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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Ghafoor, Abdul Bakhtyar, Maheen Amin, Hasnan Khalid, Muhammad University of Balochistan Department of Computer Science Balochistan Quetta Pakistan Government Post Graduate College Sariab Road Lecturer Computer Science Balochistan Quetta Pakistan HITEC University Department of Computer Science Punjab Taxila Pakistan

ISBN: (纸本)9798350381320

Speech recognition is an interdisciplinary field drawing from computer science, linguistics, and computer engineering. Many modern devices have speech recognition functions in them to allow for easier. The demand for speech recognition in new languages corresponds to cultural preservation, accessibility, market growth, research, enhanced efficiency, innovation, and empowerment, rendering it a significant pursuit within the realm of language technology. Machine learning-based speech-To-Text conversion has advanced significantly in the modern era. Brahvi, one of the most spoken languages in Balochistan, is used by approximately 4 million people in Pakistan. In this paper, a cutting-edge model for converting isolated Brahvi words from speech to text using recurrent neural networks (RNN) is proposed. Firstly, we introduced processing techniques and algorithms that can be applied to the speech recognition of single words in the Brahvi language. Seventy regularly used Brahvi language words, encompassing a variety of dialects and voice frequencies, were collected to create a pioneering dataset for this language. The specific RNN model proposed for word speech recognition and its validation on a data set in which the proposed model demonstrates 91 % accuracy. © 2023 IEEE.

关键词： Speech recognition

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Recent Trends for Text Summarization in Scientific Documents 9

Recent Trends for Text Summarization in Scientific Documents

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Sukmandhani, Arief Agus Arifin, Yulyani Zarlis, Muhammad Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required;therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization. © 2023 IEEE.

关键词： Text processing

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Classification of Hotel Customer Based on Support Vector Machine

Classification of Hotel Customer Based on Support Vector Mac...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Huda, Choirul Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Indonesia

ISBN: (纸本)9781665499699

The tourism industry has contributed positively for regional and global economic income in recent years. Although this sector faces a hard challenge in COVGD-19 pandemic, it has been predicted that it will rebound and will continue in growing as in previous periods. Tourist experience should become important to be improved regarding the service level for visitors such as the improvement of hotel customer accommodation. The aim of this study is classifying the hotel customer based on Support Vector Machine (SVM). This classification conducted through classification of satisfaction level and the related factors. The result of the study presented a good accuracy and precision of SVM in conducting customer hotel classification at 93% accuracy level with error level of prediction result for about 0.27 based on Root Mean Squared Error (RSME) function. The features of customer satisfaction at this study are proposed to be important preferences for improvement of customer services in hospitality. © 2021 IEEE.

关键词： Hotels

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Static Taint Analysis Traversal with Object Oriented Component for Web File Injection Vulnerability Pattern Detection 3

Static Taint Analysis Traversal with Object Oriented Compone...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Kurniawan, Aditya Abbas, Bahtiar Saleh Trisetyarso, Agung Isa, Sani Muhammad Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia

We introduce a composition of object-oriented component PHP grammar for taint analysis. Our novel method successfully restructured the PHP parser and reduced grammar artifact objects that must be visited in a taint analysis process by up to 52% grammar variation. Taint analysis is an analysis that detects any injection vulnerability pattern in source code. The analysis identifies the information flow of untrustworthy input that affects the sensitive sink or part of the system. The static taint analysis was run on an abstract syntax tree and traversed all nodes. A static taint analysis uses a parser to traverse abstract syntax trees of the source code. A web PHP parser has 140 grammar combinations in an abstract syntax tree, which has to be traversed to recognize the tainted flow pattern. Additionally, there are many variations of syntax and coding styles for tainted flow patterns. Therefore, the amount of combinations will consume many computation resources. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Syntactics

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Hoax Detection in Social Media: A Literature Review 4

Hoax Detection in Social Media: A Literature Review

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Saputra, Carsten Sebastian Wijaya, Norbert Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news. © 2023 IEEE.

关键词： Digital storage

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Vehicle Occurrence-Based Parking Space Detection

Vehicle Occurrence-Based Parking Space Detection

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： De Almeida, Paulo R. Lisboa Alves, Jeovane Honório Oliveira, Luiz S. Hochuli, Andre Gustavo Fröhlich, João V. Krauel, Rodrigo A. Department of Informatics Curitiba Brazil Graduate Program in Informatics Curitiba Brazil Department of Computer Science Joinville Brazil

ISBN: (纸本)9798350337020

Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%. © 2023 IEEE.

