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Machine intelligence security : A methodological blend of fuzzy logic in industry 4.0 algorithms
The way things are made has changed a lot because of Industry 4.0. It has also led to a time with great technology and relationships. The paper discusses way to improve security in Machine Intelligence in the setting of Industry 4.0. The study uses a mix of methods to combine Fuzzy Logic with cutting-edge Industry 4.0 algorithms in order to deal with new hacking problems. Because fuzzy logic can deal with doubt and imprecision, it can be used to make current methods more reliable. This creates a complex and flexible security structure. The merger was carefully planned to make the methods for finding anomalies, reducing threats, and responding to incidents work better. The suggested method aims to make machine intelligence systems more resistant to complex cyber dangers by combining the best parts of Fuzzy Logic with Industry 4.0 algorithms. This study adds to the growing conversation about how to keep smart factory settings safe by focusing on a proactive and dynamic security model. The effects of this mix of methods could be felt in many different industries, making it possible to use advanced technologies in a safer and more reliable way in the age of Industry 4.0. 2024, Taru Publications. All rights reserved. -
An Enhanced Whale Optimization Algorithm for Task Scheduling in Cloud Computing
Task Scheduling is the significant challenge in the environment of Cloud Computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced Whale Optimization Algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Hybrid Machine Learning Model (NB-SVM) for Cardiovascular Disease Prediction
One of the leading causes of death is heart disease. The prediction of cardiovascular disease remains as a significant challenge in the clinical data analysis domain. Although predicting cardiac disease with a high degree of accuracy is highly challenging, it is possible with Machine Learning (ML) approaches. The implementation of an effective ML system can minimize the need for additional medical testing, minimize human intervention, and predict cardiovascular diseases with high accuracy. This type of assessment can reduce the disease's severity and mortality rate. Only a few studies show how machine learning techniques might forecast cardiac disease. This study presents a method for improving cardiovascular disease prediction accuracy using Machine Learning (ML) technologies. Various feature combinations and many known classification techniques are used to develop various cardio vascular disease prediction models. The proposed hybrid Machine Learning (ML) prediction model for heart disease leverages a higher degree of performance and accuracy. 2023 IEEE. -
The influences of lateral groups on 4-cyanobiphenyl-benzonitrile- based dimers
Cyanobiphenyl-based compounds are known to display RT or low melting liquid crystals in a single-component system or composites. Herein, we discuss the influence of laterally substituted groups (-CN, -F, -H) on 4-[?-(4-cyanobiphenyl-4-yloxy)alk-1-yloxy]benzonitrile. Three series of new dimers were synthesised by using 4-cyano-4-hydroxybiphenyl connected via flexible spacers with different number of carbon atoms to 4-hydroxyphthalonitrile/ 2-fluoro-4-hydroxy benzonitrile/ 4-hydroxy benzonitrile. Their self-assembly in LC phases assessed by polarising optical microscopy (POM), differential scanning calorimetry (DSC) and X-ray diffraction studies, and their behaviours are compared with related non-substituted (-H) model compound. UV-Visible and fluorescent experiments confirm the strong aggregation, the intensities of emission decrease as we move from CN?F?H substitutions. A representative dimer from each series covering the aspect of polarity and flexibility have been simulated using 1000 minimisation steepest descent and CHARMM force filed to examine their self-assembly. This work helps to understand the influence of lateral groups, connecting spacers on the LC behaviour of dimers. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Irreversibility analysis of radiative heat transport of Williamson material over a lubricated surface with viscous heating and internal heat source
Thecurrent research explores the importance of surface lubrication and convective boundary conditions in the flow of non-Newtonian Williamson material. Rosseland radiative heat flux and viscous heating are also considered. The phenomenon of the generation or absorption of internal heat is studied. The conservation laws of momentum, mass, and energy are used to model the problem with suitable boundary conditions. With the help of appropriate transformations and the finite difference method, highly nonlinear equations of governance are solved. The influence of key parameters on Bejan number, velocity, entropy production, temperature profiles are analyzed by parametric analysis. It was found that the entropy generation rate improves due to the presence of the Rosseland radiative heat flux and the convective boundary on the lubricated surface. The sliding condition on the lubricated surface has lengthened the structure of the velocity boundary layer, while this trend is opposite to the thermal field. The dissipation due to the viscous forces of the Williamson material improves the production of entropy. 2021 Wiley Periodicals LLC -
Entropy generation and thermal analyses of a Cross fluid flow through an inclined microchannel with non-linear mixed convection
