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5th International Conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Kashmar, Ali H. Shihab, Ammar I. Jawad, Wafaa S. University Of Baghdad Dept. Of Computer Science College Of Science Baghdad Iraq Islamic University Of Lebanon Faculty Of Engineering Dep. Of Computer And Communication Beirut Lebanon

ISBN: (纸本)9798350335118

This paper aimed to suggest a new Secured Hash Algorithm 3 (SHA-3) with seed value implementation. Seed worth ensuring that there are fewer expectations collisions, despite an attacker selecting the data. Furthermore, attackers who combine seed values with hash functions are prevented from exploiting the properties of a hash function to construct many security attacks, such as denial of service attacks and dictionary attacks. A novel approach to enhanced SHA-3 diffusion was proposed here by XORing the seed value with θ step result after transforming the seed bits into a 5 × 5 × 64 matrix and discarding the rest. © 2023 IEEE.

关键词： Hash functions

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Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Enhancing Freezing of Gait Detection in Parkinson’s Through...

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Digital Health (ICDH), IEEE International Conference on

作者： Michele Tebaldi Graziano Pravadelli Florenc Demrozi Rosalba Giugno Cristian Turetta Dep. of Computer Science University of Verona Italy Dep. of Engineering for Innovation Medicine University of Verona Italy Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson’s Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson’s. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-indep.ndent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to $20.9 \%$ in F1-score performance compared to the training and testing approach without fine-tuning.

关键词： Training Deep learning Radio frequency Measurement Machine learning algorithms Refining Physiology

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM) 31

Parallel and hierarchically-distributed Shoreline Alert Mode...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： De Vita, Ciro Giuseppe Mellone, Gennaro Florio, Aniello Charles, Catherine Alessandra Torres Di Luccio, Diana Lapegna, Marco Benassai, Guido Budillon, Giorgio Montella, Raffaele University of Naples 'Parthenope' Dep. of Science and Technologies Naples Italy University of Madrid Carlos Iii Dep. of Computer Science and Engineering Madrid Spain University of Naples 'Federico II' Dept. of Mathematics and Applications Naples Italy

ISBN: (纸本)9798350337631

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment. © 2023 IEEE.

关键词： Parallel architectures

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

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11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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Comparative Analysis of ML Algorithms for Fraud Detection

Comparative Analysis of ML Algorithms for Fraud Detection

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara Abitova Miras Abalkanov Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Binghamton NY USA Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.

关键词： Training Logistic regression Machine learning algorithms Accuracy Sensitivity Credit cards Fraud

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Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning

arXiv

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arXiv 2024年

作者： Varotto, Matteo Valentin, Stefan Tomasin, Stefano Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy

Cellular networks are potential targets of jamming attacks to disrupt wireless communications. Since the fifth generation (5G) of cellular networks enables mission-critical applications, such as autonomous driving or smart manufacturing, the resulting malfunctions can cause serious damage. This paper proposes to detect broadband jammers by an online classification of spectrograms. These spectrograms are computed from a stream of in-phase and quadrature (IQ) samples of 5G radio signals. We obtain these signals experimentally and describe how to design a suitable dataset for training. Based on this data, we compare two classification methods: a supervised learning model built on a basic convolutional neural network (CNN) and an unsupervised learning model based on a convolutional autoencoder (CAE). After comparing the structure of these models, their performance is assessed in terms of accuracy and computational complexity. © 2024, CC BY.

关键词： Convolution

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Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning

Detecting 5G Signal Jammers Using Spectrograms with Supervis...

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IEEE International Conference on Communications Workshops, ICC

作者： Matteo Varotto Stefan Valentin Stefano Tomasin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

关键词： Wireless communication 5G mobile communication Computational modeling Data models Convolutional neural networks Synchronization Jamming

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A Region-Wide Cost-Benefit Assessment of Residential V2G Participation in Demand Response

A Region-Wide Cost-Benefit Assessment of Residential V2G Par...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Khunsha Nasr Hany Farag Shivam Saxena Dep. Of Electrical Engineering and Computer Science York University Toronto Canada Dep. Of Electrical Engineering and Computer Engineering University of New Brunswick Fredericton Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Participation of Electric Vehicles (EVs) in providing grid services is becoming popular for the economic benefits to their stakeholders, i.e., Local Distribution Companies (LDCs), ratepayers, and EV owners. Battery Electric Vehicles can be effectively used for power system applications such as demand response and peak shaving. However, there is a lack of region-wide cost-benefit analysis of Vehicle to Grid (V2G) participation in providing grid services. Thus, this paper introduces a model that could be utilized to perform residential V2G mainly to evaluate its effect on non-participating ratepayers as a measure of fairness. The developed model yields the benefit-to-cost ratios (BCRs) for LDCs (administrators), ratepayers (those who don’t participate in V2G), and EV owners (participants).

关键词： Vehicle-to-grid Sensitivity analysis Particle measurements Demand response US dep.rtment of Defense Power systems Stakeholders

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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