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A Reconfigurable Multilevel Inverters with Minimal Switches for Battery Charging and Renewable Energy Applications
In recent years, classical inverters such as the H-bridged cascaded multilevel inverter, flying capacitor, and flying capacitor multilevel inverter have contributed in electric vehicle and non-conventional energy applications. Due to higher switching and conduction losses, as well as a greater number of power switches and driver circuits, conventional multilevel inverters do not achieve the highest performance. To obtain higher performance while reducing power losses and total harmonic distortion, individual switches are controlled by logic gates. In this proposed work, one of the inverters is considered symmetrical voltage another is asymmetrical voltage for implementing these effective topologies. The proposed single-phase seven-level voltage output and current for both symmetric and asymmetric multilevel inverters are employed to test the intended computation. The MATLAB/Simulink tool is used to implement and investigate the various parameters of proposed topologies. 2022 IEEE. -
Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE. -
Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
This research introduces a hybrid model for copper price prediction, employs advanced machine learning models (linear regression, random forest, SVM, Adaboost, ARIMA), and utilizes the SHAP method for model interpretability. The study focuses on transportation-related variables over a 10-year period from Bloomberg Terminal, employing STL decomposition for time series forecasting. Key features impacting copper prices are identified, emphasizing the significance of demand, transportation, and supply. The Random Forest model highlights the critical role of demand. Addressing transportation supply constraints is crucial for enhancing model output in the dynamic copper market. 2024 IEEE. -
Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks
Inflation is one of the main issues affecting the world economy right now, necessitating the accurate inflation prediction for the development of tools and policies by the monetary authorities to prevent extreme price volatility. Expectations of inflation influence many financial and economic actions, and this dependence motivates economists to develop techniques for precise inflation forecasting. Nearly everyone in the economy is impacted by inflation, including lending institutions, stock brokers, and corporate financial officials. In many cases, inflation determines whether a firm will accept a particular project or if banks will make a particular loan. These different economic actors can modify their financial portfolios, strategic goals, and upcoming investments if they are able to forecast changes in inflation rates. The multiple interaction economic components that depend on inflation will be better understood by economic agents operating in a business context if inflation forecasting accuracy is improved. There are numerous techniques to forecast inflation ranging from basic statistical methods to complex neural network methods. Therefore, this paper employs LSTM model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the statistical model has good performance in predicting India's inflation rate compared to deep learning methods in case of smaller dataset. 2023 IEEE. -
Compact Dual-Band Millimeter Wave MIMO Antenna for Wireless Communication Systems
The article presents the compact dual-band MIMO antenna resonating at 27.5 and 32 GHz. The radiating structure is a rose-shape with elliptical slots and a horizontal slit to achieve the above resonances. The MIMO antenna dimension is 6.2 0 mm2, where an edge-to-edge distance of 1.82 mm separates radiating elements. The ground plane has simple slits to suppress the mutual coupling. The simulation results of the MIMO antenna is validated through measured and diversity parameter results. 2024 IEEE. -
Search Engine Optimization for Digital Marketing to Raise the Rank, Traffic, and Usability of the Website
According to the Content Marketing Institute, 93% of online experiences start with search. That is the explanation search. Thats why search promoting is a crucial procedure for all organizations to improve and develop their organizations. At that time the marketers and the clients who paid for advertisements started analyzing SEO and SEM. Web crawler promoting expands the perceivability of sites through SEO or through paid publicizing with the plan of expanding traffic to the site. SEM eludes to all advertising exercises that utilization web index innovation for promoting purposes. These incorporate SEO, paid postings and advertisements, and other web crawler related administrations and capacities that will expand reach and introduction of the site, bringing about more prominent traffic. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ear Recognition Using ResNet50
Deep learning techniques have become increasingly common in biometrics over the last decade. However, due to a lack of large ear datasets, deep learning models in ear biometrics are limited. To address this drawback, researchers use transfer learning based on various pre-trained models. Conventional machine learning algorithms using traditional feature extraction techniques produce low recognition results for the unconstrained ear dataset AWE. In this paper, an ear recognition model based on the ResNet-50 pretrained architecture outperforms traditional methods in terms of recognition accuracy in AWE dataset. A new feature level fusion of ResNet50 and GLBP feature is also experimented to improve the recognition accuracy compared to traditional features. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applicability of Search Engine Optimization for WordPress (WP) Website
