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International Conference on Computer Theory and Applications (ICCTA)

作者： Rghda Salah Tarek Sharaf Nada Tayel Doaa Shawky Communication and Information Engineering University of Science and Technology Zewail City Giza Egypt Software Development Program University of Science and Technology Zewail City Giza Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Adversarial attacks pose a significant challenge to the security and reliability of machine learning models. This paper focuses on detecting black-box adversarial attacks, where attackers operate without access to the model's internal parameters or architecture—a scenario more relevant to real-world threats than white-box settings, where such knowledge is assumed. Two novel detection methodologies are introduced: (1) a clustering-based approach that groups input data to identify anomalies and (2) a class-centric method that uses statistical properties of labeled data to compute class centers and boundaries for detecting deviations. These methods utilize thresholds derived from dynamic statistical metrics to ensure robust, efficient, and scalable *** experiments validate the effectiveness of these approaches using the Iris dataset. Results demonstrate that the clustering-based method excels in detecting adversarial samples, achieving high accuracy, while the class-based method offers a conservative alternative by minimizing false positives. Both methodologies provide practical solutions for enhancing model security, particularly in scenarios where model details are unavailable, thereby addressing the limitations of existing defense mechanisms. Future work will explore scalability and application to higher-dimensional datasets.

关键词： Measurement Gradient methods Computational modeling Scalability Closed box Machine learning Robustness Computer security Iris recognition Glass box

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Enhancing Video Action Recognition with Spatial–Temporal Convolutional Attention Networks 15th

Enhancing Video Action Recognition with Spatial–Temporal Co...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analytics of Attack Patterns

ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analyt...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Inbasat Fiza Fizza Khurshid Manahil Fatima Umara Noor Zahid Rashid Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz.

关键词： Computers Navigation Visual analytics Manuals Cyberattack

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Optimizing Insurance Strategies through Disaster Risk Assessment Models: A Case Study of Earthquakes in Japan

Optimizing Insurance Strategies through Disaster Risk Assess...

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Computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Qiyuan Luo Xingnan Zhang Yuanxin Zhang Burra Venkata Durga Kumar Meifeng Liu Program in Computer Science & Technology Xiamen University Malaysia Kuala Lumplur Malaysia Program in Software Engineering Xiamen University Malaysia Kuala Lumplur Malaysia Program in Mathematics and Applied Mathematics Xiamen University Malaysia Kuala Lumplur Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

With the increasing frequency of extreme weather events and natural disasters, insurance providers around the world are facing enormous challenges. This study addresses insurance claims resulting from these disasters by developing a comprehensive disaster insurance risk assessment model, focusing on Japan, a country prone to frequent earthquakes. The model integrates a Catastrophe Model, the Analytic Hierarchy Process (AHP), and an expert scoring method to optimize the risk assessment and compensation process for historic buildings. The results show that the model has strong predictive power, which provides valuable insights for insurance companies, policymakers, and urban planners in mitigating the impact of disasters and protecting cultural heritage.

关键词： Analytical models Disasters Earthquakes Buildings Insurance Predictive models Risk management Cultural differences Meteorology Context modeling

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Asma Bibi Umara Noor Zahid Rashid Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Department of Computer Science Kohsar University Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of “Mobile Twin” is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of “Revolutionary Ultra-low latency (RULL)” in the metaverse.

关键词： Computers Metaverse Computer architecture Network architecture Data processing Mobile handsets Digital twins

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FPGA Utilized Implementation of Epileptic Seizure Detection System Based on Wearable Devices using Dynamic Partial Reconfiguration 10

FPGA Utilized Implementation of Epileptic Seizure Detection ...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Fawzy, Mohamed Hussien, Ahmed Mostafa, Hassan Siemens Digital Industries Software Cairo Egypt Cairo University Electronics and Electrical Communications Engineering Department Giza Egypt University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology October Gardens 6th of October Giza12578 Egypt

ISBN: (纸本)9781665464635

Unplanned seizures are caused by a disorder in the central nervous system known as epilepsy. Although significant advancements have been made in the realm of non-EEG wearable devices, there is still much room for improvement in the field of EEG-based seizure detection and prediction using ML. The management of epilepsy has a lot of promise to be aided by non-invasive wearable technology. The suggested study intends to design and implement a support vector machine (SVM) classification-based epileptic seizure detection system based on various wearable devices. The proposed technique for detecting seizures accomplishes According to data for seizure detection, our system consistently achieves a sensitivity of 100% and an accuracy of 97%. High level MATLAB model creation is part of the design cycle. Despite the fact that high performance cannot be achieved with just one signal. Although a high performance detection system cannot achieve the requisite sensitivity and accuracy with a single signal, we presented various combining techniques. RTL modelling, design optimization, FPGA implementation, and functional verification are all included in the implementation cycle. The capacity of the FPGA's partial dynamic reconfiguration is suggested for implementation in order to make better use of the available resources. Comparing the proposed implementation to relevant earlier work, it demonstrated improved utilization. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors

Creating an Inclusive, Vibrant Learning Environment within a...

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CoNECD 2021

作者： Bailey, Margaret B. Sharma, Naveen Verschage, Lana Conlon, Timothy T. Department of Mechanical Engineering Kate Gleason College of Engineering Rochester Institute of Technology Department of Software Engineering Golisano College of Computing and Information Sciences Rochester Institute of Technology Xerox Corporation Women in Computing Program Golisano College of Computing and Information Sciences Rochester Institute of Technology

Creating an Inclusive, Vibrant Learning Environment within a Large, software engineering program - Experiential Learning Experiences Created for Students, Faculty, and Senior Design Coaches & Sponsors This presentation describes several initiatives currently underway within a large software engineering (SE) department at a private university within the northeast US to positively affect levels of inclusiveness within the department's learning environment. Information gathered from faculty, staff, student interviews and focus group discussions, as well as a department goal to improve female freshmen retention in the SE major motivated the SE department chair, departmental academic advisors and faculty, and the college's women in computing director to launch this set of aligned activities in collaboration with the NSF ADVANCE funded program (NSF #1209115) at the university level. The resulting activities ideally enhance diversity and inclusion for students from all underrepresented groups in the program with a focus on groups based on gender. Four experiential learning experiences have been created and concurrently implemented within the SE department to promote an inclusive academic environment. These include: (1) faculty targeted discussions and summer readings, (2) an interactive workshop designed for all first year SE students, (3) a workshop created for the coaches and sponsors, most of who are not regular RIT employees, who directly mentor student teams for the two‐semester senior project course, and (4) the development of a resource flowchart which supports students, faculty, and staff in maintaining an inclusive learning environment within the department. The approach used in creating each is adaptive and the four resulting products are multi‐faceted in regards to target audience, modality of learning experience, and composition of creation team. Furthermore, we will discuss key metrics aligned with the department's goal to measure impacts resulting from these

关键词： Students

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Erratum to “Investigation of the effect of colorless distributed combustion method on the flame of methane/hydrogen fuel mixture using image processing method” [Int. J. Hydrogen Energy 116 4 (2025) 312-322]

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International Journal of Hydrogen Energy 2025年 128卷 359-366页

作者： Bugrahan Alabas Osman Kumuk Ahmet Ozcan Department of Aeronautical Engineering Faculty of Aeronautics and Astronautics Erciyes University Kayseri Turkiye Unmanned Aerial Vehicle Technology and Operatorship Program Iskenderun Vocational School of Higher Education Iskenderun Technical University Hatay Turkiye Deparment of Software Development School of Applied Sciences Cappadocia University Nevsehir Turkiye

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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