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SSRN 2022年

作者： Jain, Suhani Choudhary, Naveen Jain, Kalpana MTech Student Computer Science Department College of Technology and Engineering MPUAT Udaipur India HOD of Computer Science Department College of Technology and Engineering MPUAT Udaipur India Computer Science Department College of Technology and Engineering MPUAT Udaipur India

Phishing is an online crime in which a criminal tries to persuade unsuspecting users to reveal sensitive (and valuable) personal information to the miscreant, such as usernames, passwords, financial account details, personal addresses, SSNs, and social contacts, for harmful purposes. Phishing is usually carried out by impersonating a reliable entity in Internet communication, which is accomplished through a combination of social engineering and technical trickery. Attackers regularly employ spoofing emails and deceptive websites to persuade users to provide personal information. Spoofing emails frequently pretend to be from legitimate companies and direct consumers to fake websites where they can enter important information. Phishing is one of the most common forms of online crime in today's world. Checking URLs against blacklists of known phishing websites, which are generally built based on manual verification, is a frequent countermeasure that is inefficient. As the Internet develops in size, automatic URL recognition becomes more necessary to offer end users with timely protection. This thesis explains how to use machine learning to detect dangerous phishing websites, with an emphasis on attributes retrieved just from the URL. It starts with a description of the available data and the feature engineering process, then moves on to choosing acceptable machine learning approaches. It compares algorithm performance and assesses the outcomes obtained. © 2022, The Authors. All rights reserved.

关键词： computer crime

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A Mixed-Integer Linear Programming Approach to Maintenance Budgeting in Electrical Distribution Networks Considering Repair Time Uncertainty

A Mixed-Integer Linear Programming Approach to Maintenance B...

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Future Energy Solutions (FES), International Conference on

作者： Hamed Mirsaeedi Alireza Hassankashi Mahsa Sajjadi Pierluigi Siano Graduated Student from University of Tehran Tehran Iran Graduated Student from Iran University of Science and Technology Tehran Iran Department of Electrical and Computer Engineering University of Tennessee Knoxville TN USA Department of Management & Innovation Systems University of Salerno Salerno Italy

As a component of asset management strategies, maintenance management is essential for improving the reliability of the Electrical Distribution Networks (EDNs). To increase system efficiency, electricity companies invest a significant effort to assign their financial sources to important system components. In this paper, a mixed-integer linear programming approach to maintenance budgeting considering restoration time uncertainty is suggested. The suggested technique finds the optimal budget for doing maintenance tasks in distribution network feeders. The objective is to reduce overall cost, which includes total interruption cost of customer $\left(\boldsymbol{T} \boldsymbol{C}_{\boldsymbol{C I C}}\right)$, total energy not supplied ${cost}\left(T C_{E N S}\right)$, total cost of materials and labor for repair $\left(T C_{R E P}\right)$, and total cost of maintenance $\left(T C_{P M}\right)$. Three cases are also taken into account to observe the effectiveness of the suggested maintenance method: Case I assumes that no maintenance actions are performed in the system, in Case II, maintenance budget for each distribution feeder is taken into consideration equally, in Case III, according to constraints, the suggested formulation allocates the optimal maintenance budget. The Roy Billinton test system’s bus number two (RBTS2) is subjected to the proposed reliability improvement approaches, and the results are discussed.

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Random Matrix Theory Based Radio Frequency Fingerprinting Identification of WiFi Signal

Random Matrix Theory Based Radio Frequency Fingerprinting Id...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： You Jiang Jian Xiong Junqi Wu Bo Liu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Student Innovation Center Shanghai Jiao Tong University Shanghai China School of Computer Science University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

This paper introduces a method of Radio Frequency Fingerprint (RFF) recognition based on Random Matrix Theory (RMT). RFF identification is a physical layer method to identify devices that exchanging messages in the network. The extracted subtle features of the transmitters working as the biometric fingerprint can uniquely represent the identity of the devices. However, the differences in RFF become extremely subtle when transmitters are the same type of industrial standard equipment and it is very difficult to identify the devices under this scenario. In the proposed method, a random matrix of the I/Q (In-phase/Quadrature) data of bit-similar Universal Software Radio Peripheral (USRP) devices is constructed firstly, and the extracted features are sent to the Convolutional Neural Networks (CNN) network for recognition. Experiments are carried out under Signal-to-Noise Ratio (SNR) $\in[-10,20] \mathrm{dB}$ between different datasets. It demonstrates a 0.98 accuracy in identification, outweighting the traditional classification method.

