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2022 ◽  
Vol 34 (3) ◽  
pp. 0-0

Based on rural population return management, governance theory, and information technology theory, this paper analyzes the specific performance of rural areas in managing population return, and describes the overview, quantity, life status, and demographic characteristics of rural population return, as well as the current situation of rural population return management. A method of managing rural population return based on a rural population return management model constructed by a machine learning algorithm is designed. The empirical results show that the method designed in this paper is low-cost, fast, and highly accurate, and is well suited for improving and expanding the system for managing rural return flows. The research in this paper provides a reference for further promoting the transformation strategy of rural governance in the context of new urbanization.

2022 ◽  
Vol 154 ◽  
pp. 107127
Amir Ali ◽  
Chunwei Zhang ◽  
Tayyaba Bibi ◽  
Limeng Zhu ◽  
Liyuan Cao ◽  

2022 ◽  
Vol 98 ◽  
pp. 107692
Azeem Irshad ◽  
Shehzad Ashraf Chaudhry ◽  
Anwar Ghani ◽  
Ghulam Ali Mallah ◽  
Muhammad Bilal ◽  

Jesmeen Mohd Zebaral Hoque ◽  
Jakir Hossen ◽  
Shohel Sayeed ◽  
Chy. Mohammed Tawsif K. ◽  
Jaya Ganesan ◽  

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

2022 ◽  
Vol 99 ◽  
pp. 102160
Sebastian Birolini ◽  
Emanuele Besana ◽  
Mattia Cattaneo ◽  
Renato Redondi ◽  
Jose Maria Sallan

2022 ◽  
Vol 22 (1) ◽  
pp. 1-27
Gaurav Singal ◽  
Vijay Laxmi ◽  
Manoj Singh Gaur ◽  
D. Vijay Rao ◽  
Riti Kushwaha ◽  

Multicast communication plays a pivotal role in Edge based Mobile Ad hoc Networks (MANETs). MANETs can provide low-cost self-configuring devices for multimedia data communication that can be used in military battlefield, disaster management, connected living, and public safety networks. A Multicast communication should increase the network performance by decreasing the bandwidth consumption, battery power, and routing overhead. In recent years, a number of multicast routing protocols (MRPs) have been proposed to resolve above listed challenges. Some of them are used for dynamic establishment of reliable route for multimedia data communication. This article provides a detailed survey of the merits and demerits of the recently developed techniques. An ample study of various Quality of Service (QoS) techniques and enhancement is also presented. Later, mesh topology-based MRPs are classified according to enhancement in routing mechanism and QoS modification. This article covers the most recent, robust, and reliable QoS-aware mesh based MRPs, classified on the basis of their operational features, and pros and cons. Finally, a comparative study has been presented on the basis of their performance parameters on the proposed protocols.

2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Guojun Wang ◽  
Keqin Li

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

2022 ◽  
Vol 15 (3) ◽  
pp. 1-25
S. Rasoul Faraji ◽  
Pierre Abillama ◽  
Kia Bazargan

Multipliers are used in virtually all Digital Signal Processing (DSP) applications such as image and video processing. Multiplier efficiency has a direct impact on the overall performance of such applications, especially when real-time processing is needed, as in 4K video processing, or where hardware resources are limited, as in mobile and IoT devices. We propose a novel, low-cost, low energy, and high-speed approximate constant coefficient multiplier (CCM) using a hybrid binary-unary encoding method. The proposed method implements a CCM using simple routing networks with no logic gates in the unary domain, which results in more efficient multipliers compared to Xilinx LogiCORE IP CCMs and table-based KCM CCMs (Flopoco) on average. We evaluate the proposed multipliers on 2-D discrete cosine transform algorithm as a common DSP module. Post-routing FPGA results show that the proposed multipliers can improve the {area, area × delay, power consumption, and energy-delay product} of a 2-D discrete cosine transform on average by {30%, 33%, 30%, 31%}. Moreover, the throughput of the proposed 2-D discrete cosine transform is on average 5% more than that of the binary architecture implemented using table-based KCM CCMs. We will show that our method has fewer routability issues compared to binary implementations when implementing a DCT core.

Badr Nasiri ◽  
Ahmed Errkik ◽  
Jamal Zbitou

In this work, we present a novel miniature band stop filter based on double negative metamaterial, this circuit is designed on a low-cost substrate FR-4 of relative permittivity 4.4 and low tangential losses 0.002. The proposed filter has a compact and miniature size of 15 mm in length and 12mm in width without the 50 Ω feed lines. The resonator was studied and analyzed with a view to achieving a band-stop behavior around its resonant frequency. The band-stop characteristics are obtained by implementing the metamaterial resonator on the final structure. The obtained results show that this microstrip filter achieves fractional bandwidth of 40% at 2 GHz. Furthermore, excellent transmission quality and good attenuation are achieved. This filter is an adequate solution for global system for mobile communications (GSM).

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