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Convergent replicated data structures that tolerate eventual consistency in NoSQL databases
The Eventual consistency is a new type of database approach that has emerged in the field of NoSQL, which provides tremendous benefits over the traditional databases as it allows one to scale an application to new levels. The consistency models used by NoSQL database is explained in this paper. It elaborates how the eventually consistent data structure ensures consistency on storage system with multiple independent components which replicate data with loose coordination. The paper also discusses the eventually consistent model that ensures that all updates are applied in a consistent manner to all replicas. 2013 IEEE. -
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition
Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE. -
Corroboration of skin diseases: Melanoma, vitiligo vascular tumor using transfer learning
The precise identification of skin disease is an exigent process even for more experienced doctors and dermatologists because there is a small variation between surrounding skin and lesions, a visual affinity between different skin diseases. Transfer learning is the approach which stores acquired knowledge while solving one problem and apply that knowledge to similar problems. It is a type of machine learning task where a model proposed for a task can be used again. Transfer learning is used in various areas like image processing and gaming simulation. Image processing is an evolving field in the diagnosis of various kinds of skin diseases. Here transfer learning is used to identify three skin diseases such as melanoma, vitiligo, and vascular tumors. The inception V3 model was used as a base model. Networks were pre-trained and then fine-tuned. Considerable growth of training accuracy and testing accuracy were achieved. 2021 IEEE. -
Cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture
The conventional methods of smart farming consume a significant percentage of the resources such as water, electricity, and manpower. This approach demands more time, money, and effort. The state of the art drip irrigation methods make use of the solenoid valve to control the water flow. The problem with such a system is reflected in its power consumption which is a significant factor for large-scale demands. The method proposed in this paper addresses this problem by developing an automated drip irrigation system that replaces components used in conventional methods with its economical counterparts in the market. A system using Node MCU, DC submersible motor, and soil moisture sensor is developed to automate the irrigation process ensuring efficient water and energy consumption. Since the proposed system utilizes economically cheaper components, it provides an upper edge over other systems in terms of expenditure and in turn economically feasible for large-scale demands. A mobile application is also developed to control, monitor, and schedule irrigation processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Covid-19 Classification and Detection Model using Deep Learning
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening. 2022 IEEE. -
COVID-19 outbreak prediction using quantum neural networks
Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
COVID-19 Pandemic: Review on Emerging Technology Involvement with Cloud Computing
Cloud computing is the latest technology that has a significant influence on everyones life. During the COVID-19 crisis, cloud computing aids cooperation, communication, and vital Internet services. The pandemic situation made the people switch to online mode. The technology helped to bridge the gap between the work space and personal space. A quick evaluation of cloud computing services to health care is conducted through this study in COVID situation. A short overview on how cloud computing technologies are critical for addressing the current predicament has been held. The paper also discusses distant working of cloud computing in health care. Moreover, cloud infrastructure provides a way to connect with different aid personnel. The patient data can be transferred to the cloud for monitoring, surveillance, and diagnosis. Thus, health care is provided instantaneously to all the individuals. Additionally, the study addresses the privacy and security-related issues with appropriate solutions. The paper also briefs on the different kind of services are provided by different CSPs that are cloud service providers to confront this epidemic. This article primarily focuses on cloud computing technology involvement in COVID, and secondary focus is on other technology like blockchain, drones, machine learning and Internet of things in COVID-19. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CPAODV: Classifying and Assigning 3 Level Preference to the Nodes in VANET Using AODV Based CBAODV Algorithm
Vehicles communicate with nearby vehicles to share high routing and traffic information in Vehicular Ad hoc Networks (VANETs) environment. Congestion and Delay in the transmission may occur due to the density of the nodes in the network. Traffic condition depends on the vehicles in Rural and Urban environment. Increase or Decrease in vehicles speed makes significant network changes when compared to the MANET environment. Road Side Terminals (RSTs) plays a major role in bridging the connection between the sender and the receiver nodes. The traditional AODV algorithm performs better when there are shortest path and link lifetime between the nodes in VANET. Giving 3 Level Preference to the nodes as High Preference (HP), Average Preference (AP) and Less Preference (LP) gives chances to nodes that have High Preference when compared to Less Preference. CPAODV model is proposed by implementing Classifying and giving preference to the RREQ to mitigate latency to the nodes. RST sends RREQ wisely based on the early model of Route Discovery stage itself. NS2 Simulator is used to analyze the strength of the proposed algorithm using QoS metrics like Throughput, Packet Delivery Ratio and End to End delay. This proposed CPAODV method performs better when compared to traditional AODV and CBAODV algorithm. 2020, Springer Nature Switzerland AG. -
Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling and Rendering
The step-by-step process of creating a bookshelf design is specified, including the ProBoolean compound primitive, applying edit poly modifier, using detach option, making use of lattice modifier, using bend modifier, using twist modifier. The manner in which materials are added to the model, together with environment lighting and renderer configuration, is defined. Procedures and methods for rendering are also defined. What we aim to achieve through our research is to create a Bookshelf design that uses materials to enhance the models. The shapes used in the model were Box, Teapot, sphere, chamfercyl, Oiltank, ProBoolean compound. The modifiers used were edit poly, bend, twist, lattice. Afterwards we used the Arnold light and material editor to enhance and glorify the model. 2023 IEEE. -
Cricket Shot Classification with Deep Learning: Insights for Coaching and Spectator Experience Enhancement
The cricket field has undergone significant transformations owing to recent technological advancements, particularly in countries like India. Technology has been used to determine projected scores, chances of winning, run rates, and many more parameters. This study centers on employing Deep Learning in cricket, focusing on the classification of different types of shots played by batsmen to aid in creating coaching strategies and enhancing the spectator experience. The proposed model uses a dataset of cricketing shots generated by collecting images from the internet, comprising 5781 images of 7 distinct shot types played by batters. The VGG-16, VGG-19, and RestNet-50 model architectures were trained for the classification task, with the best result obtained from VGG-16. Pre-processing tasks, such as scaling, augmentation, etc., were performed on the images before classification. Subsequently, 85% of the total images were used to train the model and for testing, rest 15% of images, resulting in an accuracy of 96.50% from VGG-16, 92% from VGG-19, and 78% from RestNet-50. 2024 IEEE. -
Crime Analysis and Forecasting using Twitter Data in the Indian Context
Since the late 1990s, social media has added more features and users. Due to the rise of social media, blogs and posts by common people are now a part of mainstream journalism. Twitter is a place where people can share their ideas about culture, society, the economy, and politics. India's large population and rising crime rate make it hard for law enforcement to find and stop illegal activities. This article shows the use of Twitter data to analyse, forecast, and visualise criminal activity using statistical and machine learning models and geospatial visualisation techniques. This helps law enforcement agencies make the best use of their limited resources and put them in the right places. The research aims to present a spatial and temporal picture of crime in India and is split into three parts: Classification, Visualisation, and Forecasting. Crime tweets are identified using a hashtag query argument in the tweepy python package's search_tweets function, followed by substring-keyword classification. The visualisation uses gmaps and bokeh python packages for geospatial and matplotlib for analytical applications. The forecasting portion compares AR, ARIMA, and LSTM to determine the best model for time series forecasting of crime tweet count. 2023 IEEE. -
Critical Estimation of CO2Emission Towards Designing a Framework Using BlockChain Technology
The automobile industry is a significant global contributor of carbon footprint this industry has impacted climate change, the research explores the existing methods of carbon footprint tracking and creates a framework by applying blockchain technology by connecting all the countries into one system as blockchain carries the capability to do due to its transparency, security and immutability the proposes of decentralised framework for real time tracking quarterly and implementing the necessary policies to mitigate the raising emission. The methodology encompasses of data analysis of using time series analysis globally and focusing certain parts of the world to show the emissions and creating a design that can help us in tracking the carbon footprint making all over the countries around to participate in suggesting to create a pathway for the future generations a better world as advance technologies come into the world for better ways to save the environment. 2024 IEEE. -
Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling
This paper is focused on the development of a novel system known as 'IngredEye.' It involves various approaches that can be grouped into categories, such as computer vision, including YOLOv8, a KNN prediction model, and a Flutter framework that hosts all of them in a mobile application environment. Previous studies have analyzed the application of computer vision and OpenCV recognition in cooking and proved that such approaches could enhance the level of convenience in the culinary field. This paper addresses issues like changes in lighting, occlusions, and other factors that have to be solved by the algorithms envisaged for real applications. The objective of this paper solely relies on integrating the OpenCV object detection method with comprehensive machine learning techniques specialized for the culinary field. Presenting the end-user with recipe recommendations based on the visual input they have given. 2024 IEEE. -
