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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Janet Kumari, J Jesy Thangam, S. Raja, A. Saleem Amrita Vishwa Vidhyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Technology and Applied Science-Shinas Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350342796

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques. © 2023 IEEE.

关键词： ant colony optimization hybrid protocol proactive reactive VANET

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Boosting the generation rate of squeezed single-photon states by generalized photon subtraction

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Physical Review A 2024年第3期110卷 033717页

作者： Hiroko Tomoda Akihiro Machinaga Kan Takase Jun Harada Takahiro Kashiwazaki Takeshi Umeki Shigehito Miki Fumihiro China Masahiro Yabuno Hirotaka Terai Daichi Okuno Shuntaro Takeda Department of Applied Physics School of Engineering Optical Quantum Computing Research Team NTT Device Technology Labs Advanced ICT Research Institute

In optical quantum information processing with continuous variables, optical non-Gaussian quantum states are essential for universal and fault-tolerant quantum computation. Experimentally, their most typical generation method is photon subtraction (PS), where single-photon detection by an on/off detector probabilistically heralds the generation of squeezed single-photon states. In PS, however, trying to avoid unwanted multiphoton detection inevitably limits the generation rate, hindering the application of squeezed single-photon states. Here, we theoretically show that generalized photon subtraction (GPS), a simple extension of PS, can improve the generation rate while maintaining the quality of the generated states. Furthermore, we experimentally demonstrate the generation rate improvement for 2-dB- and 4-dB-squeezed single-photon states compared to PS, by more than one order of magnitude, particularly for the case of 2 dB. Our results will accelerate the application of squeezed single-photon states to more advanced quantum information protocols.

关键词： Quantum information processing Quantum optics

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Exploring the Usability of Quantum Machine Learning for EEG Signal Classification 13th

Exploring the Usability of Quantum Machine Learning for E...

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13th International Advanced computing Conference, IACC 2023

作者： Singh, Devansh Kanathey, Yashasvi Waykole, Yoginii Mishra, Rohit Kumar Walambe, Rahee Aqeel, Khan Hassan Kotecha, Ketan Symbiosis Institute of Technology Symbiosis International University Pune India Centre for Development of Advanced Computing Delhi India SIU Pune India Department of Applied Artificial Intelligence and Robotics Aston University Birmingham United Kingdom

ISBN: (纸本)9783031566998

The classification of Electroencephalogram (EEG) signals into distinct frequency bands is a critical task in understanding brain function and diagnosing neurological disorders. The information obtained from frequency-specific classification has multiple applications, such as frequency-based wheelchair control, frequency-based 36-stroke brain operated keyboard for paralysed patients etc. In this work, a method based on machine learning to develop the frequency-based classification of EEG signals is proposed. The performance of Classical Machine Learning (CML) algorithms and Quantum Machine Learning (QML) techniques for the classification of EEG signals across four frequency bands are investigated. The primary objective is to evaluate the performance of QML models against traditional CML models in terms of computational efficiency, time efficiency and accuracy and uncover potential benefits offered by quantum computing for a particular task of classifying EEG signals. The goal is to assess the advantages of using quantum algorithms for classifying EEG signals. This includes improving accuracy and enhancing efficiency. These findings add to the existing knowledge about how quantum machine learning can benefit neuroscience in terms of enhancing methods that rely on EEG data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350308167

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains. © 2023 IEEE.

关键词： mAP50 mAP50-95 Object Detection Vehicle Detection YOLO Framework

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired 2

Artificial Intelligence and Machine Learning for Accessibili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Palarimath, Suresh Radhakrishnan, Venkateswaran Veerasamy, Devarajan Valsalan, Prajoona Viswan, Vimbi Thumu, Muni Balaji College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798331505745

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone. © 2025 IEEE.

