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Music Recommendation System Using Machine Learning

Music Recommendation System Using Machine Learning

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

作者： Rajesh Kumar Rakesh Department of Computer Science Chandigarh University Mohali PB India

The Internet plays a crucial role in society, and many apps use suggestion structure. It has created a wide range of applications, a global local area, and built for different types of data. Today's proposal framework has altered how we currently view the things that are to our advantage. Additionally, it is possible due to the data sifting method that is used to predict the customer's propensity. The most well-known applications of the recommender system are in the areas of books, news, articles, music, records, movies, and other media. The outline of the machine learning calculations and tactics developed for the proposal framework are covered in this *** have suggested a music suggestion framework dataset in this article. Three categories make up the proposal framework: collaborative filtering, content-based, and approach based on half a breed. Recommender frameworks are widely used these days, especially in informal groups and electronic industry, as communitarian sifting methods become more mature. This paper organises the community-based, i-based separating process that uses the information provided by clients, analyses it, and then offers the songs that are most suitable for the client at that particular *** paper describes this methodology, its steps, and its limitations. The recommended music list is arranged using the K-means algorithm and is based on the ratings that previous listeners have given the music. Additionally, this helps customers find the music of their choice based on the cinema insights of other customers efficiently and effectively without wasting much time in meaningless *** newly introduced recommender framework generates recommendations using a variety of information, including information about customers, the items that are available, and previous transactions stored in modified data sets. The customer could then efficiently browse the choices and find the music of their choice.

关键词： Machine learning algorithms Filtering Collaborative filtering Music Machine learning Media Motion pictures

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Lightweight Intrusion Detection System(L-IDS) for the Internet of Things

Lightweight Intrusion Detection System(L-IDS) for the Intern...

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Advancements in Smart, Secure and Intelligent computing (ASSIC), international conference on

作者： D Divya Priya Ajmeera Kiran P Purushotham Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.

关键词： Privacy Scalability Intrusion detection Information sharing Medical services Market research Internet of Things

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Dynamic regulation method of distributed generation in distribution network under 5G network

Dynamic regulation method of distributed generation in distr...

引用

2021 international conference on Smart Transportation, Energy and Power, STEP 2021

作者： Chen, Duanyun Li, Yuanjiu Bai, Jie Zhang, Hongpo Xie, Shimulin Tang, Zeyi Liu, Xujie State Grid FuJian Electric Power CO. LTD. Fuzhou350001 China State Grid Fujian Quanzhou Electric Power Supply Company Quanzhou360000 China State Grid Information and Telecommunication Group Co. Ltd. Beijing100052 China State Grid Info-telecom Great Power Science and Technology Co. Ltd. Fuzhou350001 China

Aiming at the problem of uneven power flow distribution caused by distributed power supply access, which leads to high voltage deviation and active network loss, a dynamic control method of distributed power supply in 5G network is proposed. The distributed topology structure of the distribution network under 5G network is generated, and the static topology information is extracted to establish the power partition. The distribution network in each area is connected to its own bus through different feeders. The distribution network reconstruction period is divided in the partition, and the network reconstruction period and the corresponding switch action scheme are determined. The state of distributed power supply was estimated according to reconstruction period and active/reactive power balance index, and the stability of dynamic control was determined. Based on the stability control, the dynamic control model of distributed power supply is established, and the network voltage can reach the preset target by adjusting the power of power switch. The simulation results show that the average voltage deviation of the proposed control method is 1.976, which is 4.431 and 3.783 lower than those based on data drive and flexible load regulation, respectively. The active power network loss of the control method in this paper is 0.342 MW, which is 0.385 MW and 0.346 MW lower than that of the control method based on data drive and flexible load regulation, respectively. Therefore, this method can improve the absorption capacity of distributed power supply and promote the power flow reciprocity of distribution network. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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Towards the speed enhancement of association rule mining algorithm for intrusion detection system 10th

Towards the speed enhancement of association rule mining alg...

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10th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2019 and 9th World Congress on Information and Communication technologies, WICT 2019

作者： Dasgupta, Sarbani Saha, Banani Department of MCA Techno International Newtown Block-DG Action Area 1 New town Kolkata700156 India Computer Science and Engineering Department2 University of Calcutta JD Block Sector III Salt Lake City Kolkata700098 India

ISBN: (纸本)9783030493387

Intrusion detection system is a device or a software application which is used to monitor network traffic data for suspicious activity and alert the system administrator about any malicious activity or network policy violation that has occurred. Among the several techniques proposed for anomaly detection in network audit data, data mining techniques are used for efficient analysis of network audit data to detect any abnormalities occurred due to specific types of attacks. Association rule mining algorithm an unsupervised data mining algorithm has been applied for analysis of network audit data for detecting anomalies. Due to rapid increase of internet based services, cyber security has become a challenging problem. In this paper, a frame work using association rule mining algorithm, has been proposed for detecting suspicious activity in network traffic data. Further in order to increase the speed of processing for large size network traffic data, big data processing tool Apache Spark has been used. Among the several association rule mining algorithm FP growth algorithm has been used to generate attack rules that will detect malicious attack on network audit data. For the purpose of the experiment the Kyoto dataset which is available freely online has been used. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Association rules

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Cloud Storage Ciphertext Search Scheme Based on Blockchain

Cloud Storage Ciphertext Search Scheme Based on Blockchain

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Information Technology, Communication Ecosystem and Management (ITCEM), international conference on

作者： LuWei Bao GeHao Lu School of Information Science and Engineering Yunnan university Kunming China

The fast search of cloud storage encrypted documents is a research hotspot of searchable encryption at present. However, with the increase of the amount of data, the retrieval efficiency of the cloud server continues to decrease, and its security is difficult to be guaranteed. In order to improve the security and reliability of data in cloud storage, this paper studies the verification of multi-keyword search and document tamper-proof. A novel multi-keyword verifiable search encryption scheme is proposed, which combines blockchain smart contract, local sensitive hash algorithm, asymmetric encryption algorithm and other technologies to achieve fast search and security verification of cloud storage encrypted data. The experimental results show that the scheme can not only ensure security and query accuracy, but also improve query efficiency.