Crowd Monitoring System Using Facial Recognition
The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and manymore. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Crown shaped broadband monopole fractal antenna for 4G wireless applications
This paper proposes a novel crown shaped fractal antenna design suitable for 4G wireless applications. One of the promising approaches in miniaturizing the antenna size is to use the fractal geometries. Several efforts have been made by various investigators around the globe to amalgamate benefits of fractal structures with electromagnetic concepts and applications. This paper outlines a new approach in designing broadband monopole 2.1 GHz fractal antenna. The design starts with square patch antenna and goes up to third iteration for obtaining better performance and impedance matching. The proposed antenna was designed and simulated using the HFSS EM simulator. Performance analysis of the antenna was done with characteristics such as return loss, VSWR, efficiency and radiation pattern found to be good at 2.1 GHz. Wireless application demands miniaturization in system as well as antenna size with better performance, hence attempts have been made to reduce the size and improve the gain, efficiency and bandwidth of the proposed antenna. 2017 IEEE. -
Cryptocurrencies: An Epitome of Technological Populism
From a global perspective, which holds significant cryptocurrencies, this study discusses the volatility and spillover effect between the whales cryptocurrencies. Volatility in cryptocurrency markets has always been a time-varying concept that changes over time. As opposed to the stock market, which has historically and recently, the cryptocurrency market is much more volatile. The markets have evidenced many fluctuations in the prices of cryptos. As a result, countries are transforming their policies to suit financial technologies in their economic practices. Blockchain technology allows people to obtain more benefits in a financial transaction and breaks hurdles in the financial system. The study has found no ARCH effect in BinanceCoin, BT Cash, Bitcoin, Vechain, and Zcash. It is discovered that there is an ARCH effect in the case of Ethereum, Tether, Tezos, and XRP. Whale cryptocurrencies have an ARCH effect. Daily closing prices of ten cryptocurrencies, including bitcoin, from January 1, 2019, to December 31, 2020, to determine the price volatility where the bitcoin whales hold significant cryptocurrency values. It has given significant results in case of volatility since we also found that Bitcoin's largest cryptocurrencies among the sample taken for the study have less volatility than other currencies. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Cryptocurrency Security and Privacy Issues: A Research Perspective
Cryptocurrency has developed as a new mode of money exchange since it has become easier, faster and safer. The first cryptocurrency was introduced in 2009 and since then, the growth rate of cryptocurrency has been increasing drastically. As of 2020, the cryptocurrency exchange all over the world has exceeded 300%. The researchers face many challenges during their research on the various cryptocurrencies. For example, most of the high-tech companies still do not support bitcoin on mobile platforms. High-tech companies like Google and Apple are also thinking into banning the bitcoin wallet from their app stores. The work provides a review of cryptocurrency and its types, scope on the investment plans and its advantages also discussed. The growth and comparison between bitcoins and gold is also discussed. The challenges researchers face and the security issues concerning it. This review provides an overview of how the different forms of cryptocurrency are increasing from over a decade. It explains the different types and the year in which they were invented. It also gives a brief comparison with respect to bitcoin, which is one of the most used cryptocurrency. Furthermore, it gives a brief explanation on investments, and schemes for those who are new in the cryptomarket. Later emphasizes on the security issues faced by this technology. It talks about proof of work and the different data attacks the software faced and how the issues were overcome. In the end, it talks about the challenges researchers face while researching cryptocurrency. 2021 IEEE. -
Cryptographic Protocols for Securing Internet of Things (IoT)
Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. 2024 IEEE. -
Cryptography: Advances in Secure Communication and Data Protection
In the innovative work secure communication and data protection are being main field, which are emerged by cryptography as a fundamental pillar. Strong cryptographic methods are now essential given the rising reliance on digital technologies and the threats posed by bad actors. This abstract examines the evolution of secure communication protocols and data protection techniques as it relates to the advancements in cryptography. The development of post-quantum cryptography is the most notable development in cryptography discussed in this study. As quantum computers become more powerful, they pose a serious threat to traditional cryptographic algorithms, such as RSA and ECC. Designing algorithms that are immune to attacks from quantum computers is the goal of post-quantum cryptography. Lattice-based, code-based, and multivariate-based cryptography are only a few of the methods that have been investigated in this context. 2023 EDP Sciences. All rights reserved.