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Secure IoT Entrance Using Mobile Application

Secure IoT Entrance Using Mobile Application

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International Electromagnetic Compatibility Conference (EMC Turkiye)

作者： Marwa Issam Abdulkareem Osama Ibraheem Ashour Yucel Batu Salman dept. software engineering Bahcesehir University Istanbul Turkey ddept. computer engineering Cankaya University Tikrit Iraq

The impact of cyber security attacks increased globally, implementing proper security standards to protect data in the cyber environment has been prioritized, while assets are also at risk of being exploited through physical security breaches. Considering the potential risk of Radio Frequency Identification (RFID) cards being lost or stolen, third parties can effortlessly gain the privileges of an authorized person. In this paper, we propose a design for a secure access control system that operates on an Android phone with multi-factor authentication to enable secure access control systems. For this purpose, the authorized users are registered in a database that can be accessed by the access control system when the user authenticates his$\backslash$her credentials with a login/password as well as biometric data from his$\backslash$her smartphone. This system works when the user is 10 meters away from the access control system, after that the user will be asked to submit his$\backslash$her biometric credentials. The database then compares the user’s keys and grants access if authentication is successful. This design is intended to reduce the number of cases in which unauthorized access to a restricted area. The observed results clearly meet the required security of physical access control systems.

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A Double MCDM-GIS Analysis for Solar Power Plant Site Selections in Disaster-Prone Area: A case study of Hawkes Bay

A Double MCDM-GIS Analysis for Solar Power Plant Site Select...

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Power and Renewable Energy engineering (PREE), International Conference on

作者： Rizki Dian Rahayani Nirmal-Kumar C Nair Dept. Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Dept. Electrical Computer and Software EngineeringUniversity of Auckland Auckland New Zealand

Selecting an appropriate site for solar power plants is a critical long-term investment decision to maximize solar energy generation while minimizing investment and operational costs and ensuring environmental sustainability. While solar irradiance has traditionally been the primary factor in site selection, accounting for disaster risk assessment is becoming increasingly crucial, particularly in countries prone to natural disasters. Hawkes Bay, New Zealand, holds great promise as a region with high solar potential. Yet, it is susceptible to natural disasters like earthquakes, landslides, and floods. This research aims to identify the most suitable solar farm location in Hawkes Bay by integrating estimated solar energy production with disaster risk assessment. The study employs a two-stages multi-criteria decision-making (MCDM) approach, combining data envelopment analysis (DEA) and the analytical hierarchy process (AHP), alongside the utilization of geographical information system (GIS) technology. The finding, among 15 candidate locations, the area in a 5 km radius of Te Haroto weather station has the most potential for solar PV farms. 82 % of the location has low disaster risk, and only 7% of the area is considered high risk.

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Stratifying large software files to improve prediction performance in software defect prediction 22

Stratifying large software files to improve prediction perfo...

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Proceedings of the 9th International Conference on Applied Computing & Information Technology

作者： Yasser Ali Alshehri Noha Alnazzawi Haneen Hijazi Rawan Alharbi Computer Science and Engineering Dept Yanbu Industrial College Saudi Arabia Dept of Software Engrg Hashemite University Jordan Computer Science and Engineering Dept Tabuk University Saudi Arabia

ISBN: (纸本)9781450397605

Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explored the effect of size by stratifying our sample based on each unit’s size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48, and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is better than predicting those in small files. In addition, the results showed higher median values of all performance measures and less variation in each measure.

