization. This algorithm reduces the computation time while maintaining accuracy. For non-parametric modeling, an LSTM-RNN is developed, incorporating tapped delay lines and predicted force feedback, which capture nonlinear hysteresis with high efficiency. Additionally, a closed-loop pole placement strategy replicates desired impact acceleration profiles. This work demonstrates hybrid optimization and neural network modeling, providing a foundation for rheological damper modeling and impact control.

In recent times, digital twins (DT) is becoming an emerging and key technology for smart industrial control systems and Industrial Internet of things (IIoT) applications. The DT presently supports a significant tool that can generate a huge dataset for fault prediction and diagnosis in a real-time scenario for critical industrial applications with the support of powerful artificial intelligence (AI). The physical assets of DT can produce system performance data that is close to reality, which delivers remarkable opportunities for machine fault diagnosis for effective measured fault conditions. Therefore, this study presents an intelligent and efficient AI-based fault diagnosis framework using new hybrid optimization and machine learning models for industrial DT systems, namely, the triplex pump model and transmission system. The proposed hybrid framework utilizes a combination of optimization techniques (OT) such as the flower pollination algorithm (FPA), particle swarm algorithm (PSO), Harris hawk optimization (HHO), Jaya algorithm (JA), gray wolf optimizer (GWO), and Salp swarm algorithm (SSA), and machine learning (ML) such as K-nearest neighbors (KNN), decision tree (CART), and random forest (RF). The proposed hybrid OT–ML framework is validated using two different simulated datasets which are generated from both the mechanized triplex pump and transmission system models, respectively. From the experimental results, the hybrid FPA–CART and FPA–RF models within the proposed framework give acceptable results in detecting the most relevant subset of features from the two employed datasets while maintaining fault detection accuracy rates exemplified by the original set of features with 96.8% and 85.7%, respectively. Therefore, the results achieve good and acceptable performance compared to the other existing models for fault diagnosis in real time based on critical IIoT fields.

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

Object detection and tracking play a critical role in intelligent transportation systems (ITS), particularly in recognizing and monitoring traffic lights to ensure safety and improve traffic efficiency. Despite progress in deep learning and optimization algorithms, traffic light detection still faces persistent challenges under varying conditions such as illumination changes, occlusions, and visual clutter. This study provides a critical review of object detection techniques specifically for traffic light detection, evaluating the evolution of machine learning frameworks, deep learning architectures, and hybrid optimization models. The review identifies research gaps in the robustness, real-time adaptability, and generalizability of existing methods. Furthermore, it highlights emerging trends such as multi-camera systems, anchor-free detection, and hybrid optimization techniques that bridge performance trade-offs between accuracy and efficiency. The findings offer a new perspective on integrating multiple approaches to achieve scalable, high-accuracy traffic light detection for future ITS applications.

Natural plant fibers such as sisal, ramie, flax, kenaf, jute, banana, coir, etc. are emerging alternatives to manmade fibers like glass, aramid and carbon fiber for many reason like their physical properties, viz., low density, renewability, recyclability as well as biodegradability. Over the past several years, composites made from natural fibers have become an attractive domain of research interest for most of the academicians and researchers. Extensive research and innovation have been conducted in the domain of polymer composites made with plant fibers as reinforcement due to their numerous advantages over the artificial fiber composites like eco-friendliness, natural properties, sustainability etc. Apart from these advantages concerned with environmental safety and protection, the composites made from natural fibers possesses many other advantages like high strength to weight ratio, less damage to processing equipment, low overall cost, less energy consumption during production and their easy hybridization with synthetic as well as other natural fibers. Therefore, natural fibers have become a significant and important part of the composite industry. Natural plant fibers can be reinforced with thermoplastics and thermosetting polymers but their suitability with polymer matrix is a significant aspect in governing the mechanical characteristics of polymer composites. Composites reinforced with natural plant fibers have some disadvantages like moisture absorption, inferior mechanical strength and weak fire resistance which can be overcome by their surface modification and through addition of various coupling agents. These composites have increasingly become a popular choice for several engineering industries including automobile, aerospace and sporting industries.In this chapter, various natural plant fibers are introduced and discussed and their mechanical properties are explored. Composites made from polymer matrix like thermoplastics and thermoset are then discussed; subsequently, the advantages and lighter load engineering applications of polymer composites embedded within natural fibers that are applied to numerous engineering sectors are presented.

In the steel industry, the propensity of byproduct gas flow fluctuation is a critical factor in energy scheduling. The financial success of the steel industry will greatly benefit from an accurate forecast of its future tendencies. Manufacturers began to focus more on the needs of the client than on the product. Energy-intensive businesses like steel manufacturing are also affected by it. It might be difficult for mass customization firms to determine a product’s overall cost with accuracy. Moreover, energy accounts for 20 %–40 % of the expenditures associated with steel goods. Growing the selection of products may inevitably result in a loss of sustainability. This study investigates the prediction of power consumption in the steel industry by employing Linear Discriminant Analysis Classification (LDAC) and Decision Tree Classification (DTC). To improve the predictive accuracy of these foundational models, two advanced optimizers were utilized: The Electrostatic Discharge Algorithm (EDA) and the Political Optimizer (PO). These optimizers were systematically integrated with the base models to develop innovative hybrid models. Specifically, the LDAC model coupled with EDA forms the LDED framework, the LDAC model combined with PO constitutes the LDPO framework, the DTC model integrated with EDA results in the DTED framework, and the DTC model combined with PO creates the DTPO framework. The DTPO model performs exceptionally well in the Accuracy metric value at the Test section (value of 0.931), while the DTC and DTED models perform similarly well (both with a value of 0.911). In the Precision metric value at the Test section (value of 0.932), the DTPO model performs the best, and the LDED model performs the worst (value of 0.857).

This study explores the synergies between advanced cooling technologies and photovoltaic systems, seeking to improve their overall efficiency and contribute to the broader goal of mitigating greenhouse gas emissions. To cool photovoltaic panels in more efficiently maner, understanding heat pipes, nanofluids, and panels interaction play key roles. For analysis and optimization, hybrid models of convolutional neural network (CNN) and firefly optimization algorithm are employed. The firefly optimization algorithm is used to optimize the thermosiphon heat pipe’s operational conditions, taking into account inputs such as the filling ratio, nanofluid concentration and panel angle. The study compared the predicted outcomes of a classic CNN model to laboratory experiments. While the CNN model was consistent with experimental findings, it struggled to predict high power values with precision. The proposed model improved high power value predictions by 4.05 W root mean square error (RMSE). The proposed model outperformed the classic CNN model for values greater than 50 W, with an RMSE of 3.95 W. The optimal values for the filling ratio, nanofluid concentration and panel angle were determined after optimization with the firefly algorithm. The research contributes to the advancement of renewable energy technologies and the optimization of photovoltaic panel cooling and energy production. Nanofluid with 1% mass concentration improves photovoltaic collector thermal efficiency due to its higher thermal conductivity coefficient. The photovoltaic collector’s electrical efficiency peaks in the morning, drops at noon due to temperature and radiation and recovers by morning. Electrical efficiency is best with nanofluid at 0.86%. Exergy efficiency closely matches electrical efficiency, with nanofluid at the optimal percentage achieving the highest efficiency and water cooling the lowest.