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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Khan, Oneza Tehreem Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665495295

Recent years has provided extremely interesting and exciting developments and applications of Machine Learning (ML) and Deep Learning (DL) to automize healthcare delivery. ML techniques are surpassing human performances in detecting, predicting, diagnosing any disease and even recommending treatments for the same. ML along with Deep Learning has revolutionized Medical Imaging Technology. Brain Tumor detection is one of the many healthcare area Artificial Intelligence has marveled at. A tumor is basically an area or part of the body suffering by swelling caused by distorted tissues where the cells start multiplying uncontrollably. Magnetic Resonance Imaging (MRI) is the technique used to detect the tumor. MRI is quite commonly used in medical science to detect abnormalities in any part of the body. Tumor in the brain is far more perilous and different to treat than in any other part of the body which makes the early prediction and monitoring of brain tumor extremely expedient. This paper discusses the various algorithms, techniques, new approaches and their comparison with the existing ones and the general process of brain tumor detection with the help of MRI. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction 4

Historical Ink: 19th Century Latin American Spanish Newspape...

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4th International Conference on Natural Language Processing for Digital Humanities, NLP4DH 2024

作者： Manrique-Gómez, Laura Montes, Tony Rodríguez-Herrera, Arturo Manrique, Rubén History and Geography Department Universidad de los Andes D.C. Bogotá Colombia Systems and Computing Engineering Department Universidad de los Andes D.C. Bogotá Colombia Civil and Environmental Engineering Department Rice University HoustonTX United States

ISBN: (纸本)9798891761810

This paper presents two significant contributions: First, it introduces a novel dataset of 19th-century Latin American newspaper texts, addressing a critical gap in specialized corpora for historical and linguistic analysis in this ***, it develops a flexible framework that utilizes a Large Language Model for OCR error correction and linguistic surface form detection in digitized *** semi-automated framework is adaptable to various contexts and datasets and is applied to the newly created dataset. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

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Computers, Materials & Continua 2022年第6期71卷 5511-5521页

作者： U˘gur Ayvaz Hüseyin Gürüler Faheem Khan Naveed Ahmed Taegkeun Whangbo Abdusalomov Akmalbek Bobomirzaevich Department of Computer Engineering Istanbul Technical UniversityIstanbul34485Turkey Department of Information Systems Engineering Mugla Sitki Kocman UniversityMugla48000Turkey Artificial Intelligence Lab Department of Computer EngineeringGachon UniversitySeongnam13557Korea Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these *** of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high *** represents a sequence of voice signal-specific *** experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech *** the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D *** learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the *** highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation *** most important output of this study is the inclusion of human voice as a new feature set.

关键词： Automatic speaker recognition human voice recognition spatial pattern recognition MFCCs spectrogram machine learning artificial intelligence

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Vehicle Trajectory-Based Intersection Classification Using Deep Transfer Learning 5

Vehicle Trajectory-Based Intersection Classification Using D...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Kased, Abanoub Rabee, Rana Fahmy, Akram Mohamed, Hussein Yacoub, Marco Elhenawy, Mohammed Ashqar, Huthaifa I. Alsobeh, Anas Masoud, Mahmoud Hassan, Abdallah A. Minia University Faculty of Engineering Computers and Systems Department Minia Egypt Australia Arab American University Civil Engineering Department Jenin Palestine School of Computing Southern Illinois University Carbondale CarbondaleIL United States Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Department of Information Systems and Operations Management Dhahran Saudi Arabia

ISBN: (纸本)9798350354690

This document is a model and instructions for M-TEX. The accelerated expansion of global road networks necessitates the availability of accurate and up-to-date cartographic data for connected and automated vehicles. Conventional mapping techniques, such as satellite imagery and field surveys, are inher-ently time-consuming and labor-intensive. This study proposes a novel approach that makes use of the ubiquity of GPS-embedded devices to facilitate the efficient updating of road maps. We present a classifier that identifies the types of intersections from vehicle trajectories, which represents a crucial initial step in the generation of automated maps. The methodology employs five distinct convolutional deep neural network architectures for the detection and classification of road intersection types. The experimental results, which were obtained using three real-world vehicle trajectory datasets, demonstrate classification accuracy levels that range from 84 % to 91 %. It is noteworthy that these results were achieved using less than 10% of the data that is typically required for comparable accuracy levels, which highlights the efficiency of the feature extraction approach that was employed. This research contributes to the advancement of intelligent transportation systems by offering a more efficient and scalable method for road network mapping and updating. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study on Embedding Models for Keyword Extraction Using KeyBERT Method 13

A Comparative Study on Embedding Models for Keyword Extracti...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Issa, Bayan Jasser, Muhammed Basheer Chua, Hui Na Hamzah, Muzaffar University of Aleppo Faculty of Informatics Engineering Syria Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350340891

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords;this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models;the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text;accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text;accordingly, we recommend using it in that context. © 2023 IEEE.

关键词： Embeddings

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Three-Phase SRF PLL Model for System Frequency Response Studies in Low-Inertia systems 11

Three-Phase SRF PLL Model for System Frequency Response Stud...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Krpan, Matej Erceg, Igor Kuzle, Igor Pandžić, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9798350399660

This paper derives a small-signal model of a three-phase synchronous reference frame phase-locked loop for use in system frequency response studies assuming a system of balanced three-phase voltages. The small-signal model is described by a second-order transfer function which relates the actual to the estimated grid frequency. The small-signal model behaves identically to the large-signal model for small disturbances. Simulation results demonstrate that phase-locked loop dynamics can be neglected in the high-inertia scenarios, while in the low-inertia scenarios they have a significant impact on the grid frequency and should be included in models. © 2022 IEEE.

关键词： Phase locked loops

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Traffic Sign Detection Using YOLOv8

Traffic Sign Detection Using YOLOv8

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Rahul Kumar Aniket Gupta Rajeswari D Department of Data Science and Business Systems School of computing College of Engineering and Technology

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy.

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Digital capabilities assessment for supporting the transformation of the customer experience

arXiv

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arXiv 2024年

作者： Leonardo, Munoz Avila, Oscar Department of Systems and Computing Engineering School of Engineering Universidad de los Andes Bogotá Colombia

Most of organizations are increasingly investing huge amounts of money today in order to have the right digital capabilities required for their industry. The area where organisations feel they have made the most progress is in improving the customer experience, which encompasses aspects such as data analytics, social media, location-based marketing, mobile channels among others. This aspect became the most important for the survival of organisations since the outbreak of the Covid-19 pandemic. While much has been achieved, many organisations are still not satisfied. One of the major problems in moving forward is the lack of literature in both academia and industry on maturity models allowing organisations to understand their current state in terms of digital capabilities to engage with customers, as well as to plan the evolutionary path to improve in this area. To fulfil this lack, this paper presents the design and validation of a maturity model that enables organizations to assess their digital capabilities in order to improve customer experience and engagement throughout the customer lifecycle. © 2024, CC BY-SA.

关键词： Sales

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