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Deep Anime Recommendation System: Recommending Anime Using Collaborative and Content-based Filtering

Deep Anime Recommendation System: Recommending Anime Using C...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Varun Prakash Sagar Raghav Shubham Sood Mrinal Pandey Mamta Arora Dept. of Computer Science & Technology Manav Rachna University Faridabad India

In the vast multitude of products and services sprawling in our world, it is often daunting to make a reasonable choice backed with statistical evidence addressing the various aspects that governs the reliability and durability of the things we opt for. As such Recommendation Systems that facilitates personalization, and always up to date filtering of products or services based on the user historical track are often celebrated as one of the most visible applications of Machine Learning and Deep Learning. It thus, usually does not come in a surprise that recommendation systems have become pivotal aspect of almost all the applications dealing with E-Business, leaving no grounds unexplored. One of such relatively uncharted examples for recommendation system, however, involves recommending anime based on user’s historical choices. The sheer potential of market sizing anime, and the long-tail nature of product or services served by online businesses thus, compelled us to propose a solution for collaborative filtering, and content-based filtering. We thus, proposed a model-based architecture that employs Gated Recurrent Neural Network along with a trivial memory-based recommendation employing Cosine Similarity to make prediction for the top K rated anime by an anonymous set of users. Moreover, since content-based filtering for recommendations tends to be inherently susceptible from loss in performance with sparse data or the data with missing values, we noticed that our Neural Network based architecture turns out far superior to Cosine Similarity based approach. Thus, our proposed research could be used by different authentic or aggregator service alike that provide anime streaming services to implement a reliable and efficient recommendation system into their existing products.

关键词： Deep learning Recurrent neural networks Filtering Collaborative filtering Optimized production technology Computer architecture Predictive models

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An Efficient Techniques For Disease Prediction From Medical data Using data Mining And Machine Learning

An Efficient Techniques For Disease Prediction From Medical ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Anjani Kumar Bhatnagar Parul Madan Ajay Rana Sandeep Sharma Sameer Sonawane C. Vimala Josphine Research Scholar Quantum University Roorkee Uttarakhand India Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India Amity University Greater Noida Uttar Pradesh India Department of EC Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh Department of Postgraduate Studies Bharati Vidyapeeth’s Institute of Management Studies & Research Navi-Mumbai Department of Artificial Intelligence and Data Science R. M. K Engineering College Kavaraipettai Tamilnadu India

Individuals are susceptible to numerous ailments in today’s global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people’s health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. This data might be used to obtain data for anticipating disease illnesses, commencing preventative measures, and improving treatment procedures long before the diseases progressed. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. Modern data mining methods are used to the obtained to analyze such characteristics that may reveal crucial and critical indications regarding the existence and progression of the medical condition.

关键词： Support vector machines Radio frequency Training Machine learning algorithms Computational modeling Predictive models Prediction algorithms

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Evaluating Serverless Architecture for Big data Enterprise Applications 21

Evaluating Serverless Architecture for Big Data Enterprise A...

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2021 IEEE/ACM 8th international conference on Big data computing, Applications and technologies (BDCAT '21)

作者： Aimer Bhat Heeki Park Madhumonti Roy Computer Science University of Illinois Urbana Champaign USA

ISBN: (纸本)9781450391641

Migration of enterprise applications to the cloud has been driven by a myriad of benefits ranging from availability of infinite computing resources to the elimination of upfront CapEx cost. However, many users still face the burden of complex framework knowledge requirement to efficiently deploy applications in the cloud. In this paper, we investigate serverless computing for performing large scale data processing with cloud-native primitives. Serverless computing environment abstracts all infrastructure handling, simplifying developers’ work who aspire to deploy applications on the cloud. With dynamic input load on the system, serverless architecture has promise to provide better resource utilization and lower costs.

关键词： Serverless computing cloud computing Function as a Service Big data processing MapReduce Enterprise applications AWS Lambda load variations

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Deep Learning in Lung and Colon Cancer classifications

Deep Learning in Lung and Colon Cancer classifications

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Krishna Mridha MD. Iftekhar Islam Shamin Ashfaq Masrur Ahsan Priyok Dipayan Barua Computer Engineering Marwadi University Rajkot India Department of Electrical and Electronic Engineering American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Software Engineering Tianjin University China Informaation Technology Marwadi Univeristy Rajkot India

ISBN: (纸本)9781665474405

The past few decades have seen revolutionary advancements in the field of Healthcare and Medicine. During this period, the underlying reasons for many fatal diseases have been unveiled, novel diagnosis techniques were invented, and medical solutions were developed. Instead of these breakthroughs, we are still vulnerable and haunted by certain diseases like cancer. Globally, cancer is the second most common cause of mortality which accounts for the death of one in every six affected individuals. Among many other types, the colon and lung are the most prevailing and life-threatening ones. Only these two variants are responsible for 25% of all cancer cases around the world. However, early lung and colon cancer detection can significantly enhance the possibility of survival. Currently, Deep Learning (DL) has facilitated the automated identification of cancer cells which is allowing us to assess more patients in a minimum time and without the help of a medical professional. The study has put forward a DL framework to classify and differentiate five types of colon and lung cancer cells. Digital Image processing (DIP) and Deep Learning (DL) algorithms are used to process and evaluate histopathological images of cancer cells. The proposed framework has obtained a maximum testing accuracy of 98.3% in identifying and classifying cancer cells. By employing the proposed model in practice, medical experts will be able to integrate an accurate and automated system for diagnosing different forms of colon and lung cancers.