关键词： Radio frequency Wireless sensor networks Accuracy Radio transmitters Fingerprint recognition Feature extraction Object recognition

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Comparative Analysis of Traditional and Machine Learning Based Face Recognition Models

Comparative Analysis of Traditional and Machine Learning Bas...

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Data, Decision and Systems (ICDDS), IEEE International Conference on

作者： Shreya R Akanksha P Mulgund Shravani Hiremath Sri Harshitha A Anjan K Koundinya Department of Computer Science PES University Bengaluru India Department of Computer Science and Engineering PES University Bengaluru India Department of Information Science and Engineering IEEE Student Branch Counsellor BMS Institute of Technology and Management Bengaluru India

Face recognition technology uses methodologies to process images, register them, and compare and verify it with a set of faces it has already registered and labelled. Recent development in visual recognition showcases the immense power of utilising machine learning techniques like deep neural networks, convolutional neural networks to achieve face recognition. Based on a comprehensive literature review it was found that some of the algorithms that provide great performance metrics in a variety of conditions are MTCNN, FaceNet model, Haar cascade, LBPH, hence it was chosen to be the subject of comparison under various conditions in the paper. A machine learning model was constructed which included MTCNN for face detection and FaceNet for face *** Performance of KNN and SVM classifiers has been studied. The second model uses a combination of Haar cascade for face detection and LBPH for face recognition, a traditional face recognition technique. The results highlight that MTCNN, FaceNet and KNN perform exceptionally well. The performance of the other models are also discussed in the results and discussion section. The research not only guides researchers and practitioners in choosing suitable face recognition techniques but also offers ground for future improvements in accuracy and reliability in the field of algorithm developments.

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques

Real-Time Facial Recognition System for Secure College Bus T...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. N. Kavitha Saranya S S Pragatheeswari E N. Kaviya Priya S R. Ranjith N. Rahul Vanchivel Department of Computer Science and Design Kongu Engineering College Tamil Nadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India Department of CSD UG Student Kongu Engineering College Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability.

关键词： Support vector machines Deep learning Renewable energy sources Accuracy Face recognition Transportation Nearest neighbor methods Real-time systems Security Face detection

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Optimization Accuracy of CNN Model by Utilizing CLAHE Parameters in Image Classification Problems

Optimization Accuracy of CNN Model by Utilizing CLAHE Parame...

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Networking, Electrical Engineering, computer Science, and technology (IConNECT), International Conference on

作者： Anjar Wanto Yuhandri Yuhandri Okfalisa Okfalisa Doctoral Student Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Department of Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Department Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Indonesia

This study aims to optimize popular Convolutional Neural Network (CNN) models, such as ResNet50, InceptionV4, VGG19, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large, by utilizing the CLAHE (Contrast Limited Adaptive Histogram Equalization) parameter. This research focuses on the problem of classifying retinal fundus images, which have challenges in analyzing the structure and features of the picture. This study compared the performance of CNN models, which were optimized using the CLAHE method as image pre-processing. The fundus retinal image data used is a publicly available dataset. The optimization process is done by changing the parameters in the CNN model and applying the CLAHE technique to improve image quality before the classification process. The results showed that using the CLAHE parameter significantly contributed to improving the performance of the CNN model in classifying retinal fundus images. Some CNN models optimized with CLAHE produce better accuracy than the unoptimized models. There are also variations in performance between the different models, with some models providing better results in classifying retinal fundus images. This study provides new insights regarding using CLAHE parameters in optimizing the CNN model for retinal fundus image classification problems. The results of this research can be the basis for further development in medical image processing and pattern recognition for more accurate and practical diagnostic applications.

