A Comparative Evaluation on Different Types of Recommender Systems

Khurana, Dhiraj; Dhingra, Sunita

doi:10.5373/jardcs/v11i11/20193173

Cited by 5 publications

(5 citation statements)

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“…The comparative analysis of the proposed improved hybrid recommender system is conducted against the existing content and collaborative recommender systems. These existing methods include content-based recommender system (CBRS), Pearson correlation based collaborative recommender system (PCRS), Frequencyweighted Pearson Correlation (FPC), Weighted Pearson Correlation (WPC), and hybrid recommender systems (HRS) [9] are compared. In this section, the configuration of each experiment is defined in a single table.…”

Section: Analysis Resultsmentioning

confidence: 99%

A Novel Hybrid Approach for Improving the Accuracy of Recommender Systems Using Weights

Khurana

Dhingra

2021

Algorithms for Intelligent Systems

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Section: Analysis Resultsmentioning

confidence: 99%

A Novel Hybrid Approach for Improving the Accuracy of Recommender Systems Using Weights

Khurana

Dhingra

2021

Algorithms for Intelligent Systems

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“…The Internet of Things has broad application prospects, but due to its unique composition, its application in the Internet of Things is increasing, and its threat to information security is also becoming increasingly serious. Therefore, how to effectively implement security protection for the Internet of Things has become an urgent task [15]. On the basis of in-depth research on the attack patterns and security requirements faced by the perception layer of the Internet of Things, this article adopts an intelligent and lightweight GRU-LSTM algorithm intrusion detection technology.…”

Section: Intrusion Detection Technologymentioning

confidence: 99%

Internet of Things data intrusion detection under GRU-LSTM algorithm

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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At present, the technology of the Internet of Things is not yet perfect. Due to the limited resources of nodes in the network, it is vulnerable to attacks and various possible attack methods, which pose great challenges to the information security and privacy of the Internet of Things. How to quickly identify intrusion behaviors in the Internet of Things environment is an urgent problem that needs to be solved in current network security. Based on the working characteristics of Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) networks, this article optimizes LSTM to obtain GRU (Gate Recurrent Unit) network, and compares it with traditional logistic regression (Softmax) classification methods. The research results of this article show that using dropout to train neural networks can weaken the interaction between neurons and effectively avoid overfitting. The accuracy of GRU-LSTM and GRU-Softmax in the IoT-23 (Internet of Things) database is 96% and 76%, respectively. This article proposes an IoT data intrusion detection method based on the GRU-LSTM algorithm, which has more advantages.

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“…The next extension is the Internet of Nano Things (IoNT) which uses NanoTechnology to connect objects at nano scale. Disaster management, Rescue operations, battlefields, and other critical missions uses Internet of Mission-Critical Things (IoMCT), is the next evolution thereof, and the devices equipped with integrated mobile sensors establish communication among themselves through the Internet of Mobile Things (IoMT) [66].…”

Section: )mentioning

confidence: 99%

IoT Resources and Their Practical Application, A Comprehensive Study

Kumar Saurabh

2023

IJRITCC

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The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

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A Comparative Evaluation on Different Types of Recommender Systems

Cited by 5 publications

References 0 publications

A Novel Hybrid Approach for Improving the Accuracy of Recommender Systems Using Weights

A Novel Hybrid Approach for Improving the Accuracy of Recommender Systems Using Weights

Internet of Things data intrusion detection under GRU-LSTM algorithm

IoT Resources and Their Practical Application, A Comprehensive Study

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