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Should Crypto Integrate Micro-Finance option?
Purpose - The purpose of the study is to identify the readiness or acceptance by the younger population specifically, the school and university students towards the investment in cryptocurrency if the micro-finance option is included in such new asset investments. Further to this the research also focusses on the mediating factor as trustworthiness to identify the impact or influence of the variable towards the acceptance of the new asset investment.Design/methodology/approach - The research conducted through relevant literature with sufficient variables measuring with five point Likert's scale. The research also tested with hypothesis on the relationship with variables. A total of 293 valid respondents data were collected and analysed through Structural Equation model.Findings - The analysis and results suggested that the perception, awareness and trustworthiness has positive impact towards the readiness towards crypto investments. Whereas, the investment behaviour has complex acceptability towards the readiness as it failed to accept the hypothesis.Research limitations/implications - the research is limited with the younger population however the research did not focusses on the economically challenged population as they may not be afford to invest in such platforms. The future studies can also be focussed on the same area with more towards the other factors that influence the economically challenged population and identify solution their economic growth. Furthermore, the study may be game changer for the policy makers in legalising the crypto investments in the country.Originality/value - According the wider background study and with substantial literature the research is of first in its kind as per the author's knowledge to integrate the micro finance concept in crypto investments to promote the investment habit among the younger population. 2024 IEEE. -
Channel Selection Using Stochastic Diffusion Search Algorithm for Classification in Brain-Computer Interface
Utilization of the Brain-Computer Interfaces (BCI) is done via Electroencephalogram (EEG) signals that provide several environmental interactions among individuals having restricted movements owing to neurodegenerative diseases or strokes. However, the BCI system was based on Motor Imagery (MI). It was not used for any form of real-life application owing to a decrease in the performance of various Common Spatial Pattern (CSP) algorithms, especially while the actual number of channels was high. A multi-channel structure of such EEG signals can increase cost and bring down speed. Due to this, a reduction in the system cost by the detection of active electrodes during the process can increase accessibility. This way, optimization techniques in choosing electrodes can be used to determine other effective channels by employing a method of random selection. For this work, a Stochastic Diffusion Search (SDS) algorithm based on herd optimization techniques was used with four different classifiers, which were the AdaBoost, the Classification and Regression Tree (CART), the Naive Bayes (NB) as well as the K-Nearest Neighbor (KNN). The channels that were chosen frequently were determined to improve the system performance with regard to accuracy and speed. The results proved that the approach proposed was successful in bringing down the channel number and run time without affecting the accuracy of classification. 2024 Seventh Sense Research Group. -
A Framework for Enhancing Classification in BrainComputer Interface
Over the past twenty years, the various merits of braincomputer interface (BCI) have garnered much recognition in the industry and scientific institutes. An increase in the quality of life is the key benefit of BCI utilization. The majority of the published works are associated with the examination and assessment of classification algorithms due to the ever-increasing interest in electroencephalography-based (EEG) BCIs. Yet, another objective is to offer guidelines that aid the reader in picking the best-suited classification algorithm for a given BCI experiment. For a given BCI system, selecting the best-suited classifier essentially requires an understanding of the features to be utilized, their properties, and their practical uses. As a feature extraction method, the common spatial pattern (CSP) will project multichannel EEG signals into a subspace to highlight the variations between the classes and minimize the similarities. This work has evaluated the efficacy of various classification algorithms like Naive Bayes, k-nearest neighbor classifier, classification and regression tree (CART), and AdaBoost for the BCI framework. Furthermore, the work has offered the proposal for channel selection with recursive feature elimination. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Survey on Feature Selection, Classification, and Optimization Techniques for EEG-Based BrainComputer Interface
