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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Raeesi, Fatima Al Kuwaiti, Mariam K. Al Berengueres, Jose United Arab Emirates University Dept. of Software Engineering Al Ain United Arab Emirates United Arab Emirates University Dept. of Computer Science & Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large. © 2023 IEEE.

关键词： Students

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Laser-assisted Multidimensional Imaging Modalities for Early Detection of Tumours in Bio Tissues: An In Vitro Study

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Lasers in engineering 2025年第4-6期59卷 199-215页

作者： Akhtar, M. Ullah, H. Aslam, K. Siddique, M.A. Akram, A. Majid, M.A. Abdullah-Al-Wadud, M. Aleem, A. Farhan, M. Batool, Z. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

The idea, upon which this research work depends, is the early detection of tumours in different biological soft tissues like bladder, respiratory and thigh tissues. Imaging and spectroscopic technique that were used to study healthy and cancerous tissues were white light microscopy and Fourier-transform infrared radiation (FTIR) spectroscopy. The observation and calculations collected through all these imaging techniques prove as helping tools to differentiate healthy and cancerous body tissues. Soft tissues from patients who had been diagnosed with bladder, respiratory, and thigh cancer were gathered from the Bahawalpur Victoria Hospital, Pakistan while keeping in mind the ethical research perspective. Two-dimensional (2-D) white light microscopy images showed that healthy and cancerous tissues have a different kind of shapes, sizes and structure arrangements. Normal tissues images show a regular pattern of structure arrangement, but cancerous tissues show an irregular shapes, sizes, and structures. Similarly, FTIR spectroscopy graphs represents different peaks of stretching vibrations of C-H, O-H, C-C, N-H, CH2 double and triple bonding of different biological tissues like bladder, respiratory and thigh tissues. This research work will help in the medical field for early detection of tumour which helps to diagnose the tumour and also helpful for better treatment planning in early stage. ©2025 Old City Publishing, Inc.

关键词： Fourier transform infrared spectroscopy

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks 7

A Comparative Study of the Performance of Real time database...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammd Nabeel Farooq, Muhammad Umer Zehra, Syeda Sundus Noronha, Jospeh Peter Terenece Ned University of Engineering and Technology Dept. of Computer Science & I.T Karachi Pakistan Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics. © 2023 IEEE.

关键词： Big data

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT 10

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Munoz-Lara, Rodrigo Cespedes, Sandra Diaz, Marcos University of Chile Dept.of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng. MontrealQC Canada

ISBN: (纸本)9798350373011

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites. © 2024 IEEE.

关键词： Internet of things

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A Study on Traceable Group Signature Scheme for Blockchain 8

A Study on Traceable Group Signature Scheme for Blockchain

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8th International Conference on Information and Network Technologies, ICINT 2023

作者： Song, Hyo-Jin Lee, Im-Yeong Soonchunhyang Univ. Dept. of Software Convergence Asan-si31538 Korea Republic of Soonchunhyang Univ. Dept. of Computer Software Engineering Asan-si31538 Korea Republic of

ISBN: (纸本)9798350301458

A blockchain is a decentralized database system in which a ledger containing transactions is stored and maintained identically by all through consensus among network participants without a central server. Instead of having a central server, network participants make sure that everyone stores and maintains the same ledger containing transactions through consensus. Integrity and reliability of the data stored in the blockchain are guaranteed, but on the other hand, privacy issues may arise. The pseudonymity of the public key reused in transactions causes a privacy invasion of the user's behavioral pattern, asset size, and offline identity. Therefore, instead of assigning unique public keys to everyone, they are provided through a single signature in a blockchain environment. It can also be described as a method for ensuring anonymity by having members of the same group use the same public key in a transaction while employing the group signature, which has been studied in this paper. This paper defines the key used for group signature is defined as a composition of a 'group private key' and a 'partial key'. Mainly among them, we consider the leakage of the partial key. In the proposed scheme, it is possible to trace the leaker and the signer from the signature generated by the outsider with the leaked partial key. In this paper, we provide a secure traceable group signature scheme to cope with the leakage of the group signing key. © 2023 IEEE.

关键词： Blockchain

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning 9

Intrusion Detection System (IDS) of Multiclassification IoT ...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Hazim, Layth Rafea Jasim, Abdulrahman Ahmed Ata, Oguz Ilyas, Muhammad College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Altinbas University Istanbul Dept. of Software Engineering Turkey Altinbas University Istanbul Dept. of Computer Engineering Turkey

ISBN: (纸本)9798350316926

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency. © 2023 IEEE.

关键词： Network security

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3D Face Reconstruction with Graph Attention Based on Action Unit Features 39

3D Face Reconstruction with Graph Attention Based on Action ...

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39th International Conference on Information Networking, ICOIN 2025

作者： Kim, Hyeonjin Lee, Hyukjoon Kong, Jinheung Kwangwoon University Dept. of Computer Engineering Seoul South Korea Kwangwoon University Dept. of Embedded Software Enngeeing Seoul South Korea

ISBN: (纸本)9798331506940

The reconstruction of 3D face shapes and expressions from 2D images remains unconquered due to the lack of detailed modeling of human facial movement based on the correlation between the different parts of faces. We propose to use facial action units (AUs), which are detailed taxonomy of the human facial movements based on observation of activation of muscles or muscle groups, in the 3D facial expression reconstruction to allow for detailed modeling of various facial expression types. We present a novel 3D face reconstruction framework called Action Unit feature-based Graph Attention Network Encoder (AUGANE) that can generate a 3D face model that is responsive to AU activation given a single monocular 2D image to capture expressions. AUGANE leverages AU-specific features as well as facial global features to enable accurate 3D facial expression reconstruction with Graph Attention Network Encoder (GANE). We also introduce a novel loss function which is to force learning toward the minimal discrepancy in AU activation between the input and rendered reconstruction. The experimental results demonstrate the superior performance of the proposed framework over state-of-the-art methods. © 2025 IEEE.

关键词： 3D face reconstruction 3DMM action units graph attention transformer

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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification 6

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

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6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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MTDC Fault Detection and Localization Using High-Pass Filter 50

MTDC Fault Detection and Localization Using High-Pass Filter

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Li, Dongyu Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China

ISBN: (纸本)9781665464543

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. © 2024 IEEE.

关键词： Chebyshev filters

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