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In the recent era, banking infrastructure constructs various remotely handled platforms for users. However, the security risk toward the banking sector has also elevated, as it is visible from the rising number of reported attacks against these security systems. Intelligence shows that cyberattacks of the crawlers are increasing. Malicious crawlers can crawl the Web pages, crack the passwords and reap the private data of the users. Besides, intrusion detection systems in a dynamic environment provide more false positives. The purpose of this research paper is to propose an efficient methodology to sense the attacks for creating low levels of false positives.

In this research, the authors have developed an efficient approach for malicious crawler detection and correlated the security alerts. The behavioral features of the crawlers are examined for the recognition of the malicious crawlers, and a novel methodology is proposed to improvise the bank user portal security. The authors have compared various machine learning strategies including Bayesian network, support sector machine (SVM) and decision tree.

This proposed work stretches in various aspects. Initially, the outcomes are stated for the mixture of different kinds of log files. Then, distinct sites of various log files are selected for the construction of the acceptable data sets. Session identification, attribute extraction, session labeling and classification were held. Moreover, this approach clustered the meta-alerts into higher level meta-alerts for fusing multistages of attacks and the various types of attacks.

This methodology used incremental clustering techniques and analyzed the probability of existing topologies in SVM classifiers for more deterministic classification. It also enhanced the taxonomy for various domains.

The user interest in content searching in the web will be changed over by time.

The system is in need to find the content of user over the temporal aspects.

So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.

So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.

In this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.

In this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.

This work also categorizes the future needs of users using this proposed system.

This study aims to examine magnetohydrodynamic mixed convective phenomena and entropy generation within a semicircular porous channel, incorporating impinging jet cooling and the effects of thermal radiation. The present study analyzes the complex flow dynamics and heat transfer characteristics of a highly diluted 0.1% (volume) concentration Cu–Al₂O₃/water hybrid nanofluid, based on findings from previous studies. The investigation is intended to support the development of effective thermal management systems across diverse industries, such as cooling of electronic devices and enhanced energy system applications.

This study incorporates a heated curved bottom wall and a cooling jet of Cu–Al₂O₃/water hybrid nanofluid impinging from the central top inlet, with two horizontal exit ports along the rectangular duct. Finite element-based simulations are conducted using COMSOL Multiphysics, using a single-phase homogeneous model justified by earlier works. This method uses experimental data of effective thermal conductivity and viscosity, emphasizing the evaluation of thermal performance in scenarios involving intricate geometries and multiphysical conditions. The study analyzes nondimensional variables such as Reynolds number (Re), modified Rayleigh number (Ra_m), Hartmann number (Ha), Darcy number (Da) and radiation parameter while maintaining a constant nanofluid volume fraction. A grid independence study and code validation were performed to ensure numerical accuracy.

The analysis indicates that elevated Re contribute to a lessening in the thermal boundary layer thickness, prompting flow separation and significantly amplifying the average Nusselt number. The mixed convective heat transfer enhancement, coupled with an overall reduction in total entropy generation, diminishes with a rising Ha. However, optimized combinations of higher values for modified Ra_m and Da yield improved heat transfer performance, particularly pronounced with increasing Ha. Radiative heat transfer exerts a detrimental impact on both heat transfer and entropy production.

While the single-phase model captures key macroscopic effects differentiating nanofluids from base fluids, it does not provide insights at the nanoparticle level. Future studies could incorporate two-phase models to capture particle-level dispersion effects. In addition, experimental validation of the findings would strengthen the study’s conclusions.

This work represents innovative perspectives on the development of efficient hydrothermal systems, accounting for the influences of thermal radiation, porous media and hybrid nanofluids within a complex geometry. The results offer critical insights for enhancing heat transfer efficiency in real-world applications, especially in sectors demanding advanced cooling solutions.

The purpose of this paper is to develop hybrid aluminum metal matrix composite by stir casting process, reinforced with graphite and hard boron carbide particles to enhance the wear resistance. An attempt is made to optimize the wear (weight loss) and coefficient of friction (COF) by considering three factors, i.e. normal load, track diameter and sliding speed which were varied at three different levels.

The effect of graphite and boron carbide on microhardness was studied by adding them in varying percentages. After determining the best combination of hybrid reinforcements, optimization of wear (weight loss) and COF was carried out at various levels of considered factors. Taguchi design of experiments was used using the software “Minitab 16.1”. ANOVA was used to analyze the effect of various parameters on wear and COF. To validate the results, mathematical modeling was carried out in terms of regression equations and results obtained by regression equations.

The results revealed that the lower weight percentage of graphite (3 per cent) and boron carbide (1 per cent) significantly improved microhardness of developed composites. Results of ANOVA revealed that normal load was the main contributing factor for wear and COF. The results obtained by regression equations and confirmatory tests were within the results obtained by ANOVA.

To the best of the author’s knowledge, very less work has been reported on optimization of wear and COF using hybrid reinforcement particles of graphite and boron carbide.

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

This study aims to undertake a comprehensive examination of heat transfer by convection in porous systems with top and bottom walls insulated and differently heated vertical walls under a magnetic field. For a specific nanofluid, the study aims to bring out the effects of different segmental heating arrangements.

An existing in-house code based on the finite volume method has provided the numerical solution of the coupled nondimensional transport equations. Following a validation study, different explorations include the variations of Darcy–Rayleigh number (Ra_m = 10–10⁴), Darcy number (Da = 10^–5–10^–1) segmented arrangements of heaters of identical total length, porosity index (ε = 0.1–1) and aspect ratio of the cavity (AR = 0.25–2) under Hartmann number (Ha = 10–70) and volume fraction of φ = 0.1% for the nanoparticles. In the analysis, there are major roles of the streamlines, isotherms and heatlines on the vertical mid-plane of the cavity and the profiles of the flow velocity and temperature on the central line of the section.

The finding of a monotonic rise in the heat transfer rate with an increase in Ra_m from 10 to 10⁴ has prompted a further comparison of the rate at Ra_m equal to 10⁴ with the total length of the heaters kept constant in all the cases. With respect to uniform heating of one entire wall, the study reveals a significant advantage of 246% rate enhancement from two equal heater segments placed centrally on opposite walls. This rate has emerged higher by 82% and 249%, respectively, with both the segments placed at the top and one at the bottom and one at the top. An increase in the number of centrally arranged heaters on each wall from one to five has yielded 286% rate enhancement. Changes in the ratio of the cavity height-to-length from 1.0 to 0.2 and 2 cause the rate to decrease by 50% and increase by 21%, respectively.

Further research with additional parameters, geometries and configurations will consolidate the understanding. Experimental validation can complement the numerical simulations presented in this study.

This research contributes to the field by integrating segmented heating, magnetic fields and hybrid nanofluid in a porous flow domain, addressing existing research gaps. The findings provide valuable insights for enhancing thermal performance, and controlling heat transfer locally, and have implications for medical treatments, thermal management systems and related fields. The research opens up new possibilities for precise thermal management and offers directions for future investigations.

Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.

For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.

The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.

This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.