The temperature difference of the various applications such as microchannel heat exchangers, microelectronics, solar collectors, automotive systems, micro fuel cells, and microelectromechanical systems (MEMS) is relatively large. The buoyancy force (mixed convection) modeled by the conventional Boussinesq approximation is inadequate since the density of the operating fluids fluctuates non-linearly with the temperature difference. Therefore, the mixed non-linear convective transport of the flow of Cross fluid through three different geometric aspects (horizontal, vertical, and inclined) of the microchannel under the non-linear Boussinesq (NBA) approximation is investigated. Mechanisms of internal heat source, Rosseland radiative heat flux, and frictional heating are incorporated into the thermal analysis. The mathematical construction is proposed using the Cross fluid model for a steady-state, and subsequent non-linear differential equations are deciphered by the spectral quasi-linearization method (SQLM). Graphical sketches were constructed and displayed that explore the stimulus of various key parameters on Bejan number, velocity, temperature, and entropy generation. It is found that the Bejan number and entropy production improved due to the non-linear density temperature variation. The convective heating boundary conditions augment the entropy production. The pressure gradient accelerates the transport of fluid in a microchannel. Furthermore, among three different geometries, the velocity, entropy production, and temperature are the highest for the vertical microchannel. 2023 Wiley-VCH GmbH. -
Entropy generation analysis of tangent hyperbolic fluid in quadratic Boussinesq approximation using spectral quasi-linearization method
In many industrial applications, heat transfer and tangent hyperbolic fluid flow processes have been garnering increasing attention, owing to their immense importance in technology, engineering, and science. These processes are relevant for polymer solutions, porous industrial materials, ceramic processing, oil recovery, and fluid beds. The present tangent hyperbolic fluid flow and heat transfer model accurately predicts the shear-thinning phenomenon and describes the blood flow characteristics. Therefore, the entropy production analysis of a non-Newtonian tangent hyperbolic material flow through a vertical microchannel with a quadratic density temperature fluctuation (quadratic/nonlinear Boussinesq approximation) is performed in the present study. The impacts of the hydrodynamic flow and Newtons thermal conditions on the flow, heat transfer, and entropy generation are analyzed. The governing nonlinear equations are solved with the spectral quasi-linearization method (SQLM). The obtained results are compared with those calculated with a finite element method and the bvp4c routine. In addition, the effects of key parameters on the velocity of the hyperbolic tangent material, the entropy generation, the temperature, and the Nusselt number are discussed. The entropy generation increases with the buoyancy force, the pressure gradient factor, the non-linear convection, and the Eckert number. The non-Newtonian fluid factor improves the magnitude of the velocity field. The power-law index of the hyperbolic fluid and the Weissenberg number are found to be favorable for increasing the temperature field. The buoyancy force caused by the nonlinear change in the fluid density versus temperature improves the thermal energy of the system. 2021, Shanghai University. -
Factors influencing dynamic capabilities of entrepreneurial-led organisations to achieve analytical transformation
Entrepreneurial spirit transforms the economic scenario resulting in a significant contribution to society. Analytical transformation enables entrepreneurs with superior effective decision-making capability through information gathering, advanced technology adoption and data analysis. Effective analysis leads to superior organisational performance. However, in entrepreneurial-led large Indian organisations, the adoption of analytics is limited to predicting results. The study aims to identify the key factors that impact analytical transformation. The study also aims to identify key dynamic capabilities to achieve such transformation. This article identifies base theories related to the identified concepts. This article aims to develop an analytical transformation capability model for entrepreneurial-driven large industries. This study also empirically validates the proposed research model. The study concludes that entrepreneurial-led large Indian technology-driven industries lag behind their technology peers in adopting prescriptive analytics. The study also proposes an analytical transformation theory that aims to provide necessary techniques to improve organisational effectiveness. Copyright 2025 Inderscience Enterprises Ltd. -
Best unbiased estimation and CAN property in the stable M/M/1 queue
The Uniform Minimum Variance Unbiased (UMVU) estimators of ??, the probability of having ? or more customers, L, the expected system size, Lq, the expected number of customers in the queue, and, the expected number of customers in a non empty queue, are derived based on a random sample of fixed size n on system size at departure points from the geometric distribution on the support {0, 1, 2,.} with mean, which is the distribution of system size in M/M/1 queueing system in equilibrium. The derivations are based on application of Lehmann-Scheffe theorem. Also, CAN estimators of performance measures are derived. In addition the probability distribution of UMVU estimators are obtained. 2014 Copyright Taylor and Francis Group, LLC. -
Hybrid Quantum Network with Snow Geese-Elk Herd Optimization for Smart Load Shedding in Grids with Electric Vehicles and Photovoltaic Systems