关键词： Cloud computing Smart contracts Ecosystems Encryption Blockchains Servers Reliability

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Real-Time Calibration Method of Air Quality data Based on AdaBoost Training Model 22

Real-Time Calibration Method of Air Quality Data Based on Ad...

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Proceedings of the 2022 11th international conference on computing and Pattern Recognition

作者： Xuejing Jiang Xun Sun Qiuming Liu Jiangxi Vocational and Technical College of Information Application China School of Software Engineering Jiangxi University of Science and Technology China

ISBN: (纸本)9781450397056

At present, a large number of cities are facing the situation of "garbage besieged", and the existing garbage disposal system can no longer meet the increasingly complex factors. With the development of a new generation of Internet of Things technology, integrating the knowledge and technology of related disciplines such as network and geographic information, it is possible to build a real-time monitoring platform for seepage and odor in landfills to complete gas monitoring. The author of the paper reviewed the related technologies of the Internet of Things, and proposed the design scheme of the online monitoring system for odor and seepage of the Maiyuan garbage dump in Nanchang City, selected 5 monitoring items, completed the data collection, and used the collected data to use Matlab and python software to carry out simulation analysis and prediction, and finally discuss the main factors and treatment measures of environmental pollution, provide theoretical guidance for relevant managers to improve the overall management decision-making level of urban domestic garbage dumps, and draw some practical conclusions.

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GeauxTrace: A Scalable Privacy-Protecting Contact Tracing App Design Using Blockchain

GeauxTrace: A Scalable Privacy-Protecting Contact Tracing Ap...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Tao Lu Fang Qi John Ner Tianqing Feng Brian Cunningham Lu Peng Electrical and Computer Engineering Louisiana State University Baton Rouge USA Dept. of Computer Science Tulane University New Orleans USA Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana and Champaign USA

Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second.

关键词： COVID-19 Privacy Costs Bluetooth Pandemics Blockchains Spatiotemporal phenomena

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Multi-task unmanned swarm control method based on dynamic optimal path planning

Multi-task unmanned swarm control method based on dynamic op...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), international conference on

作者： Chao Qu Hongrui Lin Xiaoyang Jin School of Computer Science and Technology Dongguan University of Technology DongGuan China School of computer science and engineering South China University of Technology GuangZhou China Guangdong Hongqin Communication Technology Co. Ltd. Dongguan China

With the continuous development of 5G, Internet of Things, unmanned driving, and cluster control technologies, the cooperative work of homogeneous or heterogeneous unmanned clusters will become the main application direction of unmanned systems. We proposed a multi-task unmanned swarm control method. The method establishes scene model by using cellular automata, uses cloud computing combined with multi-level edge computing as the control structure, and uses dynamic optimal path planning as the control algorithm to realize the coordinated control of unmanned clusters. We simulate the cluster cooperative control effect of this method and existing static control methods in theoretical scenarios and actual road network environments. This method solves the problem of deadlock caused by static shortest path planning of cluster control, and reduces the computation requirement of the whole system through multi-layer control calculation. The advanced nature of the method is illustrated by the analysis of the simulation results.

关键词： Federated learning Heuristic algorithms Simulation Roads Stochastic processes System recovery Multitasking

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Itemset mining based episode profiling of terrorist attacks using weighted ontology 5th

Itemset mining based episode profiling of terrorist attacks ...

引用

5th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2020

作者： Srivastava, Saurabh Ranjan Meena, Yogesh Kumar Singh, Girdhari Department of Computer Science & Engineering Malviya National Institute of Technology Jaipur India

ISBN: (纸本)9789811533822

Itemset mining is a prominent research problem of data mining domain. It is extensively utilized for knowledge discovery in domains dealing with multi-component records or itemsets. Terrorism is a similar domain where every terrorist attack carries attack attributes such as location, target and attack type as components. Treatment of terrorist attack episodes as itemsets can facilitate effective pattern analysis and forecast of future attack episodes. This paper introduces a novel approach of mapping three major attributes of terrorist attacks taken place in a region in a single weighted ontology. The weighted ontology is later employed to discover and forecast useful information about possible attack episodes in the future. © Springer Nature Singapore Pte Ltd 2021.

关键词： Terrorism

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VISUALIZATION of EXPERIMENTAL data in WEB-BASED VIRTUAL REALITY 9

VISUALIZATION of EXPERIMENTAL DATA in WEB-BASED VIRTUAL REAL...

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9th international conference "Distributed computing and Grid technologies in science and Education", GRID 2021

作者： Korečko, Štefan Va'a, Martin Fekete, Martin Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia Faculty of Science Pavol Jozef Šafárik University in Košice Slovakia

Technological advances in the field of virtual reality and personal computation in general brought us to the era of web-based virtual reality, where virtual environments can be accessed directly from web browsers and without the need of installation of any additional software. Such online virtual environments seem to be a promising tool for scientific data visualization. When accessed through appropriate hardware, such as VR headsets, they also offer full immersion and isolation from external influences. In this paper, we present a prototype solution for a histogram visualization in online virtual environments. The prototype, named NDMVR, has been implemented using A-Frame framework for visualization, *** for compositionality and JSROOT for histogram data acquisition. Its user interface is primarily adjusted to personal computers and VR headsets. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Visualization

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