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Enhancing Data engineering and Accelerating learning through Intelligent Automation 2

Enhancing Data Engineering and Accelerating Learning through...

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2nd international conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2023

作者： Pradeep, Aneesh Rustamov, Azamat Shokirov, Xudoyshukur Ibragimovna, Guli Tirova Farkhadovna, Sabirova Umida Medetovna, Akhmedova Feruza New Uzbekistan University Software Engineering Tashkent Uzbekistan New Uzbekistan University Mechanical Engineering Tashkent Uzbekistan Bukhara State University Uzbek Linguistics and Journlism Uzbekistan University Named after Mirzo Ulugbek Department of Sociology Tashkent Uzbekistan Uzbekistan Named after Mirzo Ulugbek Department of Sociology Tashkent Uzbekistan

ISBN: (纸本)9798350339949

The rapid growth of data and the increasing complexity of analytical tasks have necessitated the development of efficient data engineering processes and accelerated learning techniques. Intelligent data engineering and automated learning are two important fields in the domain of artificial intelligence. The aim of intelligent data engineering is to develop efficient and effective algorithms for processing large volumes of data. Automated learning, on the other hand, focuses on creating algorithms that can learn from data without being explicitly programmed. Both fields are closely related and have significantly contributed to the development of various applications such as machine learning, natural language processing, computer vision, and robotics. Through a comprehensive review of data engineering concepts, challenges, and automated learning techniques, this paper highlights the benefits and advantages of incorporating intelligent automation into data engineering workflows. It presents case studies and real-world examples showcasing successful implementations of intelligent automation in data engineering projects. In addition, the paper discusses performance evaluation metrics, challenges, ethical considerations, and potential intelligent automation risks. In addition, it discusses future research and development opportunities in the disciplines of intelligent data engineering and automated learning. The findings of this study shed light on the potential of intelligent automation in augmenting data engineering processes and accelerating learning, thereby revealing new avenues for efficient data utilization and knowledge extraction across a variety of domains. © 2023 IEEE.

关键词： Automation

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Democratizing Quality-Based machine learning Development through Extended Feature Models 1

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26th international conference on Fundamental Approaches to software engineering (FASE) Held as Part of the European Joint conferences on Theory and Practice of software (ETAPS)

作者： dAloisio, Giordano Di Marco, Antinisca Stilo, Giovanni Univ Aquila Laquila Italy

ISBN: (数字)9783031308260

ISBN: (纸本)9783031308253;9783031308260

ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to satisfy the stake-holders' needs requires an appropriate amount of knowledge about the ML domain. Over the years, several solutions have been proposed to automate the development of ML systems. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, and others) is still missing. In this paper, we propose a new engineering approach for the quality-based development of ML systems by realizing a workflow formalized as a software Product Line through Extended Feature Models to generate an ML System satisfying the required quality constraints. The proposed approach leverages an experimental environment that applies all the settings to enhance a given Quality Attribute, and selects the best one. The experimental environment is general and can be used for future quality methods' evaluations. Finally, we demonstrate the usefulness of our approach in the context of multi-class classification problem and fairness quality attribute.

关键词： machine learning System software Quality Feature Models software Product Line Low-code development

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An Ensemble machine learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Patch Antenna Design Using machine learning: ANN and SVR 7

Patch Antenna Design Using Machine Learning: ANN and SVR

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7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Patil, Aditi R Raju, Chinmay M Harish, Shreya Shanthi, P. Rv College of Engineering® Department of Ete Bangalore India

ISBN: (纸本)9798350343144

Every advancement in technology aims to address challenges within a specific field. One such recognized issue pertains to the laborious process of antenna design utilizing CAD tools. The proposed solution offers an effective means to predict antenna output parameters, highlighting the converse-inefficiencies arising from suboptimal design and optimization procedures. Additionally, proficiency in using CAD tools becomes a prerequisite, demanding both skill acquisition and fluency. Thus, an imperative arises for an improved and streamlined antenna design process. Different methodologies exist for ascertaining antenna radiation parameters. In the provided approach, machine learning models playa pivotal role in establishing connections between radiation and input design parameters. Validation is achieved by comparing the outcomes with HFSS software simulation results. © 2023 IEEE.

关键词： Microstrip antennas

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Detecting Distributed DoS Attacks on SDN using machine learning (ML) Methods

Detecting Distributed DoS Attacks on SDN using Machine Learn...

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2023 IEEE international conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kurakula, Arun Kumar Akhila, K. Bhavya, Mummalla Sai, Meesala Venkata Cse Tirupati India Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350397208

The DDoS attack is a well-known malicious attack that aims to interrupt traffic of the server. A DDoS attack involves the coordination of multiple systems to target a specific host simultaneously. SDN is a software Defined Network which is a type of network layout that permits the virtual design, and creation of hardware components and also has the ability to dynamically change the network settings. Even though software Defined Network (SDN) is an efficient approach, it remains vulnerable to DDoS attacks. This article proposes a novel system to detect whether the network is safe or attacked. The proposed system also lessens the limitations imposed by conventional and other existing techniques. Decision Tree (DT), one of the various types of ML algorithms, is used here to identify the potentially malicious traffic. © 2023 IEEE.

