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A weighted ensemble model for phishing website detection using random forest and deep neural network 5

A weighted ensemble model for phishing website detection usi...

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5th international conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Khan, Fabiha Hasan, Mehedi Das, Krishna International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Today, the number of people using the Internet to buy and sell goods and services is rapidly increasing. Website- based applications have become more popular in many business sectors because of their low operating costs and platform independence. User's private information may be stolen as a result of this growth and used maliciously. One method for stealing all of a user's information is phishing, which can send consumers to websites with dangerous content. It is a significant cybersecurity threat that can cause severe financial losses and reputational damage. To avoid falling victim to a phishing attack, it is essential to verify the URL before entering any sensitive information and to use anti-phishing tools and software to detect and prevent phishing attacks. this paper describes an approach to detect phishing websites by developing a weighted ensemble model using random forest (RF) and deep neural networks (DNN). By combining the predictions of these two models, we are achieving higher accuracy than any individual model. With 99% accuracy, the suggested model performs better than the individual models, while the random forest model and the DNN model alone achieve 98% and 73% accuracy, respectively. DNN and RF together can potentially capture different aspects of the data and lead to better predictions. DNNs are highly effective at detecting complex patterns in large datasets, while RF can identify the most important features for classification and handle noisy and missing data. By combining these algorithms, it is possible to leverage their strengths and improve overall accuracy and robustness of detection. © 2023 IEEE.

关键词： Cybersecurity

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Automatic closing of sketch boundaries - Opening doors to coloring fun 5

Automatic closing of sketch boundaries - Opening doors to co...

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5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Uppalapati, Padma Jyothi Dayal, Abhinav Yamala, Akhil Palivela, Siva Kumar Vishnu Institute of Technology CSE Department Bhimavaram AP India NCR Hyderabad Telangana India TCS Hyderabad Telangana India

ISBN: (纸本)9781665412728

Coloring a rough sketch gives visualization to our ideas and thoughts. Modern drawing tools that use pen and touch based devices allow for dynamic content creation. Filling colors in a hand drawn sketch requires the sketch boundary to be completely closed, else the fill algorithms leak the color outside the boundary. While sketching may be fun, paying attention to minor gaps requires precision and can be stressful. this paper proposes an efficient algorithm to automatically fill the gaps so as to make the artist focus on the art. the unsupervised learning and clustering technique is applied to identify the disconnected clusters of sketch strokes followed by backtracking algorithm to find the end points to be connected by a closest point search algorithm. Results show an accurate and fast performance for simple sketches. © 2021 IEEE.

关键词： Unsupervised learning

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5th international Scientific conference on Environmental Challenges in Civil Engineering, ECCE 2022

5th International Scientific Conference on Environmental Cha...

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5th international Scientific conference on Environmental Challenges in Civil Engineering, ECCE 2022

ISBN: (纸本)9783031268786

the proceedings contain 24 papers. the special focus in this conference is on Environmental Challenges in Civil Engineering. the topics include: Wind-Based Form-Finding of a Tensegrity Pavilion;energy-Efficient Reinforced Heating System Implemented as a Carbon Concrete Formwork;originality and Safety as a Priority in the Revitalization of Immovable Monuments?;shaping the Course of Costs Curves Generated in Diversified Investment Sectors;economic Conditions of Leaving the Construction Site by the Contractor at Different Stages of its Implementation;sustainable Building: the Role of the Soil Parameters on Earthquake Safety;the Role of DSSI on the Seismic Risk Assessment of a Building;response of Geodesic Domes on the Seismic Excitation with Time History Analysis;algorithms for the Near-Real Time Identification and Classification of Landslide Events Detected by Automatic Monitoring Tools;analysis of Dynamic Compaction Effects – Tavira Case Study;stress State and Several Problems of Estimating the Actual Operation of Effective Bridge Superstructures;durability and Assessment of Early Post-tensioned Bridges;preface;seismic Risk and Environmentally Friendly Solutions: the Geotechnical Point of View;impact of Duration and Intensity of Seismic Records on the Soil-Steel Bridge Behavior;impact of Reinforcement Layer in Soil-Steel Culvert on Laboratory and Numerical Tests;comparative Analysis of the Use of Different Concrete Models in the Evaluation of the Propagation of Damage in the Reinforced Concrete Sample;diagnostic and Design of Reconstruction of Building Váhostav;rheological Properties of Concrete Based on Waste Materials;effect of the Addition of Waste Fibers on Some Properties of Concrete.

