Document summarization is one such task of the natural language processing which deals with the long textual data to make its concise and fluent summaries that contains all of document relevant information. The branch...
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The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 m...
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The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world’s top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote ser...
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation.
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages acco...
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GbM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.
Aims to redefine the landscape of the cottage industry with an innovative social web application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to bridge the gap between produc...
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ISBN:
(数字)9798350383867
ISBN:
(纸本)9798350383874
Aims to redefine the landscape of the cottage industry with an innovative social web application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to bridge the gap between producers and customers by providing a thriving marketplace for one-of-a-kind, handcrafted things. With a focus on encouraging job possibilities, particularly for women, the application corresponds with the wider purpose of supporting “women empowerment” and contributing to national efforts such as “Make in India.”The system study highlights the existing holes in present systems, emphasizing the necessity for a complete platform tailored exclusively to the cottage sector. An architecture diagram and extensive use case overviews for customers, producers, and administrators are included in the system design. Implementation specifics include login, identity, purchasing, and administrative operations, among others. The initiative aims to promote not just economic activity in the cottage industry, but also social issues like as empowering women and inclusive employment. The dedication to ongoing development is obvious in the plans for the future, which include tightening security measures, notably for online financial transactions. Finally, the social web application aspires to build a dynamic and safe digital area for artisans, producers, and customers, therefore leaving a lasting impression on the cottage industry in the age of technology. The chatbot is implemented using the rasa open-source framework along with the digital platform. The results of the chatbot are discussed in detail in the results and discussion section.
Internet of Things (iot) networks have produced copious amounts of data that can be used to identify intrusions. Network Intrusion Detection Systems are crucial for spotting attacks before they cause any damage as par...
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This research paper aims at investigating how the smartphone photography coupled with deep learning algorithms can close research gaps relating to inaccuracy and variation in images in diagnosing oral diseases. by usi...
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The Mobile blaze methodology is an innovative strategy that employs the Mobile Net architecture to improve the effectiveness of systems designed for detecting forest fires. Given the substantial risks forest fires pos...
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ISBN:
(数字)9798350384277
ISBN:
(纸本)9798350384284
The Mobile blaze methodology is an innovative strategy that employs the Mobile Net architecture to improve the effectiveness of systems designed for detecting forest fires. Given the substantial risks forest fires pose to ecosystems and human safety, there is a pressing need for cutting-edge technologies to ensure prompt detection and response. In this project, we adapt the lightweight and efficient Mobile Net architecture, originally designed for mobile devices, to create a streamlined and high-performance model for forest fire detection. The proposed Mobile blaze system integrates state-of-the-art deep learning techniques with the unique characteristics of Mobile Net, enabling real-time processing of high-resolution imagery from various sources, including satellite and surveillance cameras. Our model is trained on a diverse dataset of forest fire scenarios, encompassing different environmental conditions and fire intensities. The use of transfer learning facilitates the efficient transfer of knowledge from pre-trained models, enhancing the model’s generalization capabilities. To evaluate the performance of Mobile blaze, extensive experiments are conducted on benchmark datasets, demonstrating superior accuracy and speed compared to traditional approaches. The lightweight nature of Mobile blaze enables deployment on resource-constrained devices, making it suitable for edge computing applications in remote forest areas. Furthermore, the system is designed to provide reliable fire detection with minimal false positives, ensuring prompt and accurate response to potential fire incidents. Mobile blaze represents a significant advancement in forest fire detection, offering a scalable and efficient solution that can contribute to early intervention and mitigation efforts. The integration of the Mobile Net architecture showcases the potential for adapting existing deep learning models to address specific challenges in environmental monitoring and disaster management.
This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (iot), and social media. by exploring the interactions am...
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ISBN:
(数字)9798331504403
ISBN:
(纸本)9798331504410
This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (iot), and social media. by exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the iot era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.
This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, follo...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors.
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