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Automated Aquaponic System 74

Automated Aquaponic System

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74th International Astronautical Congress, IAC 2023

作者： Gąbka, Mikolaj Klima, Bartlomiej Damian, Wojciech Dyląg, Lidia Gajewska, Marta Daniol, Mateusz Kolodziejczyk, Agata AstroBio Student Scientific Association AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland Space Technology Center AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland

Research presents a concept of a modular automated aquaponic system. The scope of the work included designing a modular, cost-effective aquaponic system for closed environments, enabling the cultivation of various types of plants in areas where soil-based growth is not feasible. The process began with the ideation phase, brainstorming, and conceptualization, during which the system requirements were established. During the development phase, the necessary environmental parameters that the system must provide for the cultivation of selected plant species were defined, and suitable containers for promoting proper root system growth and development were initiated. Simultaneously, an analysis of sensors available on the market was conducted, considering their capability to monitor environmental parameters directly affecting the growth and development of cultivated plants. Subsequently, CAD modelling was performed according to the initial requirements. Finally, a concept and automation design were developed. Currently, the project is in the prototyping phase, with an estimated completion date in the fourth quarter of 2023. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Aquaponics Automation Circular Economy IoT Space technology

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An Algorithmic determination for the secure dominance in a fuzzy network and its application in medical diagnosis

An Algorithmic determination for the secure dominance in a f...

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International Conference on Cognitive Computing and Information Processing

作者： S Sivasankar Syed Ibrahim Maaz Department of Mathematics RV Institute of Technology and Management Bangalore Bangalore India Computer Science Engineering Student RV Institute of Technology and Management Bangalore Bangalore India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456494

In communication networks, secure connectivity is critical. The failure of a dominant node may jeopardize the network's stability. As the network grows in size, it becomes increasingly difficult to maintain track of all of its dominant nodes and secure connections. As a result, large network analysis can be performed quickly and easily using a computer system that understands networks as matrices, and a fit algorithm will make calculating work simple and quick. In this paper, we present two algorithms for determining the dominance number and secure dominance number of a fuzzy network, with the objective of comparing the concepts of dominance and secure dominance in a fuzzy structure to diagnose the patient symptoms which guide them to appropriate medical treatment.

关键词： Medical treatment Network analyzers Information processing Stability analysis Medical diagnosis Communication networks Cognitive systems

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Feature Extraction, Essential Dimension Estimation and Dimension Reduction Method of Colony Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

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Wideband Circularly Polarized FOWLP Antenna in Package for Satellite Communications

Wideband Circularly Polarized FOWLP Antenna in Package for S...

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Electronic Components and technology Conference (ECTC)

作者： Kai Bo Zheng Mei Sun Yong Xin Guo IME attached student from Dept. of Electrical and Computer Engineering National University of Singapore (NUS) Singapore A*STAR (Agency for Science Technology and Research) Institute of Microelectronics (IME) Singapore Electrical and Computer Engineering Dept. National University of Singapore Singapore

This paper presents a wideband circularly-polarized Antenna-in-Package (AiP) element implemented in Fan-Out Wafer-Level-Packaging (FOWLP) technology for satellite communication. The AiP element features a meta-surface radiator with a single-ended aperture coupled feeding structure to achieve a wideband and circular polarization performance. By stacking a top mold of 500μm, a middle mold of 90μm and a 42μm RDL layer at the bottom, with a size of 10 mm×10 mm×0.652 mm, the AiP element achieves a Return Loss > 17.95 dB, an Axial Ratio < 2.67 dB, and a Gain of 7.19-7.88 dBi over 27–31 GHz. The antenna design concept is further confirmed using printed circuit board (PCB) technology for fast prototyping and evaluation.

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