关键词： Contrastive Learning

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Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

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Computers, Materials & Continua 2023年第3期74卷 6195-6212页

作者： T.Gopalakrishnan Mohamed Yacin Sikkandar Raed Abdullah Alharbi P.Selvaraj Zahraa H.Kareem Ahmed Alkhayyat Ali Hashim Abbas Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Public Health Department College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq College of Technical Engineering The Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped *** the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional *** imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the *** general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature *** current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying *** presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical ***,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input *** addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are ***,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

关键词： Deep learning medical imaging fusion model chaotic models ensemble model COVID-19 detection

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Adaptive energy-preserving algorithms for guiding center system

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Plasma Science and technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Overview of the CLEF-2023 CheckThat! Lab: Task 2 on Subjectivity in News Articles 24

Overview of the CLEF-2023 CheckThat! Lab: Task 2 on Subjecti...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Galassi, Andrea Ruggeri, Federico Barrón-Cedeño, Alberto Alam, Firoj Caselli, Tommaso Kutlu, Mucahid Struß, Julia Maria Antici, Francesco Hasanain, Maram Köhler, Juliane Korre, Katerina Leistra, Folkert Muti, Arianna Siegel, Melanie Türkmen, Mehmet Deniz Wiegand, Michael Zaghouani, Wajdi University of Bologna Italy Qatar Computing Research Institute HBKU Qatar University of Groningen Netherlands TOBB University of Economics and Technology Turkey University of Applied Sciences Potsdam Germany Darmstadt University of Applied Sciences Germany University of Klagenfurt Austria

We describe the outcome of the 2023 edition of the CheckThat!Lab at CLEF. We focus on subjectivity (Task 2), which has been proposed for the first time. It aims at fostering the technology for the identification of subjective text fragments in news articles. For that, we produced corpora consisting of 9,530 manually-annotated sentences, covering six languages —Arabic, Dutch, English, German, Italian, and Turkish. Task 2 attracted 12 teams, which submitted a total of 40 final runs covering all languages. The most successful approaches addressed the task using state-of-the-art multilingual transformer models, which were fine-tuned on language-specific data. Teams also experimented with a rich set of other neural architectures, including foundation models, zero-shot classifiers, and standard transformers, mainly coupled with data augmentation and multilingual training strategies to address class imbalance. We publicly release all the datasets and evaluation scripts, with the purpose of promoting further research on this topic. © 2023 Copyright for this paper by its authors.

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Enhancing Battery Voltage Prediction with Deep Learning: A Comparative Analysis of LSTM and Traditional Models 6

Enhancing Battery Voltage Prediction with Deep Learning: A C...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Niakan, Masoumeh Rostam Hajihosseini, Mojtaba Madani, Seyed Saeed Ziebert, Carlos Nri Department of Electricity and Energy Economics Tehran Iran University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350305623

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting. © 2023 IEEE.

关键词： Battery voltage prediction Deep learning Long Short-Term Memory neural networks

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A Study on Challenges of FinTech for Improving Accounting Information Systems (AIS)

A Study on Challenges of FinTech for Improving Accounting In...

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computing and Data Analytics (ICCDA), International Conference on

作者： Sara Sadiq Al Balushi Wilfred Blessing N. R Nawf Taresh Ghadeer Al-Badi Amal Mohammed Al-Maamari College of Economics and Business Administration University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

One of the controversial topics globally is the use of technology in the financial aspect, as money, economics, and data are very sensitive matters for many companies, organizations, and governments that manage and store their data in accounting information systems, which face various challenges when improving them using FinTech. The use of technology in departments related to money may reduce users' confidence in accounting information systems, increase the risk to the privacy of their data and sensitive information, and provide the opportunity for hackers to devise new methods with the aim of stealing money and sensitive information. This research studies the challenges that hinder financial technology in improving accounting information systems and discusses the main concepts of financial technology such as Blockchain, artificial intelligence, cryptocurrencies, and digital currencies, and discusses concepts of accounting information systems such as cloud computing, information systems, and data security, as understanding the problem is the beginning of arriving at the solution. We will add recommendations to resolve these issues and improve accounting information.

关键词： Economics Data privacy Data analysis Online banking Fintech Data security Government Artificial intelligence Faces Information systems

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