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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Atsadawut Surinrangsee Ausdang Thangthai Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand National Electronics and Computer Technology Center(NECTEC) Thailand

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

This paper investigates a novel method for handling with the scarcity of appropriate speech corpora for Text-to-Speech (TTS) systems in the Thai language by repurposing Automatic Speech Recognition (ASR) corpus. In this paper, we focus on whether noisy ASR by exploiting the Miipher speech restoration model approach and examine its suitability for TTS corpus *** study compares single-speaker and multi-speaker Tacotron2 models trained on both original and Miipher-processed datasets. Through a series of experiments including A/B preference tests and Mean Opinion Score (MOS) evaluations, we demonstrate the efficacy of our approach. Results show a clear preference for Miipher-processed data over original noisy data, with 67.65% of participants favoring the processed audio. As expected, given the limited amount of data per speaker in the multi-speaker dataset, the single-speaker TTS model outperformed the multi-speaker model in preference tests, with 63.55% of participants preferring the single-speaker system. MOS evaluations revealed that Miipher-processed data achieved the highest score of 3.69 ± 0.15, followed by the original noisy data at 3.56 ± 0.17, while both TTS systems showed lower scores. These findings indicate that repurposing ASR data with appropriate processing can effectively improve speech quality, but translating this improvement to TTS systems requires further investigation. Our results suggest that the quantity of per-speaker data may be more crucial than speaker diversity for TTS development using processed ASR data. We have made demo audio samples available at "From ASR to TTS: Enhancing Synthesis with Cleaned ASR data (***)".

关键词： Translation data integrity Speech enhancement Market research data models Natural language processing Cleaning Text to speech Noise measurement Automatic speech recognition

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Detection and Discrimination of Arabic Phonemes Using Long Short-Term Memory (LSTM) Model

Detection and Discrimination of Arabic Phonemes Using Long S...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Rahma M. Tolba Zaki T. Fayed Hamzah A. Alsayadi NourEldin O. Saad Taha Elarif Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Faculty of Sciences Ibb University Yemen Data Science Department School of Computer Science Canadian International Collage New Cairo Egypt

Phonetics is a crucial branch of linguistics that studies human speech sounds and is essential for language learning, speech therapy, and speech technology development. However, current Arabic speech systems cannot instantly analyze and detect mispronunciation of Arabic phonemes, only offering a recording feature for self-evaluation. So, this paper presents a state-of-the-art approach using a Long Short-Term Memory (LSTM) model for detecting and discriminating between Arabic phonemes. The model was tested on a collected dataset for the Arabic phonemes /s/ 'sīn' and /sˤ/ 'ṣād and demonstrated excellent performance in discriminating between them despite their close articulation. The model achieved an accuracy of 97.33% and outperformed other techniques proposed for small datasets. The model can be utilized either independently or integrated into a computer-assisted language learning (CALL) system. Furthermore, our findings suggest that there is significant room for improvement in this area, particularly in collecting large audio datasets for all the Arabic phonemes, refining the algorithms, and optimizing the data processing pipeline.

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Speech as a Window to the Brain: Innovative Techniques in Early Alzheimer’s Disease Detection

Speech as a Window to the Brain: Innovative Techniques in Ea...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Anass El Hallani Adil Chakhtouna Abdellah Adib Team Data Science and Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Hassan II University Mohammedia Morocco

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

As dementia cases are projected to reach 150 million by 2050, with Alzheimer’s disease (AD) accounting for $60-70 \%$ of instances, early detection becomes crucial. AD and other dementias currently rank as the seventh leading cause of death globally. Our study addresses this by employing speech analysis for AD detection, offering a non-invasive and cost-effective diagnostic tool. Utilizing the ADReSS dataset, we integrated advanced Deep Learning techniques, including YAMNet and VGGish, with Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction. An autoencoder was employed for dimensionality reduction to isolate key speech features. We applied Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) for classification. The ANN model outperformed others, achieving an impressive $89.3 \%$ accuracy, surpassing current methodologies. This research highlights the efficacy of using speech analysis and ML, DL for early AD detection. Our approach, accessible and precise, promises advancements in early diagnosis and care for AD. Future work will aim to enhance model applicability to various stages of AD and related disorders.

关键词： Support vector machines Radio frequency Dimensionality reduction Speech analysis Accuracy Artificial neural networks Feature extraction

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Utilization of Machine Learning in Breast Cancer Detection

Utilization of Machine Learning in Breast Cancer Detection

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Information Technology (ACIT)

作者： Mutaz Momani Nidal Yousef Qotadeh Aljawazneh A.T Issa Suhaila Abuowaida Raed Alazaidah Mohammed Talat Khouj Radwan Batyha Esraa Abu Elsoud Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Al-Albayt University Mafraq Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ML) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ML algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ML algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ML-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on dataset 2. In dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study.

关键词： Machine learning algorithms databases Machine learning Feature extraction Breast cancer Pattern recognition Classification algorithms Medical diagnosis Information technology Medical diagnostic imaging

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Extending RIS Life Span for Reliable Communication Under Hardware Ageing Effects

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3395-3409页

作者： Mondal, Atiquzzaman Ghosh, Shyamal Singh, Keshav Biswas, Sudip Duong, Trung Q. Indian Institute of Information Technology Guwahati Department of Electrical Communication Engineering Guwahati781015 India Indian Institute of Science Education and Research Thiruvananthapuram Department of Data Science Thiruvananthapuram695551 India National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS's life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments. © 2020 IEEE.

关键词： Wireless sensor networks

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EpiDoc data Matching for Federated Information Retrieval in the Humanities 18

EpiDoc Data Matching for Federated Information Retrieval in ...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Melzer, Sylvia Klettke, Meike Weise, Franziska Harter-Uibopuu, Kaja Moller, Ralf Centre for Study of Manuscript Cultures Universität Hamburg Warburgstraße 26 Hamburg20354 Germany Institute of Information Systems University of Luebeck Ratzeburger Allee 160 Luebeck23562 Germany Faculty for Computer Science and Data Science University of Regensburg Regensburg93053 Germany

ISBN: (纸本)9788396744784

The importance of federated information retrieval (FIR) is growing in humanities research. Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised collection of documents, FIR treats each information system as an independent source with its own unique characteristics. Searching these systems together as a centralised source results in lower precision in humanities research, even when the research data itself is structured and stored according to standardised guidelines such as EpiDoc, and requires the need to be able to trace the origin of records to avoid incorrect historical conclusions. Matching of queries against all data sets in each source is proving less effective. A global search index that enables traceable matching of key values deemed relevant would provide a more robust solution here. In this article, we propose a solution that introduces a novel EpiDoc data matching procedure, facilitating traceable FIR across distinct epigraphic sources. © 2023 Polish Information Processing Society.

关键词： Information retrieval

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