91 percent of online experiences begin with a search, according to the Content Marketing Institute. That is the hunt for an explanation. As a result, search marketing is a critical practice for any businesses looking to grow and improve. Marketers and clients that paid for adverts began researching SEO and SEM at that time. This pursuit plans to give knowledge into the paid and unpaid procedures of search engine marketing (SEM) and what falls under its umbrella including search engine optimization (SEO) and pay per click (PPC). So in this exploration work, we feel the most ideal approach to utilize a web search tool SEM, is such a method of Internet showcasing that incorporates the utilization of web crawler result pages to advance business sites. SEM was earlier used as a protective gadget for anything to be done with the online search marketing field and it was girdled along with SEO. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis
Gait analysis is a behavioral biometric that classifies human, based on how they walk and other variables involved in the forward movement. In this study, we have attempted to comprehend the significance of the upper portion of the body in gait analysis for human recognition. The data for this study came from the CASIA dataset, which was donated by the Chinese Academy of Sciences Institute of Automation. We began by extracting the gait energy image (GEI) from the dataset and employing principal component analysis to minimize the dimensionality (PCA). For classification, random forest, support vector machine (SVM), and convolution neural network (CNN) algorithms are implemented to recognize the human subjects. This paper provides experimental results to show the accuracy attained when classification is done on GEI of full-body images is higher than the accuracy attained when classification is done on GEI of the lower portion of the body only. It also shows the significance of the GEI of the upper portion of the body. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR
Foreign currency exchange plays an imperative part in the global business and in monetary market. It is also an opportunity for many traders as an investment option and the advance knowledge of fluctuation helps the investors making right decision on time. However, due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper implements two models based on machine learning, namely Recurrent Neural Networks (RNN) and a Hybrid model of Convolutional Neural Networks (CNN) with RNN known as CNN-RNN to assess the accuracy in predicting the conversion rate of US Dollar (USD) to Indian Rupees (INR). The data set used to verify and validate the models is the daily currency exchange rate (USD to INR) available in public domain. The experimental results show that the simple RNN model performs slightly better than the hybrid model in this particular case. Though the accuracy of the hybrid model is very high in terms of error calculation still the single RNN model is the better performer. This does not straight away reject the hybrid model rather needs more experimental analysis with changing architecture and data set. 2022 IEEE. -
Depth Comparison of Objects in 2D Images Using Mask RCNN
Getting distance of an object from a single 2D image has always been a task. Due to various reasons, it was difficult to compare from images whether an object is closer or farther from camera. In this paper, we propose an idea to compare multiple images taken from same focal length cameras and specifying the distance of an object in those images with respect to each other. Our dataset contains images of palm of hand with particular distance from camera, and the output difference can specify in which image the palm is closer to camera as compared to others and vice versa. For this model, we are using Mask RCNN to recognize the object; in our case, it has been trained to identify palm, and then giving the output of masked RCNN to a depth identifier model to specify the distance of the palm from the camera. Directly using depth identifier model cannot give correct output as distance of background from camera results in different value for distance of targeted object in different images. So, we will be using mask RCNN to specify which part of image depth model should find distance from the camera. In the final step, we take the output of the depth model and take the mean of the output generated by it and compare the means of various images to specify relative distance from each other. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. 2023 IEEE. -
Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods
