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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets
Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable. 2023, Tech Science Press. All rights reserved. -
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors. -
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022. -
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors. -
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved. -
Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver's facial expressions and detect facial landmarks in order to extract the driver's state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle's electronics, tracking the vehicle's statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change. 2013 IEEE. -
Stability and bifurcation analysis of a fractional-order preypredator model with ratio-dependent functional response
This paper explores the dynamics of a fractional preypredator system with a ratio-dependent functional response with memory and hereditary effects in predatorprey interactions. The model is developed by the Caputo fractional derivative, and the existence, uniqueness, positivity, and boundedness of solutions are proven to satisfy biological reality. Stability conditions for local and global stability of both predator-free and coexistence equilibria are proven through linearization and Lyapunov function techniques. The fractional order is used as a bifurcation parameter, and the appearance of Hopf bifurcations is analytically explained with demonstration of the influence of memory on oscillations. To examine discrete-time dynamics, the piecewise constant argument is used to derive a discrete counterpart of the fractional model. The discrete model indicates a wide range of rich complex oscillatory phenomena, including period-doubling and NeimarkSacker bifurcations, leading to periodic, quasiperiodic, and chaotic oscillations. Numerical computations, including bifurcation diagrams, phase portraits, and Lyapunov exponents, verify the analytical results and describe the routes of transition to chaos. A comparative analysis to compare integer- and fractional-order cases indicates that memory effects enhance dynamical richness and sensitivity to parameters. The study provides a unified framework relating continuous fractional dynamics and their discrete implementations and provides additional insight into how memory and discretization interact to modify stability and bifurcation in ecological models. 2026 the Author(s), -
Knots of the umbilical cord: Incidence, diagnosis, and management
Knot(s) of the umbilical cord have received emphasis because the clinical assessments and sonographic literature show a crucial role in fetal outcomes. The true umbilical cord knot could be a knot in a singleton pregnancy or an entanglement of two umbilical cords in monoamniotic twins. Clinical manifestations are almost silent, which can raise clinical challenges. They worsen outcomes, and the pathology can be easily missed during prenatal visits because ultrasonographers do not pay attention to the cord during an obstetric ultrasound scan. However, most medical centers now have ultrasound machines that improve fetal assessment. The umbilical cord should be routinely evaluated during a fetal assessment, and suspicion of an umbilical cord knot can be more frequently diagnosed and is detected only incidentally. Clinical outcome is usually good but depends on the knot's characteristics and if it is tight or loose. In this review, we discuss pathophysiology, the theories on formation, the main risk factors, ultrasound signs and findings, different opinions in the management, and features of pregnancy outcomes feature. 2024 International Federation of Gynecology and Obstetrics. -
Thermal analysis of a radiative nanofluid over a stretching/shrinking cylinder with viscous dissipation
This study explores the impact of thermal radiation and viscous dissipation on the stagnation point flow of a copperwater nanofluid across a convective stretching/shrinking cylinder. The copper suspension in the base fluid water enables the fluid to conduct more heat by increasing its thermal conductivity. The mathematical model that governs the flow of Cu-H2O nanofluid is formulated by the system of partial differential equations (PDEs) which are then subjected to transformation by introducing suitable similarity variables so the system is transformed to the Ordinary Differential Equations (ODEs). These equations have been solved numerically via the bvp4c package in MATLAB. The outcomes have been signified graphically in the form of heat transfer rate, temperature, skin friction and velocity which are dependent on the concerning flow parameters. For each of these result, dual solutions have been produced which are conditional on the shrinking of cylinder. These results declare that the skin friction increases for the shrinking cylinder and decreases for the stretching cylinder whereas an opposite trend is seen for the rate of heat transfer. Similarly, heat transfer is found to be decreasing for the increase in both Biot and Eckert number. Meanwhile, the existence of greater values of curvature parameter causes to enhance both first and second solution of velocity as well as the temperature is augmenting with the increase in Eckert number and volume fraction of nano particles. 2022 Elsevier B.V. -