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5th international and 20th National conference on Machines and Mechanisms, iNaCoMM 2021

5th International and 20th National Conference on Machines a...

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5th international and 20th National conference on Machines and Mechanisms, iNaCoMM 2021

ISBN: (纸本)9789811937156

the proceedings contain 60 papers. the special focus in this conference is on Machines and Mechanisms. the topics include: Design and Analysis of a Miniaturized Atomic Force Microscope Scan Head;validation of a Steering System Mathematical Model via Test Rig Measurements;Dynamic Modelling a 6-DOF Compliant Flexure-Based Stewart Micromanipulator;analysis of Connected Shallow Arches Under a Load from a Moving Rigid Wedge;Dynamic Analysis of MCF-7 Using Tensegrity Model;Dynamic Modeling of Planar Multi-section Tendon-Driven Continuum Manipulator (TDCM) Using "Euler–Lagrange Formulation";dynamic Characteristics of Human Hand-Arm System—Analytical and Simulation Approaches;vibration Isolation Characteristics of a Modified Gough-Stewart Platform with the Top Platform Filled with Damping Particles;kinematic Synthesis and Design of a Five Fingered Hand Exoskeleton;nonlinear Distribution of the Gearbox Dynamic Model Including Tooth Cracks;classification of Worm Gearbox Fault Using Dendrogram Support Vector Machine;mechanisms Responsible for Performance Improvements of Pocketed and Textured Lubricated Interfaces;feature Selection Based on Gaussian Ant Lion Optimizer for Fault Identification in Centrifugal Pump;vibration Monitoring of Defective Shaft Bearing System;formulation of Approximate Generalized Generated Database Model for Low-Speed Gear Box Developed Based on Double-Crank Inversion for a Four-Bar Chain;effect of Balanced Sliding Velocity and Slide-to-Roll Ratio on Surface Wear in Symmetric Profile Modified High Contact Ratio Spur Gears;design of a Novel Mechanism for Actuation of a Bistable Buckled Beam;modelling and Simulation of Car Suspension with Linear and Nonlinear Spring;mechatronics and algorithms to Analyse and Control the Laser Beam Spot Size for 3D Micro-printing Machine;preface.

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Design of an English Translation Intelligent Proofreading System Based on Bidirectional Long Short-term Memory Algorithm 5

Design of an English Translation Intelligent Proofreading Sy...

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5th Asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Liang, Nuochun Heilongiang University of Technology School of International Education Jixi China

ISBN: (纸本)9798350374407

the quality and accuracy of translation play a crucial role in everyday English communication. To enhance translation quality, a specialized smart proofreading system is developed for commonly used English language variations in this paper. Using Amazon Relational Database Service (RDS), the proposed system designs database structures such as statement relationship tables and translation judgment tables. the application interface is programmed in Python, incorporating various view classes like homepage, editing, and display to create the user interface. Leveraging the IaaS cloud platform of Open Nebula and utilizing bidirectional Long Short-Term Memory (LSTM) algorithms, the server-side encoding module is formulated. An information fusion module for word vectors is established, enabling the creation of an intelligent proofreading module through the construction of word and semantic proofreading units. Finally, the simulation study in black-box testing validates the system's functionalities, the research of this paper shows a substantial improvement in translation quality and notable proofreading advantages. © 2024 IEEE.

关键词： Black-box testing

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Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction 5th

Comparative Analysis of ARIMA Time Series Model and Other Te...

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Amulya, P. Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisharh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9789819997060

Accurate performance prediction of cloud workloads is essential for optimizing resource allocation, meeting service level agreements (SLAs), and ensuring efficient cloud service delivery. In this research paper, we conduct a comparative analysis of the ARIMA (Auto Regressive and Integrated Moving Average) time series model with other popular techniques for cloud workloads performance prediction. We evaluate the performance of ARIMA in comparison with other models, including machine learning algorithms and statistical methods, using real-world cloud workload performance data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Proceedings - CSCTIT 2022: 5th College of Science international conference on Recent Trends in Information Technology

Proceedings - CSCTIT 2022: 5th College of Science Internatio...