The increasing penetration of variable renewable energy and the growth of electric vehicles (EV) have created an urge for more sophisticated load management methods to ensure grid stability. Conventional load shedding (LS) methods are typically not equipped to manage the unpredictability brought about by these modern additions to the grid. This study introduces an innovative smart load-shedding strategy that uses a hybrid optimization model. At its core is a Quantum Neural Network (QNN), which enables intelligent and data-based load prioritization by evaluating factors such as load criticality, energy usage, responsiveness to demand, and operational flexibility. The required LS amount is calculated through a combined use of Snow Geese Optimization (SGO) and the Elk Herd Optimizer (EHO), with specific attention given to the flexibility offered by EVs to address the variability in photovoltaic (PV) power generation. Testing has been performed on the IEEE 33-bus network reveal a notable decrease in total load demand by around 33%, contributing to improved grid stability, with voltage levels staying close to 0.99 p.u. Additionally, the average load across the network buses dropped by roughly 52%. This hybrid approach not only ensures better performance but also achieves quicker convergence compared to existing optimization methods. The proposed intelligent LS method presents an effective strategy for preserving grid stability amid growing integration of renewables and EV by incorporating QNN with SGO and EHO while accounting for EV adaptability. The Author(s), under exclusive licence to Shiraz University 2025. -
Financing Green Startups: A Blockchain-Powered Approach
Green startups routinely encounter difficulty in obtaining financing owing to the high funding needs to launch their business, and the imprecise market acceptance and future returns of those businesses, rendering them unacceptable to traditional latter-day funding methods. The funding modality available today fails to provide the needed transparency, flexibility and accessibility to encourage ventures based on green projects. This paper develops a funding model based in blockchain for green startups, that employs tokenization, decentralised finance (DeFi) and smart contracts with automaticity, as an efficient way of funding to provide secure, traceable funding structures that make funds available related to the performance of the venture. We present a conceptual model showing the responsibilities of startups, funders and smart contracts within a de-centralised funding ecosystem. Various case studies such as Power Ledger and WePower are investigated in order to validate the practical relevance of the model. Our research indicates how blockchain mechanisms can heighten trust, enhance liquidity, and automate funding that is linked to impact. This paper contributes to future work on scalable platforms that are both regulation compliant and also provide a fit between Blockchain infrastructure and the unique requirements of sustainable innovation. 2025 IEEE. -
Factors influencing dynamic capabilities of entrepreneurial-led organisations to achieve analytical transformation
Entrepreneurial spirit transforms the economic scenario resulting in a significant contribution to society. Analytical transformation enables entrepreneurs with superior effective decision-making capability through information gathering, advanced technology adoption and data analysis. Effective analysis leads to superior organisational performance. However, in entrepreneurial-led large Indian organisations, the adoption of analytics is limited to predicting results. The study aims to identify the key factors that impact analytical transformation. The study also aims to identify key dynamic capabilities to achieve such transformation. This article identifies base theories related to the identified concepts. This article aims to develop an analytical transformation capability model for entrepreneurial-driven large industries. This study also empirically validates the proposed research model. The study concludes that entrepreneurial-led large Indian technology-driven industries lag behind their technology peers in adopting prescriptive analytics. The study also proposes an analytical transformation theory that aims to provide necessary techniques to improve organisational effectiveness. Copyright 2025 Inderscience Enterprises Ltd. -
Bridging Traditional NLP and Deep Learning: Comparative Study on Text Categorization Performance
Text categorization is an important area of Natural Language Processing (NLP) that is used to automatically organize textual information into a set of specific categories. This study is a comparative study of models that use statistical features and models that use transformers, using the example of DistilBERT-base-uncased fine-tuned and LoRA (Parameter-Efficient Fine-Tuning, PEFT). The data extracted on the Kaggle site is presented in the form of labeled text samples of five classes Business, Entertainment, Sport, Tech, and Politics. Conventional models such as Logistic Regression, Random Forest and XGBoost were trained on manually crafted word-level features (word count, mean word length and punctuation ratio) and had precisions up to 94.7%. Comparatively, the given DistilBERT-LoRA model used semantic embeddings to find the contextual dependencies and managed to reach the total accuracy of 97, precision of 97, and the recall of 96. The training and validation loss curves showed the stable convergence without overfitting, and the confusion matrix showed the consistent performance at all the classes with minimum misclassification. Comparative analysis indicated that semantic embeddings are much better than statistical models because they enhance contextual perception and strength of classification. The findings confirm the effectiveness and scalability of the LoRA-based fine-tuning, which offers an efficient but lightweight strategy in the context of real-world settings to achieve high-performance text categorization. 2025 IEEE. -
Investigation of electrical properties of developed indigenous natural ester liquid used as alternate to transformer insulation
