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Effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering
As well as setting up the sources, working with editable poly, information in the interior of the swimming pool, using turbo-smooth and symmetry modifier, this procedure of making a 3D swimming pool model is clarified. The lighting the scene and setting up the rendering, the method in which substances are added to the replica is defined. The methods and techniques of rendering are defined, too. The final rendering is the result of multiple images being drawn. The aim of our research work is to create a swimming pool design with enhancing models with materials affect. The shapes used for that are cylinder, sphere, box, plane and splines. The modifiers are editable poly, editable spline and UVW map. Finally, we used a material editor and target lights for enhancing the model. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effectiveness of Telemedicine in Disaster Relief Response Management
Due to climate change many parts of the worlds are prone to natural disasters. Thus, disaster management is the need of the hour. Effectiveness of Telemedicine in Disaster Relief response management shows the demand for telemedicine in the current time to tackle disasters. This paper investigates the history and evolution of telemedicine, their types, demand, challenges and its prospects. The proposed model, CrisisResponsive E-Health Recovery, places an approach on a concise way to manage disaster in the least time without giving up accuracy. The suggested model has the best response time as compared to the other existing model. Wide implementation of this model will result in better recovery rates in disasters. 2024 IEEE. -
Effects of Macro Economic Indicators on Foreign Portfolio Investments
In this study, both institutional and retail investors were observed making exits and entries based on macroeconomic data, utilizing measurable indicators such as GDP, inflation, bank rates, foreign exchange rates, trade volume on the national stock exchange, and portfolio investments. Employing a Vector Error Correction Model (VECM) in an econometric analysis, the study found a significant association between macroeconomic indicators and portfolio investments in India. Investors followed a discernible pattern of entering and exiting markets, with economic growth fostering greater investments. Notably, GDP, NSE Volume, and bank rates were identified as variables impacting foreign portfolio investments. In the long run, GDP positively affected foreign portfolio investments, while inflation and foreign exchange rates exhibited a detrimental influence, leading to decreased portfolio investments. Foreign Institutional Investors, prioritizing profits over business operations, focused on market sentiments, directing investments towards economies with potential performance and resulting in a higher volume of capital inflow. Overall, the study concludes that a robust economic condition attracts superior foreign portfolio investments. 2024 IEEE. -
Effects of Peer Monitoring on Student Stress Level of College Students Based on Multi-Layer Perceptron Approach
The classroom is just one of many places where the proposed approach encounter stress. Previous studies have shown that college students experience high rates of stress. It is not known if the Student Stress Inventory-Stress Manifestations (SSI-SM) is useful in identifying stressors and evaluating stress manifestations among college students. To this end, it was created a college-specific version of the Student Stress Inventory-Stress Manifestations (SSI-SM) and administered it to students to determine its validity and reliability. These procedures comprise the proposed technique and include preprocessing, feature selection, and model training. It uses Normalization as a preprocessing approach. The term' normalization' refers to the procedure of rescaling or modifying data so that all categories have the same variance. The proposed approach employed linear discriminant analysis as a means of selecting features. The models are then trained using MLP after information gain has been used to choose relevant features. The proposed approach achieves better results than the two leading alternatives, CNN and RNN. 2024 IEEE. -
Effects of the Doctrine of Discovery: A Strive to build Sustainable and Peaceful Communities in North East India
The article analyses the Doctrine of Discovery which advocates racial superiority and colonisation of indigenous lands. Indigenous people of North East India continually strives for sustainable and peaceful situation. A strong relational bond between the ethnic tribes and the environment is fundamental for self-determination, sustainability and peace. Consequently, humans bond with land stirs a readiness to sacrifice their lives for their motherland juxtaposed in the precarious context of international boundaries and past colonial annexations. The colonial-influenced literature has moulded their ethnic identity. This further leads to an upsurge of emic historical and anthropological perspective writings, framing their history, interaction with the environment, the rise of ethnic consciousness and identity politics. There is a continuous struggle to free themselves from the colonial enslavement of the Doctrine of Discovery that has ultimately encroached on their land and culture. The Electrochemical Society -
Efficiency Analysis of Modified Sepic Converter for Renewable Energy Applications
