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A Software Defect Avoidance Technique for E-commerce Website Development 6th

A Software Defect Avoidance Technique for E-commerce Website...

6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sudarshan, R. Srivatsa, S.K. Chaithra, K.N. Mohan Kumar, K.N. Department of Computer Science and Engineering VELS University Chennai600117 India Department of Electronics Engineering Anna University Chennai600044 India Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science and Engineering MS Ramaiah University of Applied Sciences Bengaluru560058 India

ISBN: (纸本)9789811613418

As per the literature study, the development of e-commerce websites is done by adopting different methodologies, which does not give due importance to the logical sequencing of the functionality of each module to be developed. This work proposes a technique that helps in producing defect-free e-commerce applications by developing a module dependency functionality matrix, mainly on ATG (Art Technology Group) framework, using rational unified process (RUP). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic commerce

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ghazal, Taher M. Ibrahim, Amer Akram, Ali Sheraz Qaisar, Zahid Hussain Munir, Sundus Islam, Shanza Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Skyline University College University City School of Information Technology Sharjah1797 United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NFC Institute of Engineering and Technology Department of Computer Science Multan Pakistan Lahore Garrison University Lahore Department of Computer Science Lahore Pakistan Lahore Garrion University Lahore Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc. © 2023 IEEE.

关键词： Support vector machines

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Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study 3

Multi-Criteria Decision-Making Approach for Laptop Selection...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Channi, Harpreet Kaur Sandhu, Ramandeep Faiz, Mohammad Islam, Sardar M. N. Chandigarh University Gharuan Department of Electrical Engineering Punjab Mohali India School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798350302288

This article uses the well-known multi-criteria decision-making (MCDM) theory to determine which laptop is the best among those that are currently on the market. To illustrate the importance of decision-making in a class of 25 students, data on laptop features was gathered for this case study. The following eight factors were considered while choosing an option: price, weight, RAM, camera megapixels, screen size, clock speed, operating system, and laptop appearance. SAW and WPM determine the optimal laptop alternative. To rank laptop qualities, identical weights are applied. The suggested work displays laptop rankings derived by SAW and WPM methods. Future engineers and management may benefit significantly from this straightforward decision-making process. More important attributes/criteria and alternatives may be taken into account, and there are a variety of weighting techniques from which to choose. The study emphasizes the importance of incorporating user preferences and real-time market data to ensure personalized and up-to-date laptop recommendations. Through this approach, users can make well-informed decisions that align with their specific needs and environmental considerations. © 2023 IEEE.

关键词： Decision making

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SIMPLE TEMPERATURE MODEL FOR HALF-BRIDGE MODULES IN DC-DC CONVERTERS 11

SIMPLE TEMPERATURE MODEL FOR HALF-BRIDGE MODULES IN DC-DC CO...

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11th International Conference on Power Electronics, Machines and Drives, PEMD 2022

作者： Kunstbergs, Noass Hinz, Hartmut Schofield, Nigel Marquina Sandoval, mathias Diego Roll, Dennis Michalik, Robert Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany Department of Engineering and Technology University of Huddersfield Huddersfield United Kingdom Faculty of Computer Science and Engineering Universidad de Cadiz Cadiz Spain

ISBN: (数字)9781839537189

ISBN: (纸本)9781839537189

Estimating junction temperature during the operation of power semiconductors is essential to avoid damage and to increase lifetime. However, published literature usually presents complex solutions to determine temperature rise. These require parameters that are not available in datasheets. Nevertheless, there is usually no additional information available for the design stage. This study introduces a simple method to determine device junction temperature of half-bridge modules using only datasheet parameters and the ambient temperature. This paper presents results from the proposed circuit design undertaken in the simulation environment PSIM and the simulation tool Semisel. The results are validated with a DC-DC converter operating up to 6.5 kW. The results show that the proposed model can predict temperature rise with a maximum deviation of 5.5%. © IET Conference Proceedings. All right reserved.

