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Novel Algorithmic Recommendation Engine for Diverse Content Discovery

Novel Algorithmic Recommendation Engine for Diverse Content ...

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international conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ashu Sharma Rakshita Raj B. Yamini Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Kancheepuram (D.t) Tamil Nadu

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

In the age of information abundance, discovering personalized content is paramount to engaging users on a variety of topics. the engine goes beyond traditional approaches by not only dynamically adjusting recommendations to the users' historical preferences, but also actively encouraging exploration of interdisciplinary areas by referencing them with other genre or domains. the recommendation engine uses a multi-layer strategy that includes collaborative and content-based filtering, hybrid models and advanced diversity metrices. It transcends the general boundaries of user preferences and fosters randomness and novelty through cross-domain recommendations. the system adapts to changing user interests by suggesting content in various formats such as articles, podcasts, videos and simulations. Community integration plays a key role, leveraging collaborative filtering to discover community-curated lists and hidden gems across various categories. Real-time trend research allows users to stay informed and engaged with the latest developments, maintaining a dynamic and up-to-date content ecosystem. Ethical considerations, transparency and user-friendly interfaces are essential elements to address bias and provide explanations for recommendations. through continuous user feedback and iterative improvements, the novel algorithmic recommendation engine for diverse content discovery aims to redefine content discovery and foster a sense of exploration, curiosity and learning across a wide range of knowledge domains.

关键词： Adaptation models Machine learning algorithms Heuristic algorithms Market research User experience Real-time systems Reliability

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Analysis of Big Data Research Hotspots Based on Keyword Co-occurrence

Analysis of Big Data Research Hotspots Based on Keyword Co-o...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Niuniu Zhang Guohe Feng National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China College of Economics And Management South China Normal University Guangzhou China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the era of mobile Internet and cloud computing, the rise of big data has profoundly impacted diverse industries and sectors, drawing significant scholarly attention. the objective of this paper is to delineate an overview of the international research landscape of big data, identifying key research hotspots and emerging trends. To gain a comprehensive overview of the current state of research in the field of big data, we employed a range of analytical techniques, including co-word analysis, clustering analysis, social network analysis, and dynamic strategy coordinate mapping. Our review encompassed articles published between 2017 and 2019, spanning a three-year period. the data were sourced from the SCI, SSCI, SPCI, and CPCI-SSH databases. the literature was subjected to a sysytematic analysis using Bibexcel, SPSS, and Gephi software, with the objective of uncovering distinct patterns and trends. the findings reveal 11 thematic clusters within international big data research. these are text mining, data fusion, distributed computing, Industry 4.0, data privacy, business intelligence, precision medicine, bioinformatics, and data mining techniques such as classification and clustering. It is noteworthy that Industry 4.0, data privacy, and text mining emerged as significant research hotspots. Furthermore, the fields of artificial intelligence, natural language processing, smart grids, energy big data, and wireless sensor networks are regarded as emerging areas of interest. It is anticipated that the convergence of bioinformatics and other pivotal areas of big data research, will serve as the impetus for future research hotspots, necessitating sustained scholarly investigation and discourse.

关键词： Text mining Data privacy Wireless sensor networks Social networking (online) Big Data Market research Software Fourth Industrial Revolution Smart grids Bioinformatics

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Blockchain-based cooperative autonomous detection of suspicious vehicles

Blockchain-based cooperative autonomous detection of suspici...

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international conference and Workshop on computing and Communication (IEMCON)

作者： Zachary Farr Mohamed Azab Effat Samir Computer and Information Sciences Virginia Military Institute Lexington VA USA Norfolk VA USA

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

Modern times have brought about increased criminal activity, and in an ever-connected world, numerous crimes go un-rectified. Car theft, while a crime, is also the basis for other crimes to be committed. In the world of smart things and ubiquitous computing surveillance cameras with small onboard computers are everywhere, from car dashboard cameras to street cameras. these devices or networks of devices are mainly referenced after a crime was committed supporting after-the-fact events rather than supporting proactive handling of potential crimes. In this paper, we present a proactive blockchain-based distributed platform to detect, verify, and track suspicious vehicles that can be a base for a heinous crime. the proposed platform takes advantage of the under-utilized resources in already-present surveillance equipment. the platform allows remote and local processing video feeds in search of suspicious vehicles, allowing police to act quickly following the identification and verification of a stolen car being detected depending on the computational power of the user's device. the platform will verify the findings by group voting using a blockchain base shared between all devices, and the subsequent tracking of the suspicious vehicle by tasking other cameras running the platform to actively scan for the vehicle. this will free up some of the limited resources our police possess, a big resource being time, and allowing these now-freed resources to be reallocated into other areas. Our system evaluation and performance show indications of prompt responses in detecting stolen cars, helping police catch the stolen cars sooner.

