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Optimization-Based Cash Management Model for Microfinance Applications Using GSA and PSO
Banks and businesses use cash as a means for exchange in finance on a regular basis to please customers. Making decisions about cash management can be challenging because banks must keep significant sums of cash in order to sustain high levels of client satisfaction. In this paper, linear PSO and GSA models are given for estimating the daily cash demand of a bank by taking into account the variables Year of Reference (RY), Years Month (My), Months Day (Dm), Days Week (Dw), Payday Effect Salary (Se), and Holiday Effect (He). Using PSO and GSA in MATLAB, the algorithms for estimating both the model coefficients for short term are implemented from the real data of a specific bank branch. The proposed system's overall cost is minimized using a fitness function. It was discovered that the results are in good accord with the observed data and that the PSO-based cash management model outperformed other models with superior accuracy. The models are then used for future cash management for validation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Customized SEIR Mathematical Model to Predict the trends of Vaccination for Spread of COVID-19
The uncertainty in life plans, restrictions on physical classrooms, loss of jobs, large number of infections and deaths due to COVID-19 are some significant causes of concern for the public as well as Governments all over the globe. Moreover, the exponential increase in the number of infected people in a short time is responsible for the collapse of the health industry during the pandemic caused by COVID-19. The health experts recommended that the quick and early diagnosis followed by treatment of patients in isolation is a way to minimize its spread and save lives. The objective of this research is to propose a customized SEIR model to predict the trends of vaccination in the USA. The experimental results prove that the Moderna vaccine reports the efficacy of 93%, which is higher than the Pfizer and Johnson and Johnson vaccines. 2022 ACM. -
Integrated hybrid membrane system for enhanced water treatment and desalination for environmental preservation
Technology advancements in desalination, water treatment, and energy efficiency are crucial to preserving our planet. It is critical to find solutions for the future that save natural resources and lessen environmental damage because the freshwater shortage is getting worse, and energy demand is increasing. They face various obstacles, even though their breakthroughs are extremely important. Lot of energy can be utilized for the traditional desalination techniques, as it negatively impacts the environment. Then, the process of the existing Water Treatment (WT) are expensive and ineffective. An Integrated Hybrid Membrane System for Enhanced WT (IHMS-EWT) is a unique technique for WT and desalination was suggested in this study. The integration of many membrane procedures like nanofiltration, reverse and forward osmosis, and membrane distillation, and these will helps in facilitating the best WT and desalination methods. Due to the incorporating Renewable Energy (RE), the IHMS-EWT also demonstrates the (SWMS) Sustainable Water Management System, as it enhances the EE and thereby reducing the environmental impact. The great potential in the wide range of applications was offered by the IHMS-EWT technique. Providing the decentralized WT solutions in the remote areas, this unique approach has the ability to reduce the fresh water scarcity in the coastal areas based on the demands of the municipal, industrial and agricultural demands. The environmental sustainability throughout the lenghthy operations was ensured by the support of IHMS-EWT. It also helps in providing resilience in the crisis situations. The cost-effective evaluations, operating parameter optimization, and performance prediction of the method was enabled by employing the computational modelling. Through simulatimg different contexts, the effective configurations and operational techniques are focussed on the study for enhancing the IHMS-EWT technology.The model shift in the SWM, the IHMS-EWT technique addresses the main problems and brings one step for more secure environment. Comparing to other existing methods, Improving the water purification by 98.2 %, 94.2 % efficiency rate, the EC prediction rate of 96.2 %, the cost-effectiveness rate by 82.4 % and the performance rate by 96.7 % by the suggested IHMS-EWT model and it was demonstrated by the outcomes of the experiment. 2024 The Authors -
End-to-End Encryption in Resource-Constrained IoT Device
Internet of Things (IoT) technologies will interconnect with a wide range of network devices, regardless of their local network and resource capacities. Ensuring the security, communication, and privacy protection of end-users is a major concern in IoT development. Secure communication is a significant requirement for various applications, especially when communication devices have limited resources. The emergence of IoT also necessitates the use of low-power devices that interconnect with each other for essential processing. These devices are expected to handle large amounts of monitoring and control data while having limited capabilities and resources. The algorithm used for secure encryption should protect vulnerable devices. Conventional encryption methods such as RSA or AES are computationally expensive and require large amounts of memory, which can adversely affect device performance. Simplistic encryption techniques are easily compromised. To address these challenges, an effective and secure lightweight cryptographic process is proposed for computer devices. This process utilizes a symmetrical encryption key block, incorporating a custom proxy network (SP) and a modified Feistel architecture. Security analysis and performance evaluation results demonstrate that the proposed protocol is secure and energy-efficient. The symmetric key encryption scheme is based on sequences in the Feistel cipher, with multiple rounds and sub-keys generated using principles derived from genetic algorithms. This proposed algorithm minimizes processing cycles while providing sufficient security. 2013 IEEE. -
Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification; [Nuevo HGDBO: Un Algoritmo Hrido de Optimizaci Genica y de Escarabajos Peloteros para la Selecci de Genes en Microrrays y la Clasificaci Eficiente del Ccer]