In braincomputer interface (BCI) systems, the electroencephalography (EEG) signal is extensively utilized, as the recording of EEG brain signals is having relatively low cost, the potentiality for user mobility, high time resolution, and non-invasive nature. The EEG features are extracted by the BCI to execute commands. In the feature set obtained, the computational complexity increases, and poor classifier generalization can be caused by the utilization of a lot of overlapping features. The irrelevant features accumulation could be avoided with the feature selection procedures application. The feature selection algorithms are utilized to select diverse features for each classifier. Classifiers are the algorithms that are run to attain the classification. The researchers have examined diverse classifier implementation techniques to identify the feature vectors class. A review of EEG-BCI techniques available in the literature for feature selection, classifiers, and optimization algorithms is presented in this work. The research challenges, gaps, and limitations are identified in this paper. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Deep Learning Method for Classification in Brain-Computer Interface
Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods. 2023 IEEE. -
Smart medical sensor
Medical sensors facilitate health-care applications to save a patient's life by continuously monitoring the patient's health. The combined feature of medical sensors and the fastest growing techniques that are Internet of things improves the accuracy of treatment. Internet of things techniques serve the smart and very effective medical service. Early diagnosis of the symptoms helps the health-care provider to get success in the treatment to save a patient's life. Many medical sensors are available in the health-care application that can monitor continuously patient health. Medical sensors can be wearable and nonwearable. There are some common parameters such as body temperature and a heart rate that are used to monitor human activity. These parameters are measured by using wearable and body-embedded sensors. The data collected from these parameters are analyzed by the medical devices for early detection of disease. The advanced internet of things techniques help to connect the sensors, patients, hospitals, and other medical devices. In this chapter, we highlight the use of different types of sensors with advanced technology (internet of things). 2023 Elsevier Inc. All rights reserved. -
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database
In the rapidly evolving field of education, a semantic search engine is essential to efficiently retrieve knowledge experts data. Universities and colleges continuously generate a vast amount of educational and research data. A semantic search engine can assist students and staff in efficiently searching for required information in such a big data pool. The existing systems have limitations in providing personalized recommendations that align with the individual learning objectives of students and scholars, thus hindering their educational experience. To address this, this paper proposed a KMSBOT. This novel recommendation system effectively summarizes academic data and provides tailored information for students, research scholars, and faculty, enhancing educational experiences. This paper meticulously details the development of KMSBOT, which comprises a neo4j-based knowledge graph technique, the NLP method for data structuring, and the KNN machine learning model for classification. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses traditional recommendation systems limitations and contributes to a brighter future, improving user satisfaction and engagement in academic environments. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Discriminated-SDS: A Novel Hybrid Approach for Optimizing EEG Based Brain-Computer Interface Signals Faced by Metaheuristic Algorithms
Brain Computer Interfaces (BCIs) will convert the thoughts of individuals with physical disabilities into commands for devices to enable them autonomous mobility. The Electroencephalogram (EEG) is widely favoured as a control signal due to its ease of acquisition compared to invasive recordings. While the affordability of EEG equipment allows for the use of numerous recording channels, this abundance increases computational complexity, necessitating optimal channel selection strategies to improve efficiency and classification accuracy. Deep Neural Networks (DNNs) often face scalability issues with multidimensional, locally correlated inputs, making them impractical for such applications. Convolutional Neural Networks (CNNs) are efficient for analysing BCI data but require careful hyperparameter tuning to achieve optimal performance. This paper introduces a framework for classifying BCI channel selection using deep learning techniques. The study primarily concentrates on refining the hyper parameters of deep learning algorithms through metaheuristic techniques, specifically employing Discriminated Stochastic Diffusion Search (SDS) to enhance BCI channel selection. The findings indicate that the proposed hyperparameter optimization methods, such as Discriminated-SDS, significantly enhance classification accuracy. The proposed D-SDS balances exploration and exploitation, mitigates the local optima issue, and is especially advantageous for intricate deep learner architectures such as VGGNet, ResNet, and InceptionNet. Hyperparameter optimization in EEG-based BCI systems can substantially improve performance, enhancing their efficiency and reliability. 2026, Iquz Galaxy Publisher. All rights reserved. -
Wireless Soil Health Beacons: An Intelligent Sensor-Based System for Real-Time Monitoring in Precision Agriculture
