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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches

An Evaluation and Comparison for Phishing Attack Detection u...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Ajeet Kumar Sharma Anushree Nitin Rakesh Pawan Kumar Verma Dept. of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Dept. of Computer Engineering & Applications GLA University Mathura Uttar Pradesh India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications.

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A Smart Surveillance System for Pedestrian Tracking and Counting using Template Matching 4

A Smart Surveillance System for Pedestrian Tracking and Coun...

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4th International Conference on Robotics and Automation in Industry, ICRAI 2021

作者： Shahzad, Ali Raza Jalal, Ahmad Air University Dept. of Creative Technology Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665423434

Human Detection and tracking have become a focal area of research as it plays a major role in computer vision applications. Methods and equipment for the detection and tracking of humans are constantly being changed as advanced methodologies are being discovered. Even though this is a huge topic in the computer vision field, pedestrian detection in infrared images is relatively new. In this Research paper, we designed a new methodology for pedestrian detection, tracking, and counting in infrared images. The main objective of our research is to give machines a technique for the detection of pedestrians in real-time. Our proposed method of human detection detects and tracks pedestrians effectively and efficiently using the OCTVBS benchmark Pedestrian Infrared/visible Stereo Video Dataset. First, we used Template matching for Pedestrian detection. After detections tracking is done using Kalman filter. Lastly, headcount is acquired from head detections gained from applying Haar-like features. We evaluated our method on the OCTVBS dataset and gained detection accuracy of 91.04%, tracking accuracy of 92.47%, and count accuracy of 82.11%. The presented method should be useful for the monitoring of pedestrians in IR video surveillance. © 2021 IEEE.

关键词： Template matching

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U-Net Based Image Segmentation Techniques for Development of Non-Biocidal Fouling-Resistant Ultra-Thin Two-dimensional (2D) Coatings

U-Net Based Image Segmentation Techniques for Development of...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gurung, Bichar Dip Shrestha Devadig, Ramesh Do, Tuyen Gadhamshetty, Venkataramana Gnimpieba, Etienne Z. University of South Dakota Dept. of Computer Science Vermillion United States South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid City United States University of South Dakota Dept. of Biomedical Engineering Vermillion United States

ISBN: (纸本)9781665468190

Bacterial adhesion to the metallic surfaces creates a complex biofilm network, resulting in many problems like corrosion and fouling. Precise quantitative analysis of the surface coverage of cells can be vital in decoding biofilm-related issues. We present a deep learning-based approach to automate the microbes' segmentation from the Scanning Electron Microscope (SEM) images of biofilm developed on coated multilayer graphene nickel samples. We collected SEM images from multilayer graphene nickel exposed to Oleidesulfovibrioalaskensis (OA-G20) for 30 days. Then we manually annotated the microbes with the help of subject expertise and trained a deep-learning U-Net architecture. In order to deal with larger image sizes, we perform patched-based image training and techniques for predicting segmentation masks over the larger image. Intersection over Union (IOU) was calculated for the evaluation of the performance of the model. After training the image for multiple epochs and extracting the optimal model parameters from the learning curve, we were able to get 70.64% mean IOU score. The patched-based technique for image training and image inference showed promising output during the segmentation. © 2022 IEEE.

关键词： Biofilms

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A novel charging approach to Temperature and State of Charge management in BEV

A novel charging approach to Temperature and State of Charge...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Filippo Gemma Giulia Tresca Salvatore Riccardo Di Salvo Andrea Formentini Pericle Zanchetta Electrical Computer and Biomedical Engineering Dept. University of Pavia IT Electrical Electronic Telecommunications Engineering and Naval Architecture Dept. University of Genova IT Electrical and Electronic Engineering Dept. University of Nottingham UK

This paper proposes a novel approach to simultaneously manage the State of Charge and Temperature balancing during the charging process of the battery cells installed within a Reconfigurable Cascaded Multilevel Converter, used in the powertrain of Battery Electric Vehicles. Depending on the charging process scenario, the algorithm prioritizes temperature or SOC balancing, respectively, still ensuring low charging times and adherence to safety temperature limits. The method is here validated through simulations, showing a final balanced dynamic for both temperature and SOC without compromising the overall efficiency of the charging process.

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Enhanced Ant Colony Optimization with Dynamic Evaporation Rate (EACO-DE): An Intelligent Congestion Control System in IoV Environment

Enhanced Ant Colony Optimization with Dynamic Evaporation Ra...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： Sudarsan Sathishkumar Amrishkanna R B. Nithya Dept. of Computer Science and Engg. Saranathan College of Engineering Trichy Tamil Nadu India Dept. of Computer Science and Engg. National Institute of Technology Trichy Tamil Nadu India

Internet of Vehicle (IoV) is a combined form of Internet of Things (IoT) and Vehicular Ad-hoc Network (VANET). Its fundamental goal is to increase the service quality of Intelligent Transportation System (ITS). The dynamic nature of IoV such as high dynamic topology, high mobility of vehicles, etc. are some of the factors that causes more congestion thus reducing the efficiency of routing. Many algorithms were designed to obtain the shortest path from the source to the destination. Choosing of Ant Colony Optimization (ACO) algorithm is one of the best ways to obtain the shortest path. In this paper, an Enhanced Ant Colony Optimization with Dynamic Evaporation rate (EACODE) algorithm is proposed to obtain the congestion-free optimized path which reduces the travel time, travel cost and traffic problems. Instead of fixed evaporation rate, it dynamic predicts the pheromone evaporation rate with the help of run time metrics to avoid the congested paths thus improving the efficiency of the travel. The simulation results show that the proposed EACO-DE algorithm improves the efficiency of the routing compared with primitive Ant Colony Optimization (ACO) algorithm and Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) under various performance metrics.