Introduction: ovarian cancer ranked as the seventh most common cancer and the eighth leading cause of cancer-related mortality among women globally. Early detection was crucial for improving survival rates, emphasizing the need for better screening techniques and increased awareness. Microarray gene data, containing numerous genes across multiple samples, presented both opportunities and challenges in understanding gene functions and disease pathways. This research focused on reducing feature selection time in large gene expression datasets by applying a hybrid bio-inspired method, HGDBO. The goal was to enhance classification accuracy by optimizing gene subsets for improved gene expression analysis. Method: the study introduced a novel hybrid feature selection method called HGDBO, which combined the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to improve microarray data analysis. The HGDBO method leveraged the exploratory strengths of DBO and the exploitative capabilities of GA to identify relevant genes for disease classification. Experiments conducted on multiple microarray datasets showed that the hybrid approach offered superior classification performance, stability, and computational efficiency compared to traditional methods. Ovarian cancer classification was performed using Nae Bayes (NB) and Random Forest (RF) algorithms. Results and Discussion: the Random Forest model outperformed the Nae Bayes model across all metrics, achieving higher accuracy (0,96 vs. 0,91), precision (0,95 vs. 0,91), recall (0,97 vs. 0,90), F1 score (0,95 vs. 0,91), and specificity (0,97 vs. 0,86). Conclusions: these results demonstrated the effectiveness of the HGDBO method and the Random Forest classifier in improving the analysis and classification of ovarian cancer using microarray gene data. 2024; Los autores. -
A comprehensive review on energy management strategy of microgrids
Renewable energy resources are a one-stop solution for major issues that include drastic climate change, environmental pollution, and the depletion of fossil fuels. Renewable energy resources, their allied storage devices, load supplied, non-renewable sources, along with the electrical and control devices involved, form the entity called microgrids. Energy management systems are essential in microgrids with more than one energy resource and storage system for optimal power sharing between each component in the microgrid for efficient, reliable and economic operation. A critical review on energy management for hybrid systems of different configurations, the diverse techniques used, forecasting methods, control strategies, uncertainty consideration, tariffs set for financial benefits, etc. are reviewed in this paper. The novelty of reformer based fuel cells, which generates hydrogen on demand, thereby eliminating the requirement of hydrogen storage and lowest carbon footprint is discussed for the first time in this paper. The topics requiring extended research and the existing gap in literature in the field of energy management studies are presented in the authors perspective, which will be helpful for researchers working in the same specialization. Papers are segregated based on multiple aspects such as the configuration, in particular, grid-tied, islanded, multi microgrids, the control strategies adopted besides the identification of limitations/factors not considered in each work. Moreover, at the end of each section, the literature gap related to each category of segregated group is identified and presented. 2023 The Author(s) -
Energy management of hybrid microgrids A comparative study with hydroplus and methanol based fuel cells
Energy management is essential for the efficient operation of microgrids with reduced energy costs and minimized emissions. Energy management of PV/battery/fuel cell/diesel generator-based microgrid to minimize the operations cost considering battery degradation and emissions for a fully functional microgrid existing in the campus of Sultan Qaboos University, Oman, is presented in this work. A microgrid with a state-of-the-art hydroplus fuel cell without the necessity for hydrogen storage is presented in this study with experimentally obtained parameters. Also, a comparison of operations cost with microgrids using two different technologies of PEM fuel cells, one with hydroplus fuel cell and the second with the methanol fuel cell which requires provision for hydrogen storage is performed with three different cases; the scheduled, grid-tied, and islanded with different scenarios under grid-tied mode. The analysis proved that using a hydroplus fuel cell instead of a methanol fuel cell with hydrogen storage reduces the cost of the daily operation by 6.9% in the scheduled mode and 18.2% in the islanded mode. In the grid-tied mode three different grid limits, 20 kW, 15 kW, and 10 kW are considered. The analysis showed no reduction, 1.3% and 5.9% reduction in the operations cost respectively. The results obtained are highly promising to be applied in microgrids where conventional fuel cells are currently employed. The new technology of fuel cells introduced in this study, possesses the advantages of near zero emissions and reduced operations costs besides avoiding the perilousness of hydrogen storage. 2024 Hydrogen Energy Publications LLC -
Development of Biocompatible Barium peroxide/Pluronic F127/L-ornithine Composite for Enriched Antimicrobial, Antioxidant and Anticancer Potential: An in vitro Study
Osteosarcoma (MG-63) is a type of bone cancer affects mostly adolescents and young adults. Disease-causing microorganisms like Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae and Candida albicans pose serious illness in humans. There is a need to develop multifunctional composite to combat cancer and other most common disease caused by disease causing microorganisms. In this context, BaO2 and pluronic F127, L-Ornithine coated BaO2 (BaO2-PF127-LO) composite have been prepared and characterized by XRD, FTIR, UV-Vis, SEM, HRTEM, EDAX, and XPS analytical techniques. BaO2 and BaO2-PF127-LO were orthorhombic crystalline structure and the crystallite size was found as 32nm for BaO2 and 26nm for modified BaO2 PL studies revealed the green emission observed at 506nm for BaO2-PF127-LO composite which is absent in the case of bare BaO2. Antimicrobial activity of BaO2 and BaO2-PF127-LO was investigated. MTT assay was performed to determine the anticancer potential while the DPPH free radical scavenging assay was carried out to determine the antioxidant potential. The experiment study revealed that the BaO2-PF127-LO exhibited enhanced antimicrobial, antioxidant, and anticancer activity and low toxicity when compared to pristine BaO2. The experimental results revealed that the BaO2-PF127-LO composite holds promising potential for biomedical applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
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. -
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 -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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.