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Estimation of ground state and excited state dipole moments of a novel Schiff base derivative containing 1, 2, 4-triazole nucleus by solvatochromic method
A novel schiff base derivative containing 1, 2, 4-triazole moiety (NBTMPA) has been synthesized from 4- [1, 2, 4] triazol-1-ylmethyl-phenylamine and 4-nitrobenzaldehyde in the presence of glacial acetic acid in an ethanolic medium. The absorbance and fluorescence spectra of (4-nitro-benzylidene)-(4- [1, 2, 4] triazol-1-ylmethyl-phenyl)-amine (NBTMPA) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of NBTMPA were calculated from solvatochromic spectral shifts. These were correlated with the refractive index (n) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TDDFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of NBTMPA was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centres in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of a TDDFT computational analysis. 2015 Elsevier B.V. -
Synthesis, characterization and photophysical studies of a novel schiff base bearing 1, 2, 4-Triazole scaffold
A novel Schiff base derivative containing 1, 2, 4-triazole nucleus (TMPIMP) was synthesized from 4- [1,2,4] triazol-1-ylmethyl-phenylamine and salicylaldehyde in the presence of glacial acetic acid in an ethanolic medium. The synthesized compound was characterized by 1H-NMR, IR and UV spectral analysis. The excitation and emission spectra of triazolyl methyl phenyl imino methyl phenol (abbreviated as TMPIMP) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of TMPIMP were calculated from solvatochromic spectral shifts. These were correlated with refractive index (?) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TD-DFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of TMPIMP was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centers in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of TD-DFT computational analysis. 2016 Elsevier B.V. All rights reserved. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Theory of planned behavior in predicting the construction of eco-friendly houses
Purpose: The present study aimed to explore the applicability of theory of planned behavior in construction of eco-friendly houses. Design/methodology/approach: Study utilized cross-sectional correlational research design, collected data from 269 adult house owners of Kerala, India, with the help of a self-report measures namely, attitude towards eco-friendly house construction, subjective norm, perceived behavioral control, behavioral intention to build eco-friendly houses, check list of eco-friendly house and socio-demographic data sheet. Descriptive statistics, Karl Pearson product moment correlation, confirmatory factor analysis and mediation analysis with the help of AMOS were used to describe the distribution of study variables and to test the research hypotheses and proposed model. Findings: Study revealed that behavioral intention to build eco-friendly house was the immediate and strongest predictor of actual behavior of constructing an eco-friendly house. Behavioral intention mediated the relationship of attitudinal variables, normative variables and control variables with the behavior of constructing eco-friendly houses. Research limitations/implications: The results vouched the applicability of theory of planned behavior as a comprehensive model in explaining the behavior of eco-friendly house construction. Practical implications: Results of the study iterates the utility of attitudinal, normative and control factors in enhancing the choice of constructing eco-friendly houses. The results can be applied to develop a marketing tool to enhance the behavior of choosing or constructing eco-friendly houses in the population. Originality/value: Role of conventional concrete construction in climate crisis is unquestioned, and adopting eco-friendly architecture is a potential solution to the impending doom of climate crisis. Behavioral changes play a significant role in the success of global actions to curb the climate crisis. Present study discusses the role of psychological variables in constructing eco-friendly houses. 2022, Emerald Publishing Limited. -
Enhancing Malware Detection Through Hybrid Deep Learning Techniques
The detection of malware needs superior methods than basic signature detection because it remains vital to cybersecurity. This research examines malware classification through the deep learning approach by analyzing Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and develops a new BiGRU + CNN hybrid model. The main purpose is to achieve better detection performance through reduced numbers of false alarms. The research employs executable file feature data while implementing preprocessing methods together with fivefold cross-validation validation to establish strong model reliability. Experimental findings show CNN along with LSTM and GRU attains excellent recall values yet produces elevated erroneous positive predictions. The proposed BiGRU + CNN model delivers superiority over single-model architecture as it reaches 96.06% accuracy alongside 96.13% precision and 99.92% recall and 97.99% F1-score. The obtained results show that this integration has better malware detection capabilities thereby demonstrating its potential for cybersecurity applications. 2025 IEEE. -
Development of CaO/Chitosan/Dopamine NanoparticlesAntibacterial, Anticancer, andAntioxidant Activities
Infectious diseases and cancer are two significant groups of diseases attributed to the major death around the globe. There is a need to develop innovative strategies to treat antibiotic resistance bacteria and cancer effectively. In this context, the present work focused on development of calcium oxide (CaO) and CaO modified with chitosan and dopamine nanocomposites (CaOCsDop) as potential antibacterial, anticancer, and antioxidant agents. The prepared nanoparticles were characterized using various characterization techniques. FTIR revealed the functional groups of prepared samples indicating the successful preparation of nanoparticles. XRD revealed the fcc cubic nature of CaO nanoparticles and the crystallite size was found to be 23 nm for CaOCsDOP and 31 nm for CaO nanoparticles. DLS results confirmed the mean particle hydrodynamic size was found as nm for 231.90 CaO and 189.90 nm for CaOCsDOP nanocomposite. The disk diffusion assay was carried out against common pathogenic bacterial strains as Pseudomonas aeruginosa, Klebsiella pneumoniae, Vibrio cholerae, Escherichia coli, and Shigella dysenteriae. MTT assay was carried out to determine the anticancer activity against MOLT-4 cell line, a human acute lymphoblastic leukemia model. The results indicated that CaOCsDOP nanocomposites exhibited enhanced antibacterial and anticancer activities compared with bare CaO nanoparticles, making them a promising multifunctional agent in biomedical applications. 2025 Wiley Periodicals LLC. -
Analyses of the Power Flow through Distributed Generator based on Unsynchronized Measurements
Based on measurements taken from the main substation and the connections between distributed generators and micro-grids that are not in sync, this study suggests a new way to look at the load flow of distributed generation. The conclusions are based on data from a distribution generatora's Load Flow Analysis that was not in sync. Distributed generation is what this approach is based on. Creating a strong communication system and using measurement data from the past are two ways to make this happen. This objective may be achieved with the use of previously gathered measurements. The time-tested backward-forward sweep method is the method of choice for analyzing power flow using unsynchronized data. This is the preferred approach. The angles of synchronization are likely to be unknowns that must be estimated. On a smart grid system with a large number of distributed generation and microgrids, a range of mathematical computations are conducted to verify the correctness of performance predictions produced by the suggested theory. The classic backward-forward sweep was shown to be the most effective method for analyzing power flow based on data that was not synchronized in many instances. This is the strategy that is presently being recommended. Because the angles of synchronization are presumed to be unknown, a mathematical equation must be devised to determine them. The Authors, published by EDP Sciences, 2024. -
AI and Big Data Analytics for Management of Nutraceuticals
This chapter explores the transformative impact of artificial intelligence (AI) and Big Data Analytics on nutraceutical management. As consumer demand for health-focused products such as supplements, functional foods, and herbal extracts grows, ensuring quality, safety, regulatory compliance, and personalization remains challenging. AI enhances formulation and quality assurance through predictive modeling and computer vision, while analyzing individual health data for personalized nutrition. Concurrently, Big Data leverages vast datasets from clinical trials to consumer feedback to forecast market trends, segment consumers, and guide R&D. The integration of AI and Big Data fosters deeper insights, driving innovation, safety, and tailored solutions. However, challenges like data privacy, standards, and ethics persist. Future developments may include AI-driven discovery of bioactives, real-time supply chain monitoring, and blockchain-based transparency. Embracing these innovations is essential for advancing safer, more effective, and personalized nutraceuticals that promote public health. Springer Nature Switzerland AG 2026. -
Structural modification of electrophilic group substituted phenyldiazenyl derivatives for antitubercular application
In the present work, four electrophilic group substitute phenyldiazenyl derivatives were synthesized using an electrophilic substitution reaction. The physicochemical analysis was carried out using FT-IR, 1H NMR, and HR-MS data. The photophysical studies were carried out using theoretical methods. Density functional theory was employed to illustrate the electronic and optical characteristics of the synthesized compounds. The HOMO-LUMO energies were theoretically computed in different solvents using Gaussian 09W software and results are compared with the experimental values. The molecule PT4 shows highest bandgap of 4.497eV. Further, the global chemical reactivity descriptors were used to determined nature of chemical reactivity. The anti-tubercular activity was evaluated using invitro and molecular docking techniques and results reveal that barbituric acid coupled with phenyldiazenyl displayed excellent anti-tubercular activity compared with the standard Gentamycin. 2024 Indian Chemical Society -
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.