Precision agriculture is a modern technology that focuses on the crop by meeting the specific needs of the field. This research presents the Wireless Soil Health Beacons design that can be used in precision agriculture to enhance the production and real-time monitoring of the soil and field parameters. The proposed system integrates bio and physical sensors into an IoTenabled Wireless Soil Health Beacons (WSHB) to provide detailed and real-time soil health parameters. The beacons are compact and are powered by solar, which is weather-resistant and interconnected via wireless nodes. A set of beacons will be implanted to capture biological and environmental data. The biosensor module detects key soil microbiological parameters such as nitrogen-fixing microbial activity, soil pathogen presence, and general microbial population shifts indicative of soil fertility and disease conditions. The physical sensor module continuously measures soil moisture levels, temperature, and salinity. The data is passed from the nodes to a processing module, which collects and analyses the critical parameters directly related to plant growth, water management, and fertiliser optimisation. A mobile interface assists the farmers and stakeholders with the required information, such as field maps, real-time soil health indicators, and critical alerts related to drought, salinity stress, or pathogen hotspots. The proposed system forms as a multidimensional soil profiling tool capable of supporting precision agriculture. Most existing soil monitoring systems rely on environmental parameters, while the proposed system allows the continuous tracking of ecological and microbial dynamics in the area. The mesh network architecture helps the system to be redundant and enhances the outcomes. The proposed system helps with sustainable agriculture and improves the yields with minimal environmental degradation, enabling an adaptive and precise farm management system. 2025 by the authors. -
Designing a One-Pot Ternary Fe-Mn-Zn Oxide Positive Electrode with Enhanced Energy-Storage Properties for Hybrid Supercapacitors
In recent years, ternary metal-oxide nanocomposite-based active electrodes have been investigated more effectively for supercapacitor applications due to the existence of a greater number of electroactive sites and the synergistic effect of three different transition-metal ions. Herein, Fe-Mn-Zn oxide ternary nanocomposites are synthesized using a simple and cost-effective one-pot hydrothermal approach. The characterizations of XRD, FTIR, FESEM, EDX, HRTEM, and XPS are analyzed for the synthesized Fe-Mn-Zn oxide nanocomposites to study their phases, functional groups, morphologies, purity, and binding energies. The electrochemical characteristics for the developed electrodes are studied in a three-electrode technique using CV, GCD, EIS, and a cyclic stability test. As expected, the ternary nanocomposite electrode of Fe-Mn-Zn oxide reveals a maximum specific capacitance (Cspc1) of 1673.4 F/g in comparison to other developed electrodes of ZnFe2O4 (271.7 F/g) and ZnMn2O4 (412.7 F/g) at the appropriate scan rate of 10 mV/s. In addition, the Fe-Mn-Zn oxide ternary nanocomposite active electrode exhibits 2616.25 F/g of total capacitance (qT**), 686.94 F/g of outer capacitance (qO**), and 1929.30 F/g of inner capacitance (qI**) which are determined by Trasatti analysis. Moreover, the fabricated hybrid supercapacitor device provides a good specific capacitance of 320.8 F/g, a high energy density of 75.3 Wh/kg at the power density of 649.9 W/kg at 1 A/g of current density range, and 88.75% of superior capacitive retention over 10,000 cycles at 10 A/g. Therefore, a ternary metal-oxide nanocomposite electrode is proposed to be a promising material for energy-storage devices. 2024 American Chemical Society. -
Designing a One-Pot Ternary Fe-Mn-Zn Oxide Positive Electrode with Enhanced Energy-Storage Properties for Hybrid Supercapacitors
In recent years, ternary metal-oxide nanocomposite-based active electrodes have been investigated more effectively for supercapacitor applications due to the existence of a greater number of electroactive sites and the synergistic effect of three different transition-metal ions. Herein, Fe-Mn-Zn oxide ternary nanocomposites are synthesized using a simple and cost-effective one-pot hydrothermal approach. The characterizations of XRD, FTIR, FESEM, EDX, HRTEM, and XPS are analyzed for the synthesized Fe-Mn-Zn oxide nanocomposites to study their phases, functional groups, morphologies, purity, and binding energies. The electrochemical characteristics for the developed electrodes are studied in a three-electrode technique using CV, GCD, EIS, and a cyclic stability test. As expected, the ternary nanocomposite electrode of Fe-Mn-Zn oxide reveals a maximum specific capacitance (Cspc1) of 1673.4 F/g in comparison to other developed electrodes of ZnFe2O4 (271.7 F/g) and ZnMn2O4 (412.7 F/g) at the appropriate scan rate of 10 mV/s. In addition, the Fe-Mn-Zn oxide ternary nanocomposite active electrode exhibits 2616.25 F/g of total capacitance (qT**), 686.94 F/g of outer capacitance (qO**), and 1929.30 F/g of inner capacitance (qI**) which are determined by Trasatti analysis. Moreover, the fabricated hybrid supercapacitor device provides a good specific capacitance of 320.8 F/g, a high energy density of 75.3 Wh/kg at the power density of 649.9 W/kg at 1 A/g of current density range, and 88.75% of superior capacitive retention over 10,000 cycles at 10 A/g. Therefore, a ternary metal-oxide nanocomposite electrode is proposed to be a promising material for energy-storage devices. 2024 American Chemical Society. -