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Patient Data Security in Telemedicine Services from Data Misuse in Health Practice 8

Patient Data Security in Telemedicine Services from Data Mis...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Yuninda, Sinka Puji Aga Pasma, Satriani Mantoro, Teddy State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

Misuse of a person's data can be found anywhere because of a lack of knowledge about how to manage data and secure it properly. The rapid development of information technology, creates various activities easily, including in the health sector. In the field of health itself, there is a long-distance consultation using various applications that facilitate long-distance relationships between doctors and patients, known as telemedicine. However, the emergence of this development does not deny the existence of cybermedicine. Cyber medicine is the dissemination of clinical and non-clinical patient information that can be misused for personal gain that can harm others. This can be used in telemedicine, all online-based, so there is a possibility of data leakage. The authors want to examine the extent of data protection for patients who use telemedicine services. This study was conducted using a literature review method taken from articles related to telemedicine. Therefore, there is a need for regulations to provide legal protection for victims who experience theft of personal data by providing certainty in the management of data and information, especially the management of personal data. If the data is not managed properly and appropriately, it will lead to abuse and cybercriminal attacks. © 2022 IEEE.

关键词： Hospital data processing

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Input Representation on Text Data for E-Commerce Product Review Summarization using Graph Convolutional Network 6

Input Representation on Text Data for E-Commerce Product Rev...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sari, Azani Cempaka Heryadi, Yaya Kartowisastro, Iman Herwidiana Budiharto, Widodo Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

This paper investigates recent advancements and methodologies in Automatic Text Summarization, a field that has garnered significant attention for its ability to condense lengthy documents into concise summaries while preserving key information and themes. The study examines the development of Automatic Text Summarization models, including both extractive and abstractive approaches. Additionally, the research delves into the use of graph convolutional networks for text representation in e-commerce product review summarization. The study employs an e-commerce dataset in conjunction with a graph convolutional network model, covering stages from preprocessing and tokenization to constructing graph-based data representation. The expected outcome is a graph-structured representation of input text data derived from Indonesian-language e-commerce product reviews. © 2024 IEEE.

关键词： Convolutional E-commerce Graph Network Product Review Summarization Text

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Autonomous face mask detection using single shot multibox detector, and ResNet-50 with identity retrieval through face matching using deep siamese neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11195-11205页

作者： Vignesh Baalaji, S. Sandhya, S. Sajidha, S.A. Nisha, V.M. Vimalapriya, M.D. Tyagi, Amit Kumar School of Compter Science and Engineering Vellore Institute of Technology Chennai India Post Graduate Department of Computer Science and Technology Women’s Christian College Chennai India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The COVID-19 pandemic poses a global health challenge. The World Health Organization states that face masks are proven to be effective, especially in public areas. Real-time monitoring of face masks is challenging and exhaustive for humans. To reduce human effort and to provide an enforcement mechanism, an autonomous system has been proposed to detect non-masked people and retrieve their identity using computer vision. The proposed method introduces a novel and efficient method that involves fine-tuning the pre-trained ResNet-50 model with a new head layer for classification between masked and non-masked people. The classifier is trained using adaptive momentum optimization algorithm with decaying learning rate and binary cross-entropy loss. Data augmentation and dropout regularization are employed to achieve best convergence. During real-time application of our classifier on videos, a Caffe face detector model based on Single Shot MultiBox Detector is used to extract the face regions of interest from each frame, on which the trained classifier is applied for detecting the non-masked people. The faces of these people are then captured, which is passed on to a deep siamese neural network, based on VGG-Face model for face matching. The captured faces are compared with the reference images from the database, by extracting the features and calculating cosine distance. If the faces match, the details of that person are retrieved from the database and displayed on the web application. The proposed method has secured best results where the trained classifier has achieved 99.74% accuracy, and the identity retrieval model achieved 98.24% accuracy. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset 8

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Setiawan, Tri Agus Heryadi, Yaya Lukas Wulandari, Lili Ayu Sonata, Ilvico Binus Graduate Program -Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350368970

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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