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Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
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. -
Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion
Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. 2024 IEEE. -
The Influence of Mobile Commerce on Consumer Behavior: A FCM-RF-DNN Analysis
For m-commerce vendors, the difficulty is to decipher what factors impact customer actions in the ubiquitous mobile setting. In addition, companies are attempting to incorporate social media into their mobile approach in some way. This proposed approach to the findings of a qualitative exploratory study regarding the use of social media and smartphones within the framework of mobile commerce. Keep in mind the order of importance while doing data preprocessing, feature selection, and training the model. The usual steps in getting data ready for processing, such as cleaning it, identifying users and sessions, and finding episodes. The IS-DT suggested method's implementation technique is utilized in feature selection. Unified FCM-RF-DNN models need to be trained after features have been retrieved. Two state-of-the-art approaches, RF and DNN, are outperformed by the suggested approach. Following the implementation of the method, accuracy improved by 96.13%. 2024 IEEE. -
Investor Perspectives: Evaluating the Impact of CSR on Excess Returns in Financial Companies
This research aims to provide insights into Corporate social responsibility (CSR) performance and its impact on portfolio performance. The research would contribute to the broader understanding of how investors can achieve financial success and positive societal impact through the CSR performance of financial companies. This study uses 56 financial companies data from 20132014 to 20212022. Seemingly unrelated regression has been used to examine the impact of FAMA and French factors on the return of different portfolios. The findings of this research are significant for Banks and NBFCs, which shows that all the factors of the FAMA and French model are significant in showing the portfolios results. This study demonstrates that banks with better CSR performance yield higher expected returns than NBFC portfolios. This finding confirms that increased socially responsible activities yield better returns for banks. It showed that more socially responsible companies provide better financial returns than those not focusing on these issues. This suggests that when companies invest in being responsible and doing good for society, it can lead to better financial results for them and the investors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Exploring the Adaptability of Attention U-Net for Post-operative Brain Tumor Segmentation in MRI Scans
This study explores the adaptability of a segmentation model, originally trained on pre-operative MRI data, in post-operative recurrent brain tumor segmentation. We utilized the Attention U-Net model for this study. In pre-operative training, the model achieved a Dice Coefficient of 0.92 and an IOU of 0.86 for brain tumor MRI segmentation. Due to the surgical artifacts in post-operative data, performance reduced with Dice Coefficient of 0.54 and an IOU of 0. To improve the performance, the model's architecture is fine-tuned by introducing dilated convolutions and residual connections. This refinement yielded improvements in results, with a Dice Coefficient of 0.68 and an IOU of 0.62 in the post-operative context. This improvement underscores the need for further research to select and adapt efficient models, retrain specific layers with an extensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cyber-Secure Framework for the Insecure Designs in Healthcare Industry
Sensitive data protection has been a top priority in the healthcare industry. This has led to the investigation of safe data storage and transaction. Despite various attempts to address this issue, data breaches continue to plague the healthcare industry. This study aims to investigate prevalent storage practices and security methodologies in the healthcare, recognizing the need for a robust framework. The work further extends with design of new security framework for healthcare industry. This framework identifies critical data and implement measures to prevent unauthorized access and data tempering. The industrial hype towards the implementation of adaptive machine learning craves the need for hybrid machine learning approaches to be adapted in the cyber secure framework. In order to improve security and confidentiality in the healthcare sector. Blockchain is used in the proposed cyber secure framework promising integrity of data with the features of immutability. This proposal aims to provide a comprehensive solution to the ongoing problem of protecting medical data. Grenze Scientific Society, 2024. -
A Novel Approach to Enhance Influencer Marketing in E-commerce: A Cross-A-Siamese Perspective
One of the most notable aspects of the Internet is the fact that the cost of (global) communication has been drastically decreased. Individuals may potentially reach massive audiences with their messages over the Internet due to its widespread use. With the rise of blog services, social networking platforms, etc., people's technological talents are no longer a limiting factor. Data preprocessing, feature selection, and model training should all be done in this sequence of significance. Applying fundamental data preparation techniques guaranteed the data's accuracy and relevancy. Feature selection includes the computation of an influencer's overall rank based on six important criteria, which are used for influencer identification and ranking. Feature retrieval is the first step in training Unified Cross-A-Siamese models. The proposed method outperforms two cutting-edge methods: Attention module and siamese. Accuracy increased by 95.70 percent once the approach was used. 2024 IEEE. -