关键词： Law enforcement Surveillance Cameras Ubiquitous computing Automobiles Object recognition Task analysis

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Prediction of Defaulters using Machine Learning on Azure ML

Prediction of Defaulters using Machine Learning on Azure ML

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international conference and Workshop on computing and Communication (IEMCON)

作者： Abhishek Shivanna Dharma P Agrawal EECS Center for Distributed and Mobile Computing University of Cincinnati Cincinnati USA

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

Banks and lending institutions take risk in issuing new credit cards and loans to customers. Lending institutions at large need to have their own credit risk assessment system in accordance with Basel II guidelines. Many lending institutions lose a large amount of money as they do not have an accurate model to predict defaulters. the goal of credit risk management system is to accurately predict borrowers' ability to repay loans or make credit card payments in a timely manner. Researchers have taken multitude of approaches to solve this problem, and it continues to be an active area of research. Data mining and machine learning are emerging tools that are widely used by lending institutions to predict defaulters. these tools can effectively mine large dataset which is not feasible by traditional methods. In this work, we have used different algorithms including Deep Support Vector Machine (DSVM), Boosted Decision Tree (BDT), Averaged Perceptron (AP) and Bayes Point Machine (BPM) to build various models, in an attempt to better predict defaulters. Dataset, comprising of 25 attributes and 30k instances, was obtained from the repository of machine learning, University of California, Irvine (UCI). Our results show that, of all the four models used, DSVM can best predict defaulters. We believe that these models can be used to better predict defaulters by credit risk management system in banking and lending institutions.

关键词： Support vector machines Machine learning Predictive models Tools Credit cards Prediction algorithms Risk management

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A Decentralized Blockchain-Enabled Immutability Mechanism Integrated with Multimodal Biometric

A Decentralized Blockchain-Enabled Immutability Mechanism In...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Veerpal Kaur Devershi Pallavi Bhatt Sumegh tharewal Pradeep Kumar Tiwari Department of Computer Applications Manipal University Jaipur India School of Advance Computing DBS Global University Dehradun Uttarakhand India Department of Computer Applications Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

With biometric authentication becoming more common, protecting personal and sensitive data is crucial in the digital age. this study examines how blockchain and multimodal biometric technologies might improve data security and authentication. Blockchain, which is decentralized, immutable, and transparent, may solve privacy, integrity, and security challenges in centralized biometric systems. Multimodal biometric systems, which use facial recognition, iris scans, and fingerprints, reduce errors and increase spoofing resistance. this paper examines blockchain and multimodal biometrics' operational principles and presents a decentralized biometric data security framework. Tampering, unauthorized access, and system failures are reduced by the suggested solution. the advantages of distributed blockchain-based architectures in data integrity, privacy, scalability, and cyberattack resilience are clear as compared to centralized biometric systems. this research shows how blockchain and multimodal biometrics can provide safe, immutable, and decentralized authentication for digital identity systems.

关键词： Biometrics Data privacy Scalability Data security Data integrity Authentication Blockchains Cyberattack Resilience Immune system

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Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism

Effectiveness of artificial intelligence, decentralized and ...

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international conference and Workshop on computing and Communication (IEMCON)

作者： Madhawa Subasinghe Dulaj Magalage Nethmee Amadoru Lakshani Amarathunga Nilum Bhanupriya Janaka L. Wijekoon Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

Good health and wellbeing, a sustainable development goal introduced by the United Nations to be achieved by 2030. Sri Lanka is a country that highly depends on tourism. A healthcare system which consists of high quality and low-cost services and an abundance of tourist attractions makes Sri Lanka to be one of the best medical tourism destinations. Tourism and travel have contributed to the GDP of Sri Lanka by 11.1 billion USD by 2018. Lack of technological advancements within the medical sector has drawn back the ability to smoothly cater medical tourism. the proposed system aims for an advanced technological improvement that would help in further developing and contributing to medical tourism. To this end, this paper introduces an Intelligent System for Secure Channeling platform that aids medical tourism with the help of artificial intelligence and blockchain technologies. System proposes a treatment prediction and suggesting the best doctor for it and a secured network to store and access electronic health records (EHR). the yielded results show that the proposed method successfully performed treatment prediction with 79-88% accuracy.

关键词： Neural networks Medical services Blockchain Predictive models Security Electronic medical records Sustainable development

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Malayalam Fake News Detection Using Optimized Convolutional Neural Network (OPCNN)

Malayalam Fake News Detection Using Optimized Convolutional ...