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5th College of Science international conference on Recent Trends in Information Technology, CSCTIT 2022

ISBN: (纸本)9798350334920

the proceedings contain 48 papers. the topics discussed include: deep learning classification based on merge ear and single finger geometry dataset;the optimize homotopy analysis method using a genetic algorithm for solving system of linear Volterra integral equations;two methods for underwater images color correction: gamma correction and image sharpening algorithms;a survey on fruit fly optimization algorithm (FOA) in robust secure color image watermarking;approximation algorithm applications in artificial intelligence: a review;solving the model of two dimensional RDB system by modified vibrational iteration method with genetic algorithm;a printed monopole antenna array inspired by historical cities in Iraq;improving low resources Arabic speech recognition using data augmentation;and hyper parameter optimization of deep learning models for fall recognition using tree-structured Parzen estimator.

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Effect of Euler’s Number on Signature Verification System 5th

Effect of Euler’s Number on Signature Verification System

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5th international conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Mukhopadhyay, Maumita Chakraborty, Sayan Department of CSE Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of CSE Techno International New Town West Bengal Kolkata India

ISBN: (纸本)9789819719228

Recently, renewed interest has grown in handwritten signature verification domain. In order to make personal verification, handwritten signature verification is the most widely used method. Such automated signature verification frameworks have wide range of applications. this work presents an innovative approach for classification between real and forged signatures using machine learning classification models. Two individual image datasets of real and forge signatures are used in this current study. Six features (mean, standard deviation, skewness, kurtosis, entropy and Euler’s Number) of the two datasets were calculated. the current work used classification algorithms such as K-Nearest Neighbors (KNN), Logistic Regression, Random Forest (RF) Classifier. the study was done to investigate the effect of Euler number in signature verification system, hence the framework recorded results with and without Euler’s Number. the classification report was further analyzed to find out the superior framework out of the three frameworks chosen and also to identify the importance of Euler’s name in signature verification system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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2022 5th international conference on Computing and Big Data, ICCBD 2022

2022 5th International Conference on Computing and Big Data,...

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5th international conference on Computing and Big Data, ICCBD 2022

ISBN: (纸本)9781665457163

the proceedings contain 35 papers. the topics discussed include: research of data-driven iterative optimization scheme for machine learning models;unified analysis of algorithms for dictionary learning;Bayesian network for base station energy consumption data completion;rapid processing technology of massive data based on XMA new collector in flight test;a novel method for product quality control based on production IoT data;digital twin for air conditioning systems based on electric vehicle operating data;data streams management: multidimensional summary with big data tools;ensemble summarization models to leverage performance;empirical study on equity incentive of core employees, enterprise innovation and financial performance-based on big data of gem listed companies;numerical simulation on drag reduction of micro-grooved surface;and hybrid neuro-particle swarm optimization model for predicting depression in asphalt pavements.

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Innovative Tunes: Utilizing Machine Learning for Predicting Spotify Music Popularity Based on Audio Features 5th

Innovative Tunes: Utilizing Machine Learning for Predicting ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Joshi, Shubham Gupta, Neha Mahajan, Rupali School of CSE Symbiosis International University Pune India School of CSIT Symbiosis University of Applied Sciences Indore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9783031770746

the digitalization of music has brought significant changes to popular music in the age of streaming media. As the number of users of a platform like Spotify continues to grow, it has become one of the most important online music providers today.. People rely on the Spotify app to listen to their favorite artists and discover new music. this study aims to investigate the relationship between music data, specifically audio features (such as key and tempo) extracted from the Spotify database, and popular music (measured by the number of streams a song receives on the Spotify platform). the main goal is to create an accurate and reliable model that can accurately predict whether a song will become a hit or not. To achieve this goal, researchers looked at four different machine learning algorithms: linear regression, random forest classifiers, and K-means clustering. these algorithms are used to analyze data and find patterns that help predict popular songs accurately. Finally, the research proposed a predictive model that uses machine learning techniques to determine the importance of music. Using these standards, songs can be classified according to their needs, thus providing a better perspective on artists, music producers, and the music industry as a whole. It is hoped that this research will help to better understand the factors that influence the success of the song and pave the way for greater decision-making in music. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Decision making

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