[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

This work proposes an innovative infrastructure of secure scenario which operates in a wireless-mobile 6G network for managing big data (BD) on smart buildings (SBs). Count on the rapid growth of telecommunication field new challenges arise. Furthermore, a new type of wireless network infrastructure, the sixth generation (6G), provides all the benefits of its past versions and also improves some issues which its predecessors had. In addition, relative technologies to the telecommunications filed, such as Internet of Things, cloud computing (CC) and edge computing (EC), can operate through a 6G wireless network. Take into account all these, we propose a scenario that try to combine the functions of the Internet of Things with CC, EC and BD in order to achieve a Smart and Secure environment. The major purpose of this work is to create a novel and secure cache decision system (CDS) in a wireless network that operates over an SB, which will offer the users safer and efficient environment for browsing the Internet, sharing and managing large-scale data in the fog. This CDS consisted of two types of servers, one cloud server and one edge server. In order to come up with our proposal, we study related cache scenarios systems which are listed, presented, and compared in this work.

IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network

Abstract Classification is a fundamental processing task in advanced network systems. This technique is exploited in 5G/6G wireless sensors networks where flow-based processing of the internet packets is highly demanded by intelligent applications that analyze big volumes of data in a limited time. In this process, the input packets are classified into specific streams by matching to a set of filters. The ternary content-addressable memory (TCAM) is used in hardware implementation of internet packets. However, due to the parallel search capabilities, this memory leads to an increase in the speed and drop of hardware bundles compared to other types of software bundles, but with the increase in the number of rules stored in its layers, the power required for searching, inserting and eliminating increases. Various architectures have been proposed to solve this problem, but none of them has proposed a plan to reduce power consumption while updating the rules in the TCAM memory. In this paper, two algorithms are presented for reducing power consumption during TCAM memory upgrades. The key idea in the proposed algorithms is the reduction in the search range as well as the number of displacements while inserting and deleting rules in TCAM. Implementation and evaluation of proposed methods represent a reduction of more than 50% of the number of visits to TCAM in both proposed algorithms, as well as reducing the update time in the second proposed algorithm compared to the first proposed algorithm which confirms the efficiency of both methods. 

https://jwcn-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13638-022-02134-2

Layered methods for updating AIoT-compatible TCAMS in B5G-enabled WSNs

In the era of smart healthcare tremendous growing, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the ”sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid and DroidEvolver) and obtain good promotion effects by our framework.

TSDroid: A Novel Android Malware Detection Framework Based on Temporal & Spatial Metrics in IoMT

The papers in this special section focus on artificial intelligence Internet of Things for medical things (AIoMT), with particular emphasis on medical sensors for remote patient monitoring and body area interfacing. Examines issues involving design and implementation, practice use, measurements, and patient monitoring. 

/pdf/guest-editorial-aiomt-enabled-medical-sensors-for-remote-1dn9prj5.pdf

Guest Editorial AIoMT-Enabled Medical Sensors for Remote Patient Monitoring and Body-Area Interfacing: Design and Implementation, Practical Use, and Real Measurements and Patient Monitoring

/pdf/correction-to-high-performance-pseudo-anonymization-of-1rkynfcm.pdf

Correction to: High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

Breast cancer is one the most common malignancies among women, along with the development of machine learning, clustering models have been utilized in this scenario. We focus on the exemplar-based clustering model in this paper. Based on the probability version of EEM clustering model, the choice of distance measurement could be more extensive. Kullbacek-Leibler distance is one of the most effective distances in Information theory, and could naturally measure the similarity between two probability distributions. Thus, this paper newly proposed a KL-distance based EEM clustering model for breast cancer by introducing KL-distance to keep target data similar to the training data into the probability version of EEM model. Deeply analyzing KL-EEM and EEM clustering models, we can conclude that the similarity based on KL-distance can be embedded into the calculation of similarity matrix. Thus, the optimization framework of EEM can be directly used to optimize the new target function of KL-EEM, which has been proved to be more effective and efficient compared with that in AP clustering model. Experiments with two real-world breast cancer datasets have been applied, and verified KL-EEM clustering model.

Mohammad Reza Khosravi

Papers

Layered methods for updating AIoT-compatible TCAMS in B5G-enabled WSNs

TSDroid: A Novel Android Malware Detection Framework Based on Temporal & Spatial Metrics in IoMT

Guest Editorial AIoMT-Enabled Medical Sensors for Remote Patient Monitoring and Body-Area Interfacing: Design and Implementation, Practical Use, and Real Measurements and Patient Monitoring

Correction to: High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

KL-EEM: A Novel Kullback-Leibler Distance Based EEM Clustering Model for Breast Cancer Identification