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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第2期86卷 61-72页

作者： Dorobăț, Ilie Cristian Posea, Vlad Boncea, Radu Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics-ICI Bucharest Romania

The paper introduces the Cultural Leaf Semantic Portal whose aim is to provide a consolidated user-friendly solution for exploring the 36,132 Romanian artifacts and the 1,086 cultural entities that house them. Using the eCHO framework, the data collected from official sources are subjected to a transformation process through which they are sanitized, temporal expressions are normalized, thus reducing the number of forms in which the centuries and millennia are expressed from 1,733 to only 67, and then they are transformed into semantic structures. Finally, the resulting data is accessible via both a SPARQL endpoint and a semantic portal. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Semantic Web

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Unravel News: An Efficient Summarization Approach

Unravel News: An Efficient Summarization Approach

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Saha, Ankan Al Shafi, Abdullah Khulna University of Engineering and Technology Dept. Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

The process of automatically creating a concise and informative summary of a news piece is known as news summarizing. This can be a useful tool for people who want to quickly get the gist of a news story without having to read the entire article. Extractive and abstractive summarization are the two basic methods used to summarize news. The process of extractive summarization entails locating and separating the key phrases from the source text. In this paper, sentence weighting is used for extractive summarization. The words in the headlines are given ten times more emphasis than the other words in the news article. The final summarization is done by rearranging the sentences as per the sequence in the main news article. The paper concludes by discussing the future of news summarization, which is an active area of research. © 2024 IEEE.

关键词： Abstractive Summary Extractive Summary ROUGE Sentence Rank Algorithm Stemming Text Rank Algorithm

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-dept. analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning Algorithms 2

Image Segmentation based Approach for Skin Disease Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sany, Nazmul Hasan Chandra Shill, Pintu Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity. © 2024 IEEE.

关键词： Decision Tree Image Processing K-NN Machine Learning Skin Disorder SVM

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An Efficient Deep Learning Technique for Intrusion Detection in Wireless Sensor Network 3

An Efficient Deep Learning Technique for Intrusion Detection...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kumari, Prity Tiwari, Rakesh Kumar Technocrats Institute of Technology and Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350322842

Predicting intrusion detection in a wireless sensor network (WSN) involves using machine learning algorithms to analyze network traffic data and predict whether an intrusion or security breach will occur. The goal of intrusion prediction is to provide advanced warning of potential attacks and allow for proactive measures to be taken to prevent or mitigate the attack's impact. Several machines and deep learning algorithms can be used for intrusion prediction in WSNs. This research describes a practical deep-learning approach for Intrusion Detection in WSN based on artificial neural networks. Spyder IDE of python with version 3.7 is used for the simulation work. The results of the simulations demonstrate that the anticipated model is accurate 99.99% of the time. © 2023 IEEE.

关键词： Python

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the 'Happy' (one of the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approach for Hate and Non Hate Speech Detection in Online Social Media 3

Deep Learning Approach for Hate and Non Hate Speech Detectio...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Sharma, Parshuram Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and groups, contribute to the polarisation of society, and even provoke acts of physical violence. Therefore, identifying and removing hate speech from online social media is an important task for the purpose of maintaining an online environment that is healthy and respectful. A method based on deep learning is proposed in this study for differentiating between hate speech and other types of communication that may be found in online social media. The solution that has been developed makes use of a natural language processing (NLP) and long term short memory (LSTM) model that is trained on a huge dataset consisting of tweets that have been annotated. Tweets that include hate speech and tweets that do not contain hate speech were both included in the dataset and were labelled by human annotators. Python software with IDE of Spyder version 3.7 is used to carry out the simulation work. The accuracy level reached overall is 91.14%. © 2023 IEEE.

关键词： Hate LSTM Machine Learning NLP Social Media Speech Twitter

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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A Systematic Study of Challenges in Cervical Cytology Image Segmentation of Intelligence based Cervical Cancer Screening 4

A Systematic Study of Challenges in Cervical Cytology Image ...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Anu, P.M. Lakshmi, C. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350305258

Cervical Cancer (CC) is the fourth frequent dreadful cancers that prevails in women across the globe. Cervical cytology is being adopted as the standard technique for the screening of pre-cancerous stage and cancerous lesions detection. For accurate segmentation in cervical cytology, the smear images play a crucial role in any Artificial Intelligence based computer Aided Diagnosis (CAD) method. This relies upon the precise segmentation of nucleus present in the cervical cytology smear images. The motive of this study deals with a systematic analysis of the various challenges that are encountered while segmenting the Cervical Cell Nucleus and the countermeasures that are incorporated to eliminate the misdiagnosis and missed diagnoses in the early stage of screening. With the motive of this, a systematic study of the published articles from the sources - Scopus, PubMed and Web of science are analyzed and reviewed. The importance of this study is due to the intensity of the disease that still positions cervical cancer as a bothering disease that disrupts the life of about five lakh female population, every year across the world. © 2023 IEEE.

关键词： Cervical Cancer Cervical Cytology Cervical Nucleus Challenges in Cervical Cytology Segmentation Intelligence based Cervical screening

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