Comparative Analysis of Non-Destructive Silkworm Cocoon Sex Classification using Machine Learning Models Based on X-Ray and Camera Images
Silk production plays a vital role in global economies, with sericulture heavily dependent on efficient seed production processes. Traditional methods involve manually cutting cocoons to classify silkworm sex, which leads to silk damage, labor intensiveness, and potential inaccuracies. In response, non-destructive technologies like X-ray and camera imaging have emerged, enabling sex classification without cocoon damage, thereby enhancing efficiency and reducing manual errors. This study undertakes a comparative analysis of X-ray and camera imaging methods for silkworm sex classification. X-ray imaging demonstrates superior efficiency in extracting detailed features from silkworm pupae, crucial for accurate classification. In contrast, camera imaging excels in the rapid and cost-effective classification of silkworms based on extracted features. The results reveal significant findings: using X-ray imaging model achieves 97.1% accuracy for FC1 and 96.3% accuracy for FC2, employing ensemble learning technique like AdaBoost. Meanwhile, camera imaging achieves an accuracy above 98% for both FC1 and FC2 using XGBoost, showcasing its effectiveness in real-time classification scenarios. Computational time analysis indicates that X-ray imaging is faster in feature extraction, while camera imaging consumes less memory during classification. These findings underscore the practical advantages of non-destructive imaging technologies and machine learning in revolutionizing sericulture practices. By enhancing productivity and sustainability through accurate sex classification of silkworms, these methods contribute significantly to the growth and efficiency of the silk industry. 2024 IEEE. -
An Innovative Method for Enterprise Resource Planning (ERP) for Business and knowledge Management Based on Tree MLP Model
This strategy highlights the benefits of utilizing cutting-edge IT to back up company goals and genuinely assist in changing internal procedures by implementing an ERP-appropriate solution. Any organization, no matter how big or little, can benefit from an enterprise resource planning (ERP) system, which is an integrated suite of tools designed to streamline and improve internal business operations. Staying true to this approach will ensure that you get the greatest results while training the model, selecting features, and doing preprocessing. In order to use dense vector embedding for preparing the raw system logs, ERP system logs are typically represented by a combination of alphanumeric characters. While selecting features, SIM uses Particle Swarm Optimization (PSO) to create uniform product configurations. Using a Tree-MLP, the model was trained. This new strategy outperforms the old one, including Decision Tree and MLP. A 94.30% improvement in accuracy was achieved after implementing the technique. 2024 IEEE. -
Analysing the Impact of CSR Spending by Big 4 Firms on their Financial Profitability
This study delves into this ongoing debate whether socially responsible companies perform better which leads to financial profit or instead have no impact. This study focuses on leading accounting companies i.e., PricewaterhouseCoopers (PwC), Deloitte, Ernst & Young (EY), and KPMG and whether CSR Spending impacts their financial profitability or goes unnoticed. Grenze Scientific Society, 2024. -
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE. -
Artificial Intelligence in Healthcare Supply Chain Management: A Bibliometric Analysis: Subtitle as needed (AI in Healthcare Supply Chain)
The presented paper discussed the review of Healthcare Supply Chain Management (HSCM) using Artificial Intelligence (AI). The implementation of artificial intelligence (AI) in HSCM has numerous benefits, including accurate demand forecasting of medical supplies, cost reduction, increased transparency, visibility, data-driven decision-making, enhanced supply chain resilience, streamlined healthcare operations, optimized transportation, and many more. Our approach to using AI in HSCM involved a thorough examination of the literature and bibliometric analysis. Research was started by exploring the Scopus database using suitable keywords. After the inclusion and exclusion criteria have been applied, the relevant papers were gone through full-text readings. Using Vos-viewer, the research papers were further analyzed for bibliometric analysis. 2024 IEEE. -
Development and characterization of carbon fiber reinforcement in Aluminium metal matrix composites
Carbon fibers (CF) possess exceptional mechanical properties and the highest degree of chemical stability. However, carbon reinforcement in metal matrix composites is extremely scarce due to production difficulties, particularly in obtaining a uniform distribution. Carbon fiber reinforced composites are typically made using high temperature processing processes. However, the fibers must be coated with Ni or Cu in order to achieve effective particle dispersion; otherwise, there is a larger likelihood of intermetallic compound formation, which reduces the chances for enhanced properties. In this work, the metallurgical, mechanical, and tribological characteristics of the carbon fiber reinforcement in AA 7050 are examined. Uncoated carbon fibers are reinforced into the Aluminium matrix using a low temperature processing technique known as powder metallurgy. The AA 7050 matrix reinforced with carbon fibers at various weight percentages between 0 and 1.5. The samples undergone mechanical and metallurgical testing in accordance with ASTM guidelines. The findings indicate that the 0.25 weight percent carbon fiber reinforcement in the matrix increased the material's hardness by 30% over the monolithic alloy, making it an excellent alternative for structural applications. Published under licence by IOP Publishing Ltd. -