关键词： machine learning random forest data mining decision tree J48 software quality software fault proneness

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MTDC Fault Detection and Localization Using High-Pass Filter

MTDC Fault Detection and Localization Using High-Pass Filter

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Annual Conference of Industrial Electronics Society

作者： Saad Ahmed Khan Abhisek Ukil Nirmal-Kumar C. Nair Dongyu Li Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Electrical and Automation Engineering East China Jiaotong University Nanchang China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The DC link capacitor discharge in the event of a DC fault is rapid and contains high frequencies. This rapid discharge of high current and frequency interface with DC bus, Voltage Source Converter (VSC), and AC source. This interface damages the equipment and possibly living beings in proximity. Therefore, it is necessary to develop a topology to detect and identify fault types promptly, along with the isolation and restoration of the system. This study introduces an improved novel fault detection technique using the Highpass Chebyshev type 2 filter due to its flatter pass-band response. Further, the polarities of peaks obtained from the fault detection calculation are used again for another proposed novel fault location method. It identifies fault types to locate and isolate the faulty system using only polarities of amplitude response peaks. In the simulation, both methods were fast and accurate in detecting, locating, and identifying appropriate fault types only by High Pass Filter (HPF) amplitude response peaks and their polarities.

关键词： Accuracy Fault detection Voltage Fault location Chebyshev approximation Discharges (electric) Topology Power systems Passband Voltage-source converters

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Samaneh Madanian Sherry Dai Priyadarshini Natarajan Hamidreza Rasoulipanah Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large.

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Two-Factor Authentication Protocol with Zero-Knowledge over an Extended Field of Elliptic Curves

Two-Factor Authentication Protocol with Zero-Knowledge over ...

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International Conference on Radio Electronics & Info Communications (UkrMiCo)

作者： Iryna Strelkovska Oleksiy Onatskiy Larysa Yona dept. Cybersecurity Software Engineering and Computer Science International Humanitarian University Odesa Ukraine

The paper proposes a cryptographic protocol two-factor authentication with the zero-knowledge over the extended field GF(2 m ) on elliptic curves using biometric data and private key of the user. The implementation of a cryptographic protocol with zero-knowledge proof based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking). The cryptographic protocol was modeled in the High-Level Protocol Specification Language, the model validation and protocol verification was performed using the Security Protocol Animator tool for Automated Validation of Internet Security Protocols and Applications. The software verification of the cryptographic protocol was performed using the software modules On the Fly Model Checker and Constraint Logic based Attack Searcher.

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Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models 31

Leveraging Language-based Representations for Better Solving...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yile Cheng, Sijie Sun, Zixin Li, Peng Liu, Yang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

ISBN: (纸本)9798891761964

Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There are also consistent improvements in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://***/THUNLP-MT/symbol2language. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Towards Accurate and Certain Molecular Properties Prediction 13

Towards Accurate and Certain Molecular Properties Prediction

引用

13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Ham, KyungPyo Yoon, JeongNoh Sael, Lee Ajou University Dept. of Artificial Intelligence Suwon Korea Republic of Ajou University Dept. of Software and Computer Engineering Dept. of Artificial Intelligence Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this review, we survey the fundamental problems and their solvers in molecular property prediction problems: graph neural networks modeling and uncertainty estimation. Molecules are naturally represented with graphs, thus graph neural networks, such as graph convolution networks and several types of message passing networks, are their natural models for accurate predictions. However, graph neural networks and any other deep learning models require a large amount of data. Unfortunately, molecular data are insufficient and skewed. Thus, not just an accurate method but models that return uncertainty estimations, such as Bayesian estimations, deep ensembles, and evidential methods, are required. © 2022 IEEE.

关键词： Graph neural networks

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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Frontiers of Information Technology (FIT)

作者： Amna Salahudin Eeman Qadeer Ayesha Ahmed Labiba Gillani Fahad Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold crossvalidation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection.

关键词： Support vector machines Deep learning Adaptation models Accuracy Magnetic resonance imaging Focusing Vectors Robustness Alzheimer's disease Residual neural networks

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN)

Fingerprint generation and authentication though Adaptive co...

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International Multi-Topic ICT Conference (IMTIC)

作者： Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Alina Baber Maria Andleeb Siddiqui Dept. of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan Dept of Software Engineering Sir Syed University of Engineering and Technology karachi Pakistan

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data.

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