关键词： DC-DC converters

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Exploring Deepfake Detection: A Comparative Study of CNN Models

Exploring Deepfake Detection: A Comparative Study of CNN Mod...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Raveena Rita Chhikara Pooja Punyani Department of Applied Sciences The NorthCap University Gurugram India Department of Computer Science Engineering The NorthCap University Gurugram India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

The rapid advancement of deep learning methodologies has given rise to worries regarding the misuse of hyper-realistic multimedia due to the introduction of deepfake content created by generative adversarial network (GAN) models. Deepfakes, which include altered audio and/or video clips that are nearly identical to real ones, can be used maliciously for things like propaganda, cybercrimes, and political campaigns. To address this challenge, a comparison is conducted involving several CNN models, like EfficientNetB0, VGG-16, DenseNet121, VGG-19, MobileNetV2, ResNet50, InceptionV3, and Xception for deepfake detection. The models are trained using transfer learning technique and by fine-tuning them on the dataset using various hyperparameters. The performance analysis was performed on six cases in which the optimizer, learning rate, batch size, and epochs were adjusted. By exploring this comparative study, a contribution is made to the development of more robust solutions for detecting deepfakes. A thorough analysis of different pre-trained models is conducted and verified, based on the reported outcomes, ResNet50 outperforms the other models. The evaluation of the model's performance involves the comparison of various metrics that have been identified, such as Accuracy, Precision, AUC-ROC curve, and F1-score.

关键词： Training Deepfakes Analytical models Accuracy Computational modeling Transfer learning computer architecture

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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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Current Trends in Automated Test Case Generation 18

Current Trends in Automated Test Case Generation

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Potuzak, Tomas Lipka, Richard Department of Computer Science and Engineering/ Ntis - New Technologies for Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 European Center of Excellence Plzen306 14 Czech Republic European Center of Excellence/ Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Ntis - New Technologies for the Information Society Plzen306 14 Czech Republic

ISBN: (纸本)9788396744784

The testing is an integral part of the software development. At the same time, the manual creation of individu-al test cases is a lengthy and error-prone process. Hence, an intensive research on automated test generation methods is ongoing for more than twenty years. There are many vastly dif-ferent approaches, which can be considered automated test case generation. However, a common feature is the generation of the data for the test cases. Ultimately, the test data decide the prog-ram branching and can be used on any testing level, starting with the unit tests and ending with the tests focused on the behavior of the entire application. The test data are also mostly independent on any specific technology, such as programming language or paradigm. This paper is a survey of existing literature of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work. © 2023 Polish Information Processing Society.

关键词： Software testing

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对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学对比分析

引用

Journal of Central South University 2023年第8期30卷 2616-2626页

作者： KHALID Abdulkhaliq M-alharbi HINA Gul MUHAMMAD Ramzan SEIFEDINE Kadry ABDULKAFI Mohammed-saeed Mechanical Engineering Department College of EngineeringUmm Al-Qura UniversityMakkah 24382Sandi Arabia Department of Computer Science Bahria UniversityIslamabad 44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题... 详细信息

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题由一组方程组成,借助MATLAB bvp4c包进行求解。为了使参数与相关剖面的影响可视化,给出了图形说明,并利用图表来评估关键参数变化是如何影响相关场及其相应的物理量。通过对盐1和盐2的Dufour参数的多次估计,可以推断出流体流速的波动。验证了流体动力流相对于流体磁流的优势。此外,还将特定情景下已发表的结果与当前研究结果进行了比较。

关键词：三重扩散纳米流体二次混合对流可变导热系数非线性热辐射

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Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic Algorithm Machine Learning Model Optimization Particle Swarm Optimization PSO Spatial Feature Extraction Whale Optimization Algorithm WOA XGBoost

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A Nonlinear Cryptosystem Based on Structure Phase Masks, QZ Modulation, Using Gyrator Transform Domain

A Nonlinear Cryptosystem Based on Structure Phase Masks, QZ ...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhavana Sharma Hukum Singh Mehak Khurana Department of computer Science and Engineering the NorthCap University Gurugram India Department of Applied Sciences The NorthCap Univesity Gurugram India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This communication delves into an encryption methodology centered around the gyrator transform and the utilization of diverse structured phase masks (SPMs). Unlike conventional random phase masks (RPMs), this approach substitutes them with structured phase masks (SPMs) derived from Devil's Vortex Fresnel Lens (DVFL) and Fresnel Toroidal Lens (FTL). The primary objective behind incorporating these phase keys is to bolster algorithmic security by expanding the key-space. Structured phase masks (SPMs) boast the capability of housing multiple storing keys within a single-phase mask, owing to their nature as diffractive optical elements (DOEs), making them arduous to replace. Consequently, they introduce additional security parameters that enhance the system's robustness and reliability. The efficacy of the proposed schemes has been validated through computer simulations, with assessments conducted via the computation of mean-squarederror (MSE) and peak signal-to-noise ratio (PSNR) between the original and decrypted images. Furthermore, these schemes have undergone scrutiny regarding their sensitivity to various encryption parameters and their resilience against noise attacks.

关键词： PSNR Phase modulation Sensitivity analysis Gyrators Transforms Robustness Encryption

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