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arXiv 2024年

作者： Zhang, Lin-Peng Broersma, Hajo Wang, Ligong School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710129 China Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede7500 AE Netherlands

An r-graph H is a hypergraph consisting of a nonempty set of vertices V and a collection of r-element subsets of V we refer to as the edges of H. An r-graph H is called linear if any two edges of H intersect in at most one vertex. Let F and H be two linear r-graphs. If H contains no copy of F, then H is called F -free. The linear Turán number of F, denoted by exlinr (n, F), is the maximum number of edges in any F -free n-vertex linear r-graph. The crown C13 (or E4) is a linear 3-graph which is obtained from three pairwise disjoint edges by adding one edge that intersects all three of them in one vertex. In 2022, Gyárfás, Ruszinkó and Sárközy initiated the study of exlin3 (n, F) for different choices of an acyclic 3-graph F . They determined the linear Turán numbers for all linear 3-graphs with at most 4 edges, except the crown. They established lower and upper bounds for exlin3 (n, C13). In fact, their lower bound on exlin3 (n, C13) is essentially tight, as was shown in a recent paper by Tang, Wu, Zhang and Zheng. In this paper, we generalize the notion of a crown to linear r-graphs for r ≥ 3, and also generalize the above results to linear r-graphs. © 2024, CC BY.

关键词： Graphic methods

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Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehensive Approach for Phishing Email Detection

Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehe...

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Information Technology (ACIT)

作者： Mahmoud AlJamal Rabee Alquran Mohammad Aljaidi Osama S AlJamal Ayoub Alsarhan Issa Al-Aiash Ghassan Samara Mohammad BaniSalman Mohammad Khouj Department of Cybersecurity Faculty of Science and Information Technology Irbid National University Irbid Jordan Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Al Hussein Technical University Amman Jordan Jordan Research and Training Reactor(JRTR) Irbid Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Emails stand as a predominant mode of online communication, facilitating the exchange of information. With the ubiquity of emails, there has been a marked rise in unsolicited messages, particularly phishing emails. These deceptive emails aim to mislead recipients, often masquerading as trustworthy entities, to extract confidential information. Embedded with dubious offers, advertisements, malicious links, malware, and trojans, perpetrators deploy these emails with malicious intent, primarily to commit fraud. Consequently, distinguishing between phishing attempts and genuine emails becomes paramount. This study constructs machine learning-based models to detect phishing emails, ensuring accurate discernment between genuine and fraudulent messages. Leveraging two datasets, the Fraud Email Dataset and the Phishing Email Dataset, we developed and evaluated classifiers, including Logistic Regression, Decision Tree, Random Forest, and SVM. The Random Forest model emerged as the most effective, achieving accuracies of 98.72% on the Fraud Email Dataset and 99.15% on the Phishing Email Dataset, demonstrating strong generalizability across diverse phishing patterns. These results underscore the efficacy of ensemble methods for enhancing cybersecurity by reliably detecting phishing emails.

关键词： Support vector machines Logistic regression Accuracy Phishing Electronic mail Fraud Trojan horses Reliability Information technology Random forests

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Diagnosis of Liver Disease Induced by Hepatitis Virus Using Machine Learning Methods 8

Diagnosis of Liver Disease Induced by Hepatitis Virus Using ...

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8th International Conference on Information Technology Trends, ITT 2022

作者： Hafeez, Muhammad Ahmad Imran, Azhar Khan, Muhammad Imran Khan, Ali Haider Nawaz, Asif Ahmed, Sheeraz Air University Department of Creative Technologies Islamabad Pakistan Air University Faculty of Computing and A.I Department of Creative Technologies Islamabad Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan Faculty of Electrical Engineering Higher Colleges of Technology Dubai United Arab Emirates Iqra National University Department of Computer Sciences Peshawar Pakistan

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Liver, the largest vital organ in the human body, helps in digestion, energy storage, and toxic removal processes. Hepatitis A, B, C, fatty liver, cirrhosis, and liver cancer all cause swelling of the belly and legs, changes in the color of urine, and jaundice. Early identification is necessary to save the lives of those infected with the Hepatitis virus. Although clinical tests such as Abdominal Ultrasound, Chloride Blood Test, Liver Biopsy, Liver Function Tests (LFT), Magnetic Resonance Cholangio Pancreatography (MRCP), Total Protein and Albumin/Globulin (A/G) Ratio, and others are used in the diagnosis of Hepatitis virus, but high-level expertise of the doctors are also required. With continual advancements in Artificial Intelligence (AI) and Machine Learning algorithms, medically intelligent systems for early detection of fatal liver disease can be developed for prediction of deadly liver disease in the early stage. The goal of this paper is to examine the accuracies of several Machine Learning algorithms such as Linear Regression, Decision Tree, Random Forest, KNN, and SVM. For this work, author used the data set accessible at the UCI database and proved that the Support Vector Machine perform better than all other methods with.91.84% accuracy. © 2022 IEEE.