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Analysis of Perturb and Observe MPPT Under Varying PV Operating Climatic Conditions

Analysis of Perturb and Observe MPPT Under Varying PV Operat...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Abdirahim Addawe Musaria K. Mahmood Sulaiman M. Karim Ahmad M. Nezam dept. of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Turkiye dept. of Medical Instr. Tech. Eng. Jannat Iraq College Ramadi Iraq dept. of Computer Technology Engineering Al-Hadba'a University College Mosul Iraq dept. of Computer Engineering Karabuk Üniversitesi Karabuk Turkey

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Improving the efficiency of photovoltaic (PV) sys-tems has emerged as a critical priority. Achieving optimal power production from solar modules requires operating at maximum power point (MPP) under various climatic conditions. This paper describes the operation of the Perturb and Observe (P&O) as an efficient Maximum Power-Point Tracking (MPPT) algorithm. Simulations of various operating scenarios are performed using MATLAB to assess the algorithm efficiency under both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). This study considers factors such as output power efficiency, time response, and steady-state power oscillation. The simulation results show a fast-tracking, lower oscillation in the case of STC but fail to track the MPP in the case of multi-panel under PSC.

关键词： Photovoltaic systems Solar irradiance Simulation Production Software Steady-state Time factors

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Enhancing Explainability and Transparency in Deep Learning Models for Healthcare Applications

Enhancing Explainability and Transparency in Deep Learning M...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： V. Indhumathi Veeramalai Sankaradass V. K Manindra Manish K. Jagadeesh Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India Dept. of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recently, it has been observed that deep learning models can be very useful in handling healthcare facilities mostly medical diagnosis and management. However, they are not interpretable and transparent enough to be accepted and used in the clinical environments where the matching reasoning is important. This paper proposes a new strategy to improve explainability of deep learning approaches when used in healthcare data set. We introduce an IDNN design with explainable layers and postdoc approaches like SHAP and LIME for visualization and explanation of model predictions. Our framework is applied to multiple healthcare datasets such as MIMICIII and ChestXray14 where we will compare the traditional method with the proposed method by training accuracy and explainability evaluation metrics. Results indicate that this causes minimal compromise in accuracy on the other hand, other explainability measures of model fidelity, and comprehensibility reveal significant gains, signifying the potential of this method in advancing the use of AI solutions in healthcare contexts.

关键词： Deep learning Training Hands Accuracy Explainable AI Statistical analysis MIMICs Medical services Predictive models Medical diagnosis

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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Electric And Power Engineering (EPEi), IEEE International Conference And Exposition On

作者： Lorena Saitta Emanuela Cutuli Giovanni Celano G Claudio Tosto Francesca Guarino Gianluca Cicala Maide Bucolo dept.of Civil Engineering and Architecture University of Catania Catania Italy dept. of Electrical Electronic and Computer Science Engineering University of Catania Catania Italy dept. of Biomedical and Biotechnological Science University of Catania Catania Italy

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach.

关键词： Performance evaluation Fluids Biomedical optical imaging Voltage measurement Cells (biology) Three-dimensional printing Stability analysis Real-time systems Biomedical monitoring Monitoring

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Sentiment Analysis of Reddit Posts Using the BERT Model in Peer-to-Peer Networks

Sentiment Analysis of Reddit Posts Using the BERT Model in P...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Prasant Kumar Dash Tithi Singh Rabi Shankar Panda Ritika Budhiraja Chinmoyee Das Rajshree Swain Dept. of Computer Science and Engineering C.V Raman Global University Bhubaneshwar Odisha Dept. of Computer Science and Engineering Dr.Akhilesh Das Gupta Institute of Professional Studies New Delhi Dept. of Cognitive & Brain Sciences Indian Institute of Technology Gandhinagar Gandhinagar Gujarat

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Sentiment analysis, a key area in Natural Language Processing (NLP), involves categorizing text data based on its emotional tone-positive, negative, or neutral. With the growing reliance on online interactions, understanding sentiments expressed in text is vital for assessing user opinions, behaviours, and engagement. In peer-to-peer (P2P) networks, where content sharing and decentralized user interaction dominate, sentiment analysis can uncover critical insights into digital relationships and collaborative tendencies. This paper explores sentiment analysis within P2P platforms using the BERT (Bidirectional Encoder Representations from Transformers) algorithm, a state-of-the-art NLP model. Unlike traditional methods, BERT effectively captures contextual and nuanced sentiments, enabling more accurate classification. The methodology includes preprocessing data, extracting embeddings using BERT, and employing fine-tuned models for sentiment categorization. Dimensionality reduction and visualization techniques further reveal patterns, sentiment clusters, and alignment between emotional tones in user interactions. Results demonstrate that BERT-powered sentiment analysis identifies content trends, emotional polarities, and behavioural dynamics in decentralized environments. The research also addresses challenges such as handling diverse content and biases in sentiment interpretation. This study highlights the growing need for advanced sentiment analysis techniques to enhance content profiling, trend forecasting, and user understanding on decentralized platforms, offering valuable implications for businesses and researchers.

关键词： Dimensionality reduction Sentiment analysis Analytical models Accuracy Bidirectional control Market research Encoding Peer-to-peer computing Forecasting Business

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Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review

Importance of Machine Learning and Deep Learning Algorithms ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Engineering and Natural Sciences Maltepe University İstanbul Turkey Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Chattanooga Chattanooga TN US

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors.

关键词： Deep learning Machine learning algorithms Earthquakes Radon Neural networks Estimation Prediction methods

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