A boosting module and a traditional SEPIC (single ended primary inductance converter) are combined to create the suggested circuit. As a result, the converter gains from the SEPIC convertera's many benefits. Also, the converter that is being presented is appropriate for renewable energy sources due to its high voltage gain and continuous input current. In comparison to a traditional SEPIC with a single-controlled switch, it offers a higher voltage gain. The voltage gains of the converter that has been suggested is closely related to that of the converter that was recently developed. This converter was constructed on the foundation of the conventional converter, as well as the conventional DC-to-DC converter. One of the most important characteristics of a projected converter is that it is equipped with a single controlled device and has the capability to increase voltage gain without the utilisation of a coupled inductor structure or transformer. The non-idealities of the semiconductor devices and passive components have been taken into consideration in the analysis of voltage gain in continuous current mode (CCM). The conventional SEPIC converter can be modified by incorporating capacitors and diodes. The experimental results indicate that this converter can amplify the output voltage by approximately 10 times and has an efficiency of around 97%. The Authors, published by EDP Sciences, 2024. -
Efficiency Enhancement using Least Significant Bits Method in Image Steganography
Over the years, there has been a tremendous growth in the field of steganography. Steganography is a technique of hidden message passing i.e. transferring a message which is not visible to human eyes, through some media such as an image, music, games etc. In this particular article we focus on Image steganography which has its own advantages and has undergone a lot of improvements in the past years. The most basic image steganography can be achieved by changing the LSBs (Least Significant Bits) of the image pixels. These bits can usually be called the redundant bits. However, changing a large numbers of LSBs of an image can distort the image to an extent where it would be easily noticeable that the image maybe carrying a hidden message rendering it useless. These LSBs are changed according to the message bits allowing the person to hide their data which can be decoded later by reading the LSBs of image pixels. This paper introduces and explains a method to improve the efficiency of LSB method. 2022 IEEE -
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. -
Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution
Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security. 2023 IEEE. -
Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting
This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE. -
Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images
Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE. -
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE. -
Efficient multipath model based cross layer routing techniques for Gauss Markov movable node management in MANET
This research unveils an innovative cross-layer routing methodology tailored for managing Gauss Markov mobile nodes within MANETs. The primary focus deceits cutting-edge inspiring network performance through the efficient utilization of resources and the steadfast maintenance of mobile node connectivity. Central to this model is the implementation of joint optimization, which takes into account both node mobility patterns and resource allocation dynamics to pinpoint the most favorable data transmission pathway. Incorporating multipath routing, the methodology enables the simultaneous exploration of multiple transmission routes, thereby fortifying the network against potential link failures and disruptions. By embracing a cross-layer approach, it seamlessly integrates functionalities across network, and steering layers, thereby amplifying the complete system efficacy. Comprehensive simulations conducted reveal the superior performance of this approach compared to existing techniques, particularly in terms of network throughput, latency reduction, and augmentation of packet delivery ratios. Such findings underscore the immense potential of this methodology across a spectrum of MANET applications that demand streamlined and dependable data transmission mechanisms. 2024 Author(s). -
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE. -
Efficient Routing Strategies for Energy Management in Wireless Sensor Network
Wireless Sensor Network (WSN) refers to a group of distributed sensors that are used to examine and record the physical circumstances of the environment and coordinate the collected data at the centre of the location. This WSN plays a significant role in providing the needs of routing protocols. One of the important aspects of routing protocol in accordance with Wireless Sensor Network is that they should be efficient in the consumption of energy and have a prolonged life for the network. In modern times, routing protocol, which is efficient in energy consumption, is used for Wireless Sensor Network. The routing protocol that is efficient in energy consumption is categorized into four main steps: CM Communication Model, Reliable Routing, Topology-Based Routing and NS Network Structure. The network structure can be further classified as flat/hierarchical. The communication model can be further classified as query, coherent/non-coherent, negotiation-based routing protocol system. The topology-based protocol can be further classified as mobile or location-based. Reliable routing can be further classified as QoS (Quality of service) or multiple-path based. A survey on routing protocol that is energy-efficient on Wireless Sensor Network has also been provided in this research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. -
EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemics impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who arent wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This studys proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society -
ELCCFD: An Efficient and Enhanced Credit Card Fraud Detection using Enhanced Deep Learning Principle
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN's output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems. 2024 IEEE. -
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE.