scholarly journals Intelligent Media Forensics and Traffic Handling Scheme in 5G Edge Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sa Math ◽  
Prohim Tam ◽  
Seokhoon Kim
Keyword(s):  
Traffic Congestion ◽  
Computer Software ◽  
Support Vector ◽  
Media Forensics ◽  
Svm Algorithm ◽  
Challenges And Opportunities ◽  
The Media ◽  
Communication Traffic ◽  
Application Requirements ◽  
And Control

The 5th generation (5G) communications evolved with heterogeneous user terminals and applications. A convergence of Mobile Edge Computing (MEC) and Software-Defined Networks (SDN) delivers gigantic challenges and opportunities for enhancing computing resources and user Quality of Service (QoS) in fronthaul and backhaul networks. Due to the precipitous expansion of user media in the 5G epoch, efficient media forensics methods are mandatory for specifying and offering effective safety handling based on individual application requirements. According to the exponential increment of Heterogeneous Internet of Things (HetIoT) devices, gigantic traffic will generate through bottleneck 5G fronthaul gateways. 5G fronthaul network environments consist of inadequate resources to surmount the enormous user traffic and communications, QoS will be reduced when the existence of traffic congestion occurs. To confront the aforementioned issues, this paper proposed intelligent media forensics and traffic handling scheme for controlling the Uplink (UL) transmission according to the Downlink (DL) statuses. Support Vector Machine (SVM) algorithm was applied to conduct the media forensics and MEC server integrated into fronthaul gateways, in which gateways resources are divided into UL and DL. Caching technology will be a part of 5G environments, and DL will be utilized for traffic caching. So, it is compulsory to adjust the communication traffic according to UL/DL resource utilization and control the forwarding traffic which relies on resource availability. The experiment was conducted by using computer software, and the proposed scheme illustrated a noteworthy outperformance over the conventional method in terms of diverse significant QoS factors including reliability, latency, and communication throughput.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

scholarly journals Author identification of short texts using dependency treebanks without vocabulary

2019 ◽  
Vol 35 (4) ◽  
pp. 812-825 ◽  
Author(s):  
Robert Gorman
Keyword(s):  
Text Classification ◽  
Authorship Attribution ◽  
Support Vector ◽  
Ancient Greek ◽  
Combinatorial Explosion ◽  
Independent Variables ◽  
Author Identification ◽  
Important Addition ◽  
Digital Methods ◽  
And Control

Abstract How to classify short texts effectively remains an important question in computational stylometry. This study presents the results of an experiment involving authorship attribution of ancient Greek texts. These texts were chosen to explore the effectiveness of digital methods as a supplement to the author’s work on text classification based on traditional stylometry. Here it is crucial to avoid confounding effects of shared topic, etc. Therefore, this study attempts to identify authorship using only morpho-syntactic data without regard to specific vocabulary items. The data are taken from the dependency annotations published in the Ancient Greek and Latin Dependency Treebank. The independent variables for classification are combinations generated from the dependency label and the morphology of each word in the corpus and its dependency parent. To avoid the effects of the combinatorial explosion, only the most frequent combinations are retained as input features. The authorship classification (with thirteen classes) is done with standard algorithms—logistic regression and support vector classification. During classification, the corpus is partitioned into increasingly smaller ‘texts’. To explore and control for the possible confounding effects of, e.g. different genre and annotator, three corpora were tested: a mixed corpus of several genres of both prose and verse, a corpus of prose including oratory, history, and essay, and a corpus restricted to narrative history. Results are surprisingly good as compared to those previously published. Accuracy for fifty-word inputs is 84.2–89.6%. Thus, this approach may prove an important addition to the prevailing methods for small text classification.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Over-the-counter sales of antibiotics for human use in India: The challenges and opportunities for regulation

2021 ◽  
Vol 21 (2) ◽  
pp. 147-173
Author(s):  
Gerard Porter ◽  
Anita Kotwani ◽  
Lovleen Bhullar ◽  
Jyoti Joshi
Keyword(s):  
Stakeholder Participation ◽  
Economic Incentives ◽  
Social Scientists ◽  
Over The Counter ◽  
Middle Income ◽  
Information Campaigns ◽  
Challenges And Opportunities ◽  
Regulatory Instruments ◽  
Human Use ◽  
And Control

This article assesses the regulatory framework relating to over-the-counter (OTC) sales of antibiotics for human use in India. The OTC sale of antibiotics is recognised as a pathway for the emergence of antimicrobial resistance (AMR); a serious public health challenge in need of urgent regulatory responses. Analytically, this article identifies opportunities within existing laws in India and highlights gaps that need to be filled by modifying existing laws or developing new ones. Conceptually, it suggests a need to reflect on the limits of traditional, top-down, ‘command-and control’ regulation and to think about alternative approaches. The article therefore advocates for an approach to regulation that incorporates two elements. First, it argues for a broader concept of regulation that encompasses binding as well as non-binding regulatory instruments and initiatives aimed at influencing stakeholder behaviour (including soft regulation, economic incentives, information campaigns and uses of technology). Second, it makes the case for enhanced stakeholder participation in regulatory design. The article will be relevant for health policy and drug regulators in India and other low- and middle-income countries, as well as legal scholars, social scientists and others interested in the regulation of OTC sales of antibiotics for AMR containment.

Heavy Lifters, India: Towards a Circular Economy Era

2021 ◽  
pp. 097226292110435
Author(s):  
Anupama Prashar
Keyword(s):  
Operations Management ◽  
Circular Economy ◽  
Business Models ◽  
Total Cost Of Ownership ◽  
Total Cost ◽  
Planning And Control ◽  
Cost Of Ownership ◽  
Challenges And Opportunities ◽  
And Control ◽  
The Impact

The case helps students to understand the emerging concept of linear and circular economies. It facilitates to examine the implications of circular business models such as remanufacturing on operations management decisions. It also introduces them to the concept of total cost of ownership and impact of remanufacturing on reducing total cost of ownership. The cases help students to evaluate the challenges and opportunities of remanufacturing business in emerging economy like India. This case is among the first few cases on the application of circular economy principles in context of heavy-duty and off-road sector and the impact of these principles on product design and production planning and control decisions.

Smart Traffic Light System

2021 ◽  
Vol 9 (9) ◽  
pp. 679-686
Author(s):  
Rashi Maheshwari
Keyword(s):  
Traffic Congestion ◽  
Traffic Signals ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Mathematical Functions ◽  
Light System ◽  
Smart Traffic ◽  
And Control

Abstract: Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.

scholarly journals Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals

2018 ◽  
Vol 5 (4) ◽  
pp. 92 ◽  
Author(s):  
Kathryn Huyvaert ◽  
Robin Russell ◽  
Kelly Patyk ◽  
Meggan Craft ◽  
Paul Cross ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Domestic Animals ◽  
Domestic Animal ◽  
Pathogen Transmission ◽  
Management Actions ◽  
Animal Populations ◽  
Challenges And Opportunities ◽  
Conservation Concern ◽  
Transmission Pathways ◽  
And Control

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

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