An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

Nakıp, Mert; Karakayali, Kubilay; Güzeliş, Cüneyt; Rodoplu, Volkan

doi:10.1109/access.2021.3092228

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…It clearly shows us how a new economic class will lead our future economic development [3]. At the same time, Nakip et al creatively put forward the 3T theory of attracting creative talents and believed that talent, technology, and tolerance are the key to attracting creative talents, stimulating innovative development and promoting economic growth [4]. Sun et al proposed that "the world is not flat."…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Development of Smart City Tourism and Cultural and Creative Industries Based on Internet of Things

Liu

2022

Wireless Communications and Mobile Computing

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In order to solve the problem of asymmetric development of cultural and creative industries in smart city tourism, this paper proposes an analysis method based on the IPA model. Based on the analysis of the current situation of the tourism cultural and creative projects in the traditional block, this method combines the RMP analysis theory and the tourist satisfaction theory to study the local cultural elements and the satisfaction of tourism cultural and creative projects with the IPA model. The results are as follows: sightseeing (42.5%) is the highest motivation of tourists, followed by cultural and creative experience (16.2%), of which 84.8% are self-help tourists, and only 2.3% choose to join the group. Life custom is a significant factor influencing the satisfaction of patients who think it is very important but are actually dissatisfied. Smart tourism and creative tourism projects are considered important by the respondents, but there is a large difference between expectations and perceptions. These are the projects that managers need to focus on to correct at present. It proves that the activation of tourism cultural and creative projects should follow the market-oriented principle, the principle of reflecting characteristics, the principle of cultural guidance, the principle of effectiveness, and the principle of sustainable development in order to achieve the ultimate success.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Development of Smart City Tourism and Cultural and Creative Industries Based on Internet of Things

Liu

2022

Wireless Communications and Mobile Computing

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“…PSO simulates the social behavior of birds and fish to explore the feature space. Although it has shown promise, PSO tends to get stuck in local minima, leading to suboptimal solutions 23 , 24 . Which is done via use of Quad-Hybrid Feature Selection Algorithm (QFS) Process.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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“…IoT devices are quite simple and cannot easily be coordinated for timing or scheduling [24] based on distributed control [8,10]. Thus recent experimental results [37,38] show that machine learning techniques can be used to predict IoT traffic generated by individual devices, and other work [11,39,[46][47][48]50] designs proactive/predictive access schemes that determine the transmission times from IoT devices based on such predictions to mitigate the MAP.…”

Section: Proactive Solutionsmentioning

confidence: 99%

Mitigating the Massive Access Problem in the Internet of Things

Gelenbe

Nakıp

Marek

et al. 2022

Communications in Computer and Information Science

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The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for real-time data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.

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An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

Cited by 10 publications

References 35 publications

Integrated Development of Smart City Tourism and Cultural and Creative Industries Based on Internet of Things

Integrated Development of Smart City Tourism and Cultural and Creative Industries Based on Internet of Things

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Mitigating the Massive Access Problem in the Internet of Things

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