关键词： Deep learning Lung Lung cancer Cancer detection data models Colon Classification algorithms

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Analysis of Arabic Songs: Abdel ElHalim as a Case Study 6th

Analysis of Arabic Songs: Abdel ElHalim as a Case Study

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Gomaa, Walid Egypt Japan University of Science and Technology Alexandria21934 Egypt Faculty of Engineering Alexandria University Alexandria11432 Egypt

ISBN: (纸本)9783030697167

Recently there has been a great deal of interest in computationally studying and investigating musicals. This has been fostered by recent advances in computational power, computational paradigms, analytics, and algorithms. Arabic is spoken by more than 400 million speakers and it is one of the most used languages over the Internet. Over the last decade, Arabic, its varieties, and dialects have begun to gain ground in the area of research within the Natural Language Processing (NLP) communities. In this work we initiate the study of Arabic songs and their lyrics. We start by studying the career work of one of the most famous and influential singers in Egypt and the Arab World during the 20th century, namely, Abdel ElHalim Hafez (). We use data analysis tools in order to shed light into the artist’s career. The work of Abdel ElHalim is very diverse including a wide range of genres from romanticism, nationalism, spiritual religion, etc. In order to celebrate this inspiring and diverse body of work, we will explore and analyze the general characteristics of his work including composers and lyricists with whom he had been working. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data Analytics

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Development of Flexible, Sharing and Leasing a Car Using MERN Stack

Development of Flexible, Sharing and Leasing a Car Using MER...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： P. Hemalatha M. Dhavavarshini Dhivya. N Department of Artificial Intelligence and Data Science IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) Villupuram India

ISBN: (纸本)9781665493970

The proposed venture will include an analysis of the key decisions and challenges encountered along the way, as well as lessons learned and potential directions for future development. The objective of this project is to build a real-world, scalable, online car reservation system that makes use of the whole MERN (MongoDB, Express, React, and ***) stack. Business transactions will be simplified and improved by implementing the suggested MERN stack automobile rental management system. The development system will enable users to efficiently manage transactions, scheduling, and automobile inventory in the car rental sector. The article introduces a peer-to-peer vehicle webservice without the need for a centralized power, allowing in cheaper rates and more open sharing of information in the system. This is the key unique aspect of the paper. In addition to the physical application and associated development environment that created the working MVP, this paper also analyses prospective improvements that, when coupled with the practical application, could serve as the foundation for the project's further phases. In many countries, renting a car is increasingly becoming the most popular form of mobility, particularly among employees and students. Users who are accustomed to sophisticated technology benefit from this convenience. The workflow and types of resources retained in the car rental industry are two areas where many revolutions have switched from traditional to online systems. Clients will be able to make reservations for their cars from everywhere in the world thanks to the Car Rental System. For the purpose of submitting information to this application, users fill out their personal information. A customer who registers on the website will have access to booking a vehicle. The suggested system is an online platform that is entirely integrated. It effectively and successfully automates manual procedures. Customers can fill up the details based on their needs th

关键词： Industries Technological innovation Job shop scheduling Switches Manuals Software Peer-to-peer computing

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Cloud computing Interoperability : An overview

Cloud Computing Interoperability : An overview

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international conference on New technologies of Information and Communication (NTIC)

作者： Messaouda Ayachi Hassina Nacer Hachem Slimani Limed Laboratory Faculty of Exact Sciences University of Bejaia Bejaia Algeria Department of Computer Science Movep Laboratory University of Science Technology Houari Boumedienne Algiers Algeria

At present, cloud computing has attracted a serious deal of research interest and attention in multiple domains. One of the core challenges in this environment is to achieve interoperability among heterogeneous cloud service providers (heterogeneous resources, APIs (Application Programming Interface), SLA(Service-level agreement) policy, etc.) to keep up with the increasing demand of cloud services and the growing requirements of user’s applications. For that, we provide in this paper an overview of the existing approaches and proposed solutions. In this setting, we aim to clarify: Who has posed the Cloud computing Interoperability (CCI) problem? What does CCI mean? When and Why CCI is needed? Where does CCI problem arise? And the key question that is: How to resolve CCI problem? For this latter, we propose a taxonomy where we distinguish between the considered factors before resolving the CCI problem, and the obtained characteristics of the proposed solutions after resolving the CCI problem. Then we study existing works of CCI according to this proposed taxonomy, where we have generated three graphs allowing us to discuss CCI solution approach VS consumer-centric, CCI solution architecture VS consumer-centric, and CCI solution approach VS CCI solution type. We have concluded that: 1) the application service model is more highlighted in the literature then the management and platform levels, 2) the provider-centric solutions use generally model based approaches and are deployed as middleware or brokers, 3) the user-centric solutions are based on the adapting methodologies and deployed as brokers, 4) the hybrid solutions are based on the adapting methodologies and offer standard or broker architectures, 5) the type of CCI solution in model based approaches is mainly corresponding to framework products, 6) the final product of adapting methodologies can be a service or a library type.

