ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Pelatihan Implementasi Machine Learning pada Bidang Pendidikan

Machine learning is a machine that can learn like humans. Machine learning (ML) technology was developed so that machines can learn by themselves without direction from the user. Machine learning consists of various disciplines such as statistics, mathematics and data mining so that machines can learn by analyzing data patterns without the need to be explicitly reprogrammed. Making machine learning applications is not easy because you have to have good understanding of methods and programming skills. Therefore, this service uses a solution to improve the abilities of the participants, namely a training approach by presenting material and demonstrating the use of machine learning in midwifery education. The activity was carried out on April 21 2021 online via the Zoom Meeting application with student participants. Based on the results of the material presentation session and hands-on practice using the Python programming language at Google Colab, it showed that the participants looked enthusiastic in following the material. Not only that, the participants know various machine learning methods and can apply them in completing a case study and building web applications with Flask tools

Machine Learning Defence Mechanism for Securing the Cloud Environment

A computer paradigm known as ”cloud computing” offers end users on-demand, scalable, and measurable services. Today’s businesses rely heavily on computer technology for a variety of reasons, including cost savings, infrastructure, development platforms, data processing, data analytics, etc. The end users can access the cloud service providers’ (CSP) services from any location at any time using a web application. The protection of the cloud infrastructure is of the highest  significance, and several studies using a variety of technologies have been conducted to develop more effective defenses against cloud threats. In recent years, machine learning technology has shown to be more effective in securing the cloud environment. In recent years, machine learning technology has shown to be more effective in securing the cloud environment. To create models that can automate the process of identifying cloud threats with better accuracy than any other technology, machine learning algorithms are  trained  on  a  variety  of  real-world  datasets. In this study, various recent research publications that used machine learning as a defense mechanism against cloud threats are reviewed

Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

Comparison of Autoscaling Frameworks for Containerised Machine-Learning-Applications in a Local and Cloud Environment

When deploying machine learning (ML) applications, the automated allocation of computing resources-commonly referred to as autoscaling-is crucial for maintaining a consistent inference time under fluctuating workloads. The objective is to maximize the Quality of Service metrics, emphasizing performance and availability, while minimizing resource costs. In this paper, we compare scalable deployment techniques across three levels of scaling: at the application level (TorchServe, RayServe) and the container level (K3s) in a local environment (production server), as well as at the container and machine levels in a cloud environment (Amazon Web Services Elastic Container Service and Elastic Kubernetes Service). The comparison is conducted through the study of mean and standard deviation of inference time in a multi-client scenario, along with upscaling response times. Based on this analysis, we propose a deployment strategy for both local and cloud-based environments.Comment: 6 pages, 3 figure

Video advertisement mining for predicting revenue using random forest

Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use

Comparative analysis of real-time fall detection using fuzzy logic web services and machine learning

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively

Web Content Extraction Techniques: A survey

As technology grows everyday and the amount of research done in various fields rises exponentially the amount of this information being published on the World Wide Web rises in a similar fashion. Along with the rise in useful information being published on the world wide web the amount of excess irrelevant information termed as ‘noise’ is also published in the form of (advertisement, links, scrollers, etc.). Thus now-a-days systems are being developed for data pre-processing and cleaning for real-time applications. Also these systems help other analyzing systems such as social network mining, web mining, data mining, etc to analyze the data in real time or even special tasks such as false advertisement detection, demand forecasting, and comment extraction on product and service reviews. For web content extraction task, researchers have proposed many different methods, such as wrapper-based method, DOM tree rule-based method, machine learning-based method and so on. This paper presents a comparative study of 4 recently proposed methods for web content extraction. These methods have used the traditional DOM tree rule-based method as the base and worked on using other tools to express better results

Comparative analysis between different automatic learning environments for sentiment analysis

Sentiment Analysis is a branch of Natural Language Processing in which an emotion is identified through a sentence, phrase or written expression on the Internet, allowing the monitoring of opinions on different topics discussed on the Web. The study discussed in this paper analyzed phrases or sentences written in Spanish and English expressing opinions about the service of Restaurants and opinions written in the English language about Laptops. Experiments were carried out using 3 automatic classifiers: Support Vector Machine (SVM), Naïve Bayes and Multinomial Naïve Bayes, each one being tested with the three data sets in the Weka automatic learning software and in Python, in order to make a comparison of results between these two tool