Visual Symphony for Swift and Accurate Object Detection in Choreographed Deck of Cards
The Convolutional Neural Network model used for playing card recognition and categorization, offering trustworthy data regarding the suits of playing cards hearts, diamonds, clubs and spades as well as the corresponding numerical or alphabetical values. The model is built on a sophisticated dataset that guarantees high levels of precision for nearly all sorts of graphical representations and playing card scenarios. A wide range of entertainment andgames bands canuse the CNN idea. As aresult, the CNN-trained model is an excellent alternative for many different kinds of applications, including virtual reality games and card game automation, due to its capacity to extract and retain complex features from card pictures for accurate object identification. As a result, this research has shown how crucial deep learning models like CNNs are for enhancing computervision systems' suitability for real-world scenarios requiring precise and quick identification of objects. As a result, the suggested CNN-based approach offers a great chance to enhance cardidentification system performance and promoteadvancements in memory and gaming technology. 2024 IEEE. -
Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques
This paper presents a comprehensive study on stock price predictions by integrating market factors and sentiment analysis of news headlines. The research is divided into two modules, each employing distinct methodologies to enhance the accuracy of stock price forecasts. In the first module, market factors are investigated using three advanced algorithms: Long Short-Term Memory (LSTM), Gradient Boosting Decision Trees (GBDT), and Facebook Prophet (FBPROPHET). These algorithms are evaluated based on metric scores such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The analysis focuses on predicting high and low values of market prices for the period from January to June 2021. The comparative assessment of these algorithms provides insights into their effectiveness in capturing market trends and making precise predictions. In the second module, the paper explores the impact of news headlines on stock prices by extracting sentiment using three distinct algorithms: lexical-based analysis, Naive Bayes, and FinBERT. The sentiment analysis aims to gauge the market sentiment reflected in news articles and assess its influence on stock price movements. Prediction accuracy is calculated for each algorithm, highlighting their strengths in capturing sentiment patterns. 2024 IEEE. -
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. -
Impact of Risk Perception on Use and Satisfaction with Online Pharmacies and Proposed Use of IoT to Minimize Risks
This study investigates consumer risk perceptions regarding online pharmacies and their impact on usage frequency and satisfaction. The growing popularity of online pharmacies offers benefits such as accessibility, cost savings, and privacy. However, significant risks, including the potential for counterfeit drugs and insufficient medical oversight, raise concerns. This study has measured consumer perceptions of risk, satisfaction, and usage frequency through a survey conducted in Northeast India, excluding Sikkim (online) and Sikkim (offline). The findings reveal that the fear of receiving counterfeit medications is a significant risk factor, negatively influencing both the frequency of use and consumer satisfaction. Despite this, the impact is relatively weak, suggesting that while risk perception is a concern, it does not significantly deter online pharmacy usage. The study suggests that integrating advanced technologies such as IoT, RFID, and blockchain can mitigate these risks by ensuring the authenticity of medications in the supply chain. 2024 IEEE. -
Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach
Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024. -
The Effect of Sustainable Development Goals (SDG's) on the Financial Performance of Listed Companies
The corporate sector is emerging as a significant stakeholder in this transformative journey asnations throughout the world work to align their policies and practices with the SDGs. Theincorporation of SDGs into financial planning has made tremendous headway in India, acountry with a rapidly expanding economy and a diverse corporate landscape. The 50companies that made up the Nifty 50 at the end of 2023 are examined in this study. Twosources provided the financial data on these companies: the Bloomberg platform andSecurities and Exchange Commission (SEC) reports. Only thirty of the NIFTY 50 companieswere putting the SDGs into practise on the previously indicated date. There are fourconfigurations in the successful FP model that describe how the SDGs and FP relate to oneanother. The lack of SDGs, when combined with other variables, explains a high ROE in twoof these four configurations. The examination of the data concludes that businesses who havetraditionally attained higher FP (i.e., higher ROE) have not included SDGs into their strategy.Furthermore, the inclusion of SDGs in strategies results in a lower return on equity (ROE).The paper however takes into consideration only size and risk as the main variables tocalculate the ROE. We recommend the future researchers to consider the other financialvariables while doing the analysis to get a more insightful analysis on the effect of SDGs. Grenze Scientific Society, 2024.