This research leveraged machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to predict breast cancer from CT and MRI scans. A dataset comprising 2345 instances of malignant and benign cases was meticulously curated, with 80% allocated for training and 20% for testing. The experimental results revealed the DNN as the top-performing model, exhibiting remarkable accuracy (95.2%), precision (94.8%), recall (95.6%), and F1 score (95.2%). The ANN also demonstrated strong performance, achieving an accuracy of 93.6% with balanced precision and recall scores. In contrast, the SVM, while respectable, fell slightly behind the machine learning models in terms of overall accuracy and performance. Detailed confusion matrices further elucidated the models capabilities and limitations, providing valuable insights into their diagnostic prowess. These findings hold great promise for breast cancer diagnosis, offering a non-invasive and highly accurate means of early detection. Such a tool has the potential to enhance patient care, reduce the strain on healthcare systems, and alleviate patient anxiety. The success of this research highlights the transformative impact of advanced machine learning in medical imaging and diagnosis, signaling a path toward more efficient and effective healthcare solutions. Further research and clinical validation are essential to translate these promising results into practical applications that can positively impact patients and healthcare providers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network
This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. 2024 IEEE. -
Deep Learning Advancements in E-commerce Supply Chain Management in Forecasting and Optimization Strategies
In this study, the influence of deep learning technologies on the optimization of supply chain management in the context of the e-commerce industry is examined. Using a dataset of historical data of sales, inventories, market fluctuations, and customer and supplier details, I investigate the efficiency of different deep learning models to predict demand and facilitate the optimal balance of inventories. Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and a model proposed by the authors are defined and applied, considering their accuracy, precision, recall, and F-1 score. The results show that the proposed model outperforms traditional products, achieving 97.5% of accuracy. In the context of the comparative analysis, the specific features of CNN, LSTM, and RNN are revealed, helping to understand the benefits and drawbacks of each recommendation. As a result, the proposed model proves that deep learning technologies have the power to change the approach to predictive analytics and supply chain management, allowing practitioners to focus on strengths and overcome the weaknesses of their structures. The impact of data preprocessing and hyperparameters is also considered along with the necessity to choose the most appropriate model evaluation technique. In the future, it is possible to implement other complex deep learning models, integrate additional data, and address the problem of data scaling and heterogeneity. In the era of modern technologies, e-commerce organizations should take these findings into consideration to discover the potential of deep learning, improve supply chain performance, reduce costs, and attract clients. This research contributes to the topic of using deep learning technologies in supply chain management, promoting innovation, and changes that may affect the industry drastically. 2024 IEEE. -
An Investigation on Machine Learning Models in Classification and Identifications Cervical Cancer Using MRI Scan
This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. During the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. 2024 IEEE. -
Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach
This research includes an innovative approach to refine natural gas price predictions by employing advanced machine learning techniques, including Random Forest, Linear Regression, and Support Vector Machine algorithms. Against the backdrop of natural gas's increasing influence in the energy sector, both environmentally and economically, the study adopts a robust methodology using a comprehensive dataset from Kaggle. Through rigorous data preprocessing, feature engineering, and model training, the chosen algorithms are optimized to capture complex patterns within the data, demonstrating the potential to significantly enhance forecast precision. The application of these techniques aims to extract meaningful insights, providing stakeholders in the natural gas market with more accurate and reliable predictions, there by contributing to a deeper understanding of market dynamics and informed decision- making. 2024 IEEE. -
Enhanced Geographical Information System Architecture for Geospatial Data
[No abstract available] -
Farm field security system using CNN and GSM module
Loss of crops and the destruction of livestock have been a major problem for many people in rural areas due to grass-fed animals whose food is derived from plants. According to research 32% are herbivores [1]. Reduced emissions from deforestation as well as deforestation are the main reason for wildlife moving towards urban areas. It results in wildlife infestation and human and animal conflicts. Therefore, compensating for the rapid loss of crops and the slaughter of livestock requires animal shelter and isolation in order to restrict the entry of animals into farm fields. The paper describes an effective and reliable way to identify and repel wildlife from farmland and to send real-time data to the farmer indicating animal attack on fields. An image of an animal will be obtained by convolution neural networks using intensive reading algorithms that provide a message to the farmer using the GSM module. It uses a user alert system and the animal scaring method. The test results show that the proposed algorithm has high visual accuracy. The detection level of the test set is achievable and the detection result is reliable. 2024 Author(s).