The performance of every electrical system depends on the different electrical devices especially transformers. Petroleum-based mineral oil is widely used for insulation and cooling purpose. The disadvantage of mineral oil is its low biodegradability and is a major threat to the ecosystem due to its poor oxidative stability. To remedy the drawbacks, focus on alternative fluids that can replace traditional mineral oil. Alternative liquids such as natural esters are used which do not panic the ecosystem. With the support of additives in natural esters liquids, the productivity of the oil can be increased, paving the path for the green conversion of liquids in high voltage applications. The purpose of this article is to analyze the electrical properties of the newly developed indigenous oil. The inhibited oil was insulating oil to which antioxidants were added such as 2,6-ditertiary-butylparacresol, butylated hydroxyl anisole and tertiary butyl hydro qunine to slow down the oxidation rate and to check the electrical properties. This article discusses the electrical properties of mineral oil, developed indigenous oil with and without antioxidants as per IEC62770 standards. A 1.1 kVA transformer was then designed in a laboratory for load tests and Indigenous oil performance under load was evaluated. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Comparative study of Breakdown Phenomena and Viscosity in Liquid Dielectrics
Liquid dielectrics are extensively used in electrical apparatus which are operating in distribution and transmission systems. The function of electrical equipment strongly depends on the conditions of liquid dielectric. Liquid dielectrics used are the most expensive components in power system apparatus like transformers and circuit breakers. A failure of these equipment would causes a heavy loss to the electrical industry and also utilities. Insulation failures are the leading cause of transformer failures and thus the liquid dielectrics plays a major role in the safe operation of transformers. One of the main causes for the failure of transformers is due to the presence of moisture. In this work, the life of insulating medium is estimated by comparing the Breakdown strength and Viscosity of different pure oils with that of the contaminated oils (which contains moisture) and also finding the alternative for mineral oil. vegetable oils which are reliable, cost-effective and environmental friendly even when they are contaminated. 2019 IEEE. -
Investigation of dielectric properties of indigenous blended ester oil for electric system applications
The insulation condition of a transformer decides the longevity of the equipment. The unpredicted failure of power transformer will lead to major disaster in the distribution network and it affects both environment and public safety. Nowadays synthetic oil and natural esters are alternatives to transformer oil because of the biodegradable nature. In this paper, investigations were carried out to study the performance of the blended ester. The different properties investigated were viscosity, breakdown voltage, flash point, dielectric dissipation factor and moisture content. Comparisons of the properties were made between mineral oil, vegetable oil without additives and with additives. Further Investigation was carried out to study the impact of antioxidants and degasification. The results indicated that the addition of antioxidants and degasification of the vegetable oil improve significantly its voltage withstanding capacity. The Indigenous oil is code named as DM; Indigenous oil with DBPC is codenamed as DM1, Indigenous oil with BHA is codenamed as DM2. The results have been tabulated and found to be satisfactory. 2020 ASTES Publishers. All rights reserved. -
Studies on Tensile Properties of Graphene Hydroxyl Reinforced Aluminium 6061 Composites for Vehicle Structures Applications
Aluminium composite plays a significant role in the mechanical structures. Low tensile strength of the aluminium alloy limits its application in mechanical structures. Graphene hydroxyl (GrOH) is a noble emerging material which is an allotropic form of carbon. It has high cohesive strength, good bonding ability with other materials. Carbon bonds processes high compatibility when it reinforced with other material. Reinforcement of GrOH with aluminium composites enhances the wear strength of composite material. This paper focused on analysis of tensile properties and percentage elongation of aluminium composites reinforced with GrOH with various weight percentage (wt. %). The characteristics of aluminium composites, particularly related to its tensile properties are very much important for its use in vehicle structures applications. 2022. MechAero Foundation for Technical Research & Education Excellence. -
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis and prediction of seed quality using machine learning
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithms predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the projects primary goal is to develop the best method for the more accurate prediction of seed quality. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Early CKD Prediction Using Ensemble and Basic Machine Learning Models
Chronic kidney disease (CKD) is a progressive illness that often remains undiagnosed until advanced stages and represents a significant global health burden. Proper and timely diagnosis of CKD can significantly improve patient prognosis and reduce treatment costs. This study evaluates several machine learning (ML) models, including support vector machine (SVM), random forest (RF), gradient boosting (GB), Nae Bayes (NB), AdaBoost, and a multilayer perceptron (MLP) neural network. Additionally, it proposes a stacking ensemble model combining RF and GB for accurate CKD prediction using a publicly available Kaggle dataset. Missing value handling and feature normalisation are performed during data preprocessing, and model performance is evaluated using an 80:20 traintest split with metrics such as the area under the curve (AUC), classification accuracy (CA), F1-score, precision, recall, and Matthews Correlation Coefficient (MCC). Experimental results indicate that RF and GB achieve the strongest individual performance, while the proposed stacking ensemble attains the highest CA of 99.4%. These findings highlight the potential of artificial intelligence (AI)-driven predictive models to support proactive CKD diagnosis and enhance clinical decision-making in healthcare systems. 2026 by the authors of this article. Published under CC-BY.