关键词： Decision trees

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WhatsApp Chatbot for Real-Time Stock Updates and Analytics 14

WhatsApp Chatbot for Real-Time Stock Updates and Analytics

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14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Rao, Tanmay Vairagade, Rupali Muppidwar, Shantanu Sherki, Shreyash Barokar, Jagdish G. H. Raisoni College of Engineering Department of Information Technology Nagpur India

This research project focuses on the development of a WhatsApp bot for stock updates using Flask, Python, and Alpha Vantage API. The aim of the project is to provide a platform for users to easily access and receive up-to-date information on their preferred stocks through a user-friendly and convenient WhatsApp interface. The project involves the integration of Flask, a Python web framework, with the Alpha Vantage API, which provides real-time stock data. The bot is designed to provide users with stock prices, trends, and alerts, as well as personalized notifications based on their stock portfolio. The implementation of this project demonstrates the potential of using machine learning and AI-powered chatbots to provide efficient and personalized solutions to financial information and investment needs. The project also highlights the importance of using open-source software and APIs to reduce costs and improve accessibility to advanced technologies. Overall, this research project contributes to the growing body of knowledge on the use of AI and machine learning in finance and demonstrates the potential for their application in other fields. © Grenze Scientific Society, 2023.

关键词： Python

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Application of Internet of Things and Cognitive Design Protocol for Clothing Pattern Design

Application of Internet of Things and Cognitive Design Proto...

引用

2023 Annual international conference on Emerging Research Areas: international conference on Intelligent Systems, AICERA/ICIS 2023

作者： Zearah, Sajad Ali Abdul Kadeem, Sahar R. Taha, Ahmed Mahdi Hussin, Marwa Saad Majeed, Maryam Ghassan Al-Tameemi, Ahmed Raed Scientific Research Center Al-ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Designing software is used regularly in most fabric firms to create prototypes of new garments. The study suggests a deep-learning-based IoT-based computational modeling protocol for fashion pattern creation. For this new protocol, the computerized design algorithm relies on a Deep learning-based Imaging Model (DLIM). Employing only 2D forms, the designer can create any pattern they like. To help choose the right hue for projects, DLIM provides palettes of various hues. DLIM interprets the concept's pattern, colors, and quantity for automated clothing production and generates a code that the machine can read. The designs have been uploaded at various locations on the IoT network, and this information is compiled into a database for later recognition by DLIM. The designer can go beyond their creativity with the help of DLIM and a computational development protocol. Simulations of this technology have shown that it reduces the mean square error rate of the IoT platform while still providing a straightforward interface and pleasant experience for customers. Therefore, the computational designing protocol for IoT platforms may deliver maximum performance (98.93%) with minimal mistakes. © 2023 IEEE.

关键词： Deep learning

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Design of a hybrid model of finite element method and machine learning to predict mode I-II crack expansions 3

Design of a hybrid model of finite element method and machin...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Yao, Jianchun Li, Xiaoqi Xiang, Jiawei College of Mechanical and Electrical Engineering Wenzhou University Wenzhou325035 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Compound mode I-II crack expansion is a common fracture source for the failures of mechanical components in real-world running structures. Therefore, prediction of crack extensions of mode I-II loading is a long-term research hotspot. software FRANC3D is widely used to simulate the growth of fatigue cracks with high precision for engineering applications. However, the high computational cost for the usage of FRANC3D are obviously. Data-driven machine learning model is another strategy to predict crack expansion with low accuracy for the lack of training samples in real-world running structures. In order to fast and accuracy predict compound mode I-II crack expansion, a hybrid model of finite element method (FEM) and machine learning (ML) is developed by interchangeably using FEM and ML. Two cases are given to validate the performance of the present hybrid model by using FEM and support vector regression (SVR) and generalized regression neural network (GRNN), respectively to predict compound mode I-II crack expansions in a stress plate. Finally, to verify the high precision and efficiency of the hybrid model compared with the results of simulation and other models. © 2023 SPIE.

关键词： Cracks

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Intelligent Advertising System Incorporating User Feedback: Design and Implementation using OpenCV and Python 3

Intelligent Advertising System Incorporating User Feedback: ...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Pradeep, Aneesh New Uzbekistan University Software Engineering Tashkent Uzbekistan

ISBN: (纸本)9798350338607

Conventional advertisement displays have always been a one-way communication channel, as they do not provide an avenue for users to give feedback. This is a primary barrier since firms must tailor their advertising strategies to appeal to their target population based on user response. In this essay, we suggest a clever advertising system that uses a camera to record customer comments. By utilizing computer vision and machine learning, the system may evaluate facial expressions and other visual clues to estimate the user's degree of involvement and interest. Using this data, the system may provide users with tailored advertisements, boosting conversion rates and enhancing the effectiveness of the advertising campaign. © 2023 IEEE.

关键词： Python

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Effective Feature Extraction Framework to Improve Network Intrusion Detection System 9

Effective Feature Extraction Framework to Improve Network In...

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9th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Mv, Hari Vinayak Jarin, T. Raja, Kiruba Thangam Jyothi Engineering College APJ Abdul Kalam Technological University Department of ECE Thrissur India Jyothi Engineering College APJ Abdul Kalam Technological University Department of EEE Thrissur India School of CSE and Information Systems Vellore Institute of Technology Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350319651

A network intrusion detection system (NIDS) is a crucial component of a robust cybersecurity strategy. Its primary purpose is to continuously monitor network traffic and detect suspicious or malicious activity that could be indicative of a cyberattack or unauthorized network access. The effectiveness of NIDS depends heavily on the techniques we use to boost the classification accuracy of intrusion and minimize the computational difficulty while performing training and testing. The high volume of network traffic combined with its large number of features will increase the classification time. With the recent emergence of deep learning techniques, scientists have shown interest in learning dataset features, followed by the classification of intrusions. This study offers a novel method for extracting high-dimensional features from input data by employing a stacked sparse autoencoder. Simple machine learning models are then built using the remaining low-dimensionality features. Simulations were conducted, and the efficacy of binary and multiclass classifications was verified. The proposed method exceeds most of the other existing approaches in terms of performance. © 2023 IEEE.

关键词： Feature extraction

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