A new stepwise method for selection of input and output variables in data envelopment analysis
Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welchs statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information. 2021 the author(s). -
Efficiency of Indian Banks with Non-Performing Assets as Undesirable Outputs
The performance evaluation of any banks is of utmost importance for bank management, investors, and policymakers. Due to globalization, all the banks are working in a competitive environment. Several risk factors affect the operational efficiency of banking system. This study aims to evaluate the efficiency of Indian banks with NPAs as uncontrolled variables. Due to the nature of NPAs, these are assumed as undesirable outputs in the DEA modelling. The results reveal that public sector banks experienced more input losses due to NPAs compared to private banks. The private banks experienced more loss in inputs due to the scale of operation. The Wilcoxon Signed-Rank test shown that the impact of NPAs and scale of operation are statistically significant at 0.05 level. 2023 American Institute of Physics Inc.. All rights reserved. -
The impact of Covid-19 on global upstream and downstream supply chain management activities
COVID-19 pandemic has affected thousands of people worldwide; with significant economic changes in the past and to the changes to be made for future. Many organisations especially; The Intergovernmental economic organisation (OECD - The Organisation for Economic Co-operation and Development) warned the companies and industries on the global economic cut, the corona virus will be boarding. The global economy and international markets pitched down with the spread of corona virus spreading from China which is the world's second largest economy to other countries including Asia; Europe; Australia; Europe; America and the Middle East. Many economies came up with many policies to prevent the further spread of this virus; including restrictions on travel and quarantines; which has disrupted international supply chains affecting a lot of business operations and dwindle revenues. About 75 percent of business including Wholesale; Manufacturing; Retail and Services in China and about 51,000 companies have this impact at a global level according to data from Dun and Bradstreet. The success or failure of every Business depends on how well they manage their supply chain management activities. The impact of corona virus on supply chain activities is twofold. One is; Upstream Supply chain management where companies should monitor the backward integrated activities in procuring the inventory; which has accommodated a loss in the production because of closure of factories and a slowdown in the economy. Second is; Downstream Supply chain management where the intermediaries and middlemen face a lot of problems because of scarcity in inventory and many quarantine measures taken by many economies. Many disruptions in both Upstream and Downstream Supply chains lead to severe scarcity of inventory which was experienced globally by all the economies. This situation has made many economies to think of the inter connectivity and inter dependency among global nations in terms of supply chain. This article is aimed to highlight the effects and changes COVID-19 pandemic has brought in the supply chain industry from both Upstream and Downstream perspective. 2022 Author(s). -
Artificial Intelligence & Automation: Opportunities and Challenges
Artificial Intelligence (AI) and Automation innovation are growing at a steady rate that are changing organizations and bringing efficiency and adding to the economic development. The utilization of AI and robotization will likewise help improve different areas from wellbeing to horticulture. Furthermore, utilizing Automation and Artificial Intelligence would, follow the schedule, transform the idea of work and the working environment itself. For sure, machines will actually do large numbers of the undertakings typically done by people, just as supplement manual work and play out certain errands that an individual wouldn't have the option to do. Consequently, AI and mechanization have a great deal to bring to organizations and enterprises worldwide. This research paper comes up with a rundown through the blooming of Artificial Intelligence and Automation. We explored the existing potentiality of cognitive emerging technologies. This paper outlines the discussion about artificial intelligence and automation technologies and an overview of the applications. 2023 American Institute of Physics Inc.. All rights reserved. -
Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE. -
A shap-enhanced PCA-DBSCAN framework for interpretable retail customer segmentation and strategic insight