关键词： Decision trees

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Classification of Libya Floods and Morocco Earthquakes Using CNN-Based Deep Learning Models

Classification of Libya Floods and Morocco Earthquakes Using...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Kibitok Abraham Moataz Abdelwahab Mohammed Abo-Zahhad School of Electronics Communications & Computer Engineering Egypt-Japan University of Science & Technology (E-JUST) New Borg El Arab City Alexandria Egypt Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt

Natural disaster's impact on human lives has been costly. The Libya floods and Morocco earthquakes affected more than two million people. Deep learning and computer vision techniques can mitigate the challenges brought by natural disasters. To enhance this field, a dataset from these two natural disasters, including aerial and lateral images, has been collected, augmented, and developed where 13 deep learning models, including AlexNet, VGG16, VGG19, GoogleNet, InceptionV3, ResNet-1S, ResNet-50, ResNet-101, DenseNet-201, NasNetLarge, EfficientNet-b0, MobileNetV2, and SqueezeNet have been tested. In addition, the newly developed YOLOvS deep learning models have been applied to the dataset. The YOLOvS-m model achieves the highest accuracy of 97.36 %. On the other hand, the SqueezeNet and YOLOvS-n model stand out for their lightweight nature, boasting a compact size of 2.658 MB and 3 MB, respectively. The AlexN et model exhibits a short training time of 367 seconds relative to other models that have been implemented. The YOLOvS-x model demonstrates the highest precision rate of 9S.4 %. Conversely, the YOLOvS-1 model exhibits the highest recall rate of 9S.51 % and the best F1-Score of 97.8 %.

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Performance evaluation of risc-based memory-centric processor architecture 1

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9th computer science On-line Conference, CSOC 2020

作者： Efnusheva, Danijela Computer Science and Engineering Department Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

ISBN: (数字)9783030519742

ISBN: (纸本)9783030519735

Recent technological advances in computer hardware and software industry resulted in a wide range of single- and multi-core processors, operating systems, compilers and applications, with the ultimate goal to increase the overall performances of the computing system. Although the number of transistors that can fit on a chip has been doubling every 18 months, there is still difficult to improve the performances of sequential single-core processors, and even of parallel many-processor and multi-core shared-memory systems. The main issue resides in the computer’s hardware and software inability to alleviate the occurrence of processor-memory bottleneck and to provide parallel instruction execution at peak level. Therefore, this paper proposes a memory-centric approach of parallel processing in a distributed system, which includes several RISC-based processor cores that integrate on-chip memory and provide direct access to it, without using registers and cache memory. Furthermore, the execution of several algorithms with different arithmetical intensity is evaluated and the achieved improvements are discussed. © Springer Nature Switzerland AG 2020.

关键词： Parallel programming

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Probing Rotational Decoherence with a Trapped-Ion Planar Rotor

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Physical Review Letters 2025年第3期134卷 033601-033601页

作者： Neil Glikin Benjamin A. Stickler Ryan Tollefsen Sara Mouradian Neha Yadav Erik Urban Klaus Hornberger Hartmut Häffner Department of Physics University of California Berkeley Berkeley California 94720 USA Challenge Institute for Quantum Computation University of California Berkeley Berkeley California 94720 USA Institute for Complex Quantum Systems and Center for Integrated Quantum Science and Technology Ulm University Albert-Einstein-Allee 11 89069 Ulm Germany Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Present address: Exponent Inc. Electrical Engineering and Computer Science Practice Warrenville Illinois USA. University of Duisburg-Essen Faculty of Physics Lotharstraße 1 47057 Duisburg Germany

The quantum rotor is one of the simplest model systems in quantum mechanics, but only in recent years has theoretical work revealed general fundamental scaling laws for its decoherence. For example, a superposition of orientations decoheres at a rate proportional to the sine squared of the angle between them. Here, we observe scaling laws for rotational decoherence dynamics for the first time, using a 4 μm diameter planar rotor composed of two Paul-trapped ions. We prepare the rotational motion of the ion crystal into superpositions of angular momentum with well-defined differences ranging from 1−3ℏ, and measure the rate of decoherence. We also tune the system-environment interaction strength by introducing resonant electric field noise. The observed scaling relationships for decoherence are in excellent agreement with recent theoretical work, and are directly relevant to the growing development of rotor-based quantum applications.

关键词： Trapped ions

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Student Perceptions of AI-Enhanced Adaptive Learning Systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ahmed Sharafeldeen Hossam Magdy Balaha Ibrahim Shawky Farahat Mohammed Ghazal James Connelly Eric van Bogaert Ayman El-Baz Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology Faculty of Medicine University of Louisville Louisville KY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system’s performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen’s kappa, and accuracy, demonstrating impressive results. Specifi-cally, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems.

关键词： COVID-19 Support vector machines Correlation Computed tomography Lungs Signal processing Feature extraction Ventilation Lesions Speech processing

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Integrating Innovative Pedagogies in engineering: The Impact of Temperature on PV Cells

Integrating Innovative Pedagogies in Engineering: The Impact...

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International Conference on Control, Instrumentation, and Automation

作者： Mehraneh Samadani Elahe Moradi Roghayeh Gavagsaz-Ghoachani Environmental Science and Engineering Department Islamic Azad University Science and Research Branch Tehran Iran Department of Electrical and Computer Engineering Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch Islamic Azad University Tehran Iran Renewable Energy Engineering Department Faculty of Mechanical and Energy Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

This paper explores the impact of temperature on photovoltaic (PV) cell performance, emphasizing the critical role of innovative teaching methods in engineering education. By integrating illustrative representations and analogies, such as the popcorn analogy, complex concepts related to solar energy, and PV cell efficiency are made accessible. The study examines key factors, like the Shockley five-parameter model and the effects of environmental conditions on PV cell efficiency, highlighting how higher temperatures reduce open-circuit voltage (V OC ) and overall power output. Using visual aids and storytelling techniques, aims to enhance student understanding and retention of these intricate topics. The findings underscore the importance of educational strategies that foster deeper comprehension and critical thinking, ultimately preparing future engineers to contribute effectively to sustainable energy solutions. This approach not only aids in better grasping the technical aspects but also cultivates a sense of environmental responsibility among students.

关键词： Temperature sensors Photovoltaic systems Visualization Renewable energy sources Temperature distribution Cooling Solar energy Artificial neural networks Voltage Sustainable development

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