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international conference on Advanced Computer Control, ICACC

作者： Aiswarya Mohan Nafla Iqbal Manaal Mashpher Department of Artificial Intelligence and Data Science Rajagiri School of Engineering and Technology Kakkanad India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

these days, a range of issues are being caused by fake news, including satirical pieces, fake news, and intentional government propaganda. Because of the blathering social media discourse, the idea of "fake news" has recently evolved. Wide-ranging consequences of fake news dissemination include the development of distorted opinions and the tampering of election outcomes to benefit specific politicians. Spammers also use captivating news headlines to entice readers to click on their advertisements. these days, it can be quite difficult to tell if the news we hear is accurate. there aren’t many methods for assessing authenticity, and the ones that are accessible are all difficult and beyond the capabilities of the average person. A fact-checking platform that gives us that chance is desperately needed. Deep Learning (DL) approaches are suited for more accurately and efficiently detecting false news because of their recent accomplishments in difficult natural language processing tasks. the suggested approach is a multi-layered, optimal CNN model for detecting fake news. the OPCNN and Word embedding are optimized for feature extraction using the hyperopt optimization method in order to produce highly accurate results. In this study, we generated word embeddings using the FastText technique. the purpose of the model is to identify news stories published in Malayalam that are either false or misleading. the model is trained on 80% of a newly compiled Malayalam dataset consisting of news articles from various online sources having 11,686 news items with 5843 false and real news entries apiece and then evaluated on the remaining 20%. the evaluation’s findings show that the suggested model works better than current methods and has a high degree of accuracy when it comes to identifying false information in Malayalam.

关键词： Visualization Accuracy Social networking (online) Voting Predictive models Data models Robustness Convolutional neural networks Fake news Videos

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Solution Recommender for System Failure Recovery via Log Event Pattern Matching on a Knowledge Graph: Demo 17

Solution Recommender for System Failure Recovery via Log Eve...

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Proceedings of the 11th ACM international conference on distributed and Event-based Systems

作者： Miyuru Dayarathna Prabhash Akmeemana Srinath Perera Malith Jayasinghe WSO2 Inc. Mountain View CA USA

ISBN: (纸本)9781450350655

System anomalies such as network interruptions, operating system halt, disk crash could result in significant financial losses to organizations. In this demonstration we describe a novel log event analysis framework called Solution Recommender which provides a ranked list of solutions to overcome such system errors. the solution recommender gathers log events via a publisher/subscriber mechanism and indexes them inside the WSO2 Data Analytics Server (DAS). Collected information is analyzed using a knowledge graph which conducts log event pattern matching to identify solutions for system failures. We have implemented the proposed approach on WSO2 Log Analyzer for WSO2 API Manager and tested its functionality. In this paper we describe our experience of implementing the log event recommender interlace, the first such recommender developed in a log event analyzer system. the insights presented here will assist practitioners with implementing such Log Event analysis solutions for real world scenarios.

关键词： Log Analysis Systems Administration Event Pattern Matching Data Visualization Events Cloud computing Application Debugging

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A Decentralized Approach for Homogenizing Load Distribution: In Cloud Data Center Based on Stable Marriage Matching 19

A Decentralized Approach for Homogenizing Load Distribution:...

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Proceedings of the 11th international conference on Management of Digital EcoSystems

作者： Disha Sangar Hårek Haugerud Anis Yazidi Kyrre Begnum Oslo Metropolitan University (OsloMet) Oslo Norway

ISBN: (纸本)9781450362382

Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded *** this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center. In search of better solutions, we have looked outside the field of computer science for inspiration. Inspired by Nobel Peace Prize winners Alvin Roth and Lloyd Shapley's work on Stable Matching [4], we borrow the concept of stable marriage matching problems where we pair pairs of underloaded servers and overloaded servers based on some notion of preferences for the purpose of homogenizing their load through exchange of VMs. Furthermore, our solution is distributed by accommodating this aspect in the original Stable Matching algorithm. We provide some real-life experimental results that demonstrate the efficiency of our approach.

关键词： distributed Load Balancing cloud computing Stable Marriage Self-Organization

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Enforcing end-to-end application security in the cloud (big ideas paper) 10

Enforcing end-to-end application security in the cloud (big ...

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Proceedings of the ACM/IFIP/USENIX 11th international conference on Middleware

作者： Jean Bacon David Evans David M. Eyers Matteo Migliavacca Peter Pietzuch Brian Shand University of Cambridge Imperial College London CBCU/ECRIC National Health Serivice

ISBN: (纸本)9783642169540

Security engineering must be integrated with all stages of application specification and development to be effective. Doing this properly is increasingly critical as organisations rush to offload their software services to cloud providers. Service-level agreements (SLAs) with these providers currently focus on performance-oriented parameters, which runs the risk of exacerbating an impedance mismatch with the security middleware. Not only do we want cloud providers to isolate each of their clients from others, we also want to have means to isolate components and users within each client's *** propose a principled approach to designing and deploying end-to-end secure, distributed software by means of thorough, relentless tagging of the security meaning of data, analogous to what is already done for data types. the aim is to guarantee that---above a small trusted code base---data cannot be leaked by buggy or malicious software components. this is crucial for cloud infrastructures, in which the stored data and hosted services all have different owners whose interests are not aligned (and may even be in competition). We have developed data tagging schemes and enforcement techniques that can help form the aforementioned trusted code base. Our big idea---cloud-hosted services that have end-to-end information flow control---preempts worries about security and privacy violations retarding the evolution of large-scale cloud computing.

关键词： cloud computing information flow control policy publish/subscribe application-level virtualisation

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