The rapid expansion of online retail underscores the critical need for precise customer segmentation to drive personalized marketing, reduce churn, and boost lifetime value. This study develops an end-to-end, highly interpretable segmentation pipeline encompassing advanced feature engineering, dimensionality reduction, exhaustive hyperparameter tuning, and robust validation to reveal stable, actionable customer groups in a large, real-world UK online-retail dataset (541,909 records). We augment the classic RFM (Recency, Frequency, Monetary) framework with: TPAC TF-IDF embeddings of item descriptions, holiday-purchase flags, and exponential recency decay; CACV net monetary value and cancellation ratios. After outlier filtering on RFM scores, we apply PCA (230 dimensions) and compare ten clustering methods (selected to represent major algorithmic paradigms: centroid-based [K-Means], probabilistic [GMM], hierarchical [BIRCH, Agglomerative], density-based [DBSCAN, OPTICS, HDBSCAN], graph-based [Spectral], message-passing [Affinity Propagation], and mode-seeking [Mean Shift]). We perform a full grid search per algorithm using a 'safe' silhouette scorer (ignoring noise) and also report Davies-Bouldin and Calinski-Harabasz indices. Temporal stability is assessed via adjusted Rand indices across time splits, and cluster interpretability is enhanced through SHAP-based feature importance analyses. By integrating textual, temporal, and cancellation behaviors into segmentation followed by systematic tuning and multi-metric validation our pipeline delivers superior cluster quality and actionable business insights compared to prior work. Segments directly enable strategic interventions: 'High-Decay Loyalists' (precision = 0.92) receive VIP retention offers yielding 2231% ROI lift, while 'At-Risk Cancellers' (recall = 0.89) trigger targeted win-back campaigns. We also demonstrate a reproducible framework for selecting both model and feature set. DBSCAN (? = 0.3, min_samples = 3 on 10 PCA components) achieved the best silhouette score (0.986), markedly exceeding the 0.72 benchmark in the literature. Agglomerative clustering (average linkage, 2 clusters) scored 0.776, while OPTICS and Spectral Clustering also outperformed classical Gaussian- or centroid-based models. A temporal ARI above 0.8 confirms cluster stability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. -
The Role of Machine Learning in Shaping Virtual Reality Educational Experiences: A Multi-Model Analysis
The integration of Virtual Reality (VR) in educational settings offers unique opportunities for enhancing learning experiences and outcomes. This study evaluates the efficacy of various machine learning models in predicting student engagement and educational outcomes within VR-enhanced learning environments. Utilizing a dataset comprising 5,000 entries related to VR usage in education, we applied both classification and regression machine learning techniques to predict binary outcomes (e.g., the usage of VR in education) and continuous outcomes (e.g., levels of student engagement). Models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and advanced regression techniques including Ridge, Lasso, Polynomial Regression, and Support Vector Regression (SVR) were systematically analyzed. The performance of each model was assessed based on accuracy, precision, recall, F1-score, R2, MSE, and MAE. We find that models such as SVM and Random Forest performed well for classification tasks and handled imbalances in classes gracefully, while SVR and Random Forest Regressor did a better job at regression tasks, being able to capture complex, nonlinear relationships in the data. It highlights the possibility that machine learning can accurately predict outcomes of VR engagement and perhaps can help inform VR-based education design to be more effective. The goal of this comparative analysis is to provide guidance for educators and technologists in deciding which machine learning strategy is suitable to facilitate education in VR with regard to improving educational outcomes. 2025 IEEE. -
Integrating Vertical Greening Systems for Urban Heat Mitigation and Well-being in Bengaluru's High-Rise Buildings: A Literature Review and Pilot Study
Rapid urbanization in Bengaluru has aggravated the Urban Heat Island (UHI) effect and decreased green space in high-rise developments. This phenomenon creates elevated "heat hotspots"that increase cooling energy demand and impact public health, social equity, and economic sustainability. To mitigate these issues, balcony greening and other Vertical Greening Systems (VGS) are considered nature-based solutions. This research paper integrates a comprehensive literature review of VGS performance with a pilot study examining Bengaluru residents' perceptions. The pilot study comprises a cross-sectional survey of 55 participants (95% CI: 13.2%). Existing literature demonstrates VGS effectiveness in reducing surface temperature by 2-4C and ambient temperature by 1-3C, thereby reducing cooling energy requirements by 15-23%. Survey results indicate high acceptance (80.9%, 95% CI: 68.5-89.7%), with 87.5% (95% CI: 76.0-94.1%) recognizing VGS benefits for cooling and psychological stress reduction. However, maintenance burden (54.5%), structural concerns (25.5%), and native flora scarcity (73.2%) were identified as significant barriers. Chi-square analysis revealed statistically significant associations between acceptance levels and perceived benefits (?2 = 18.42, p < 0.001), indicating strong adoption potential when barriers are addressed. This research paper offers critical insights into tropical high-rise vertical greening perceptions, informing climate-resilient urban development policies for Bengaluru and similar megacities. Published under licence by IOP Publishing Ltd. -
Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model
Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost. 2024 IEEE.