关键词： Cloud computing Adaptation models Computational modeling Taxonomy Computer architecture Libraries data models

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Detection of DoS Attack using Pearson Correlation Coefficients with Bi-LSTM and LZW Compression

Detection of DoS Attack using Pearson Correlation Coefficien...

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international conference on Parallel Distributed and Grid computing (PDGC)

作者： Ankit Agrawal Rajiv Singh Manju Khari Department of Computer Science Banasthali Vidyapith Rajasthan India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9781665454025

Access to information, software, and applications through the Internet has been made possible through cloud computing technologies. An individual consumer with a computer and an internet connection can access the applications and request the necessary amenities or resources from any location through cloud networks. With these developments in cloud networks, dangers are more likely to occur. There are several reasons that cloud services are insufficient or unable, but the two most frequent problems are cloud network component or denial of service (DoS) attack at the cloud network servers. Hence, a detection model is designed for DoS attack using the Bi-LSTM classification. In this model, the information regarding the user data is collected and pre-processed using the missing data values replacement and Min-Max normalization technique. These pre-processed data are extracted for features using the Pearson correlation coefficient and classified using the Bi-LSTM algorithm. An alarm message about the DoS assault is sent to the user after its detection, and if the data is not attacked, it is compressed using the LZW compressing algorithm and stored in the cloud. The effectiveness of the proposed model is determined and evaluated with those of the existing techniques. The evaluated performance metrics such as accuracy, error, sensitivity, and precision of the proposed model are 0.98, 0.02, 0.94 and 0.96. Thus, the evaluated values of the designed DoS attack detection model using the Pearson correlation coefficient-based features selection with Bi-LSTM classification and LZW compression technique performs better than the exiting techniques.

关键词： Correlation coefficient Training Cloud computing Sensitivity Correlation Computational modeling Predictive models

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Blockchain-Based Model for Secure IoT Communication in Smart Healthcare 2nd

Blockchain-Based Model for Secure IoT Communication in Smart...

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2nd international conference on Emerging technologies for computing, Communications, and Smart Cities, ETCCS 2021

作者： Hameed, Gawhar Singh, Yashwant Haq, Shahidul Rana, Bharti Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Jammu181143 India

ISBN: (纸本)9789811902833

Over the last decade, a dramatic growth has been witnessed in healthcare monitoring systems and wireless healthcare monitoring systems have been developed in many countries of the world. Smart healthcare is one of the key areas where IoT infrastructures and solutions are widely used to facilitate the best possible patient surveillance, accurate diagnosis, and timely operation of patients with existing diseases. Smart healthcare systems on the other hand, face various challenges including data transparency, traceability, immutability, and security. Also, most of the existing healthcare systems are often controlled by a centralized monitoring unit that poses potential risks of single-point failures during natural calamities. Being motivated by the aforementioned issues, this paper aims at leveraging blockchain technology for smart healthcare to overcome the issues and challenges that smart healthcare faces as a result of traditional security policies. We propose a theoretical Autonomous Healthcare model, which divides the whole healthcare scenario into hospital-patient collaborations. The proposed model uses multiple blockchains, a global blockchain (GBC), which is used for collaboration among hospitals. Thus, creating a global healthcare system in which a patient is not limited to a certain set of region-based hospitals and doctors. Other blockchains (RBC) are specific to the regions of the patients for problems that are minor and require less interaction and consultation of doctors. Global blockchain (GBC) is a permission-less blockchain while region blockchains (RBC) are permissioned ones. The proposed model promises a decentralized framework that facilitates sharing and exchanging, and integration of information across all the users and third parties. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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COVID-19 Patient Chest X-Rays: Automatic Detection Using Deep Learning 6th

COVID-19 Patient Chest X-Rays: Automatic Detection Using Dee...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Mostafa, Aly Elbehery, Ahmed Ashraf, Ali Mohamed, Omar Mahmoud, Ali Department of Computer Science Helwan University Helwan Egypt

ISBN: (纸本)9783030697167

Since the Coronavirus disease 2019 (COVID-19) has been declared as a pandemic. It was necessary to resort to a speedy and cost-effective method of detection than the traditional methods, which, in turn, helps to speed up and increase the accuracy of the diagnosis in the infected patients and control the spread of the virus. We aim to develop a Convolutional Neural Network (CNN) model to automate the detection of COVID-19 X-ray patients. COVID-19 could be identified in chest X-rays that contain abnormalities, such as consolidation. In this paper, numerous chest X-ray images are collected. data augmentation techniques were used to compensate for the lack of real high-resolution images. Our methodology yielded an overall accuracy: 96.9%, precision of 95.15% and recall of 99.37% © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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