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Research on a New Speech Synthesis Fraud Method 9

Research on a New Speech Synthesis Fraud Method

引用

9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Hang Li, Yuhui Kui, Shijian Qin, Zhaoyi Harbin Institute of Technology Faculty of Computing Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China Harbin Institute of Technology Faculty of New Energy Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350376548

With the rapid development of deep learning technology, deep counterfeiting has also quietly ascended to the stage of history, and voice disguise fraud, as a new type of attack means, has laid a huge hidden danger for people's privacy security. Based on the research of speech synthesis, this paper designs and implements a speech disguise synthesis system based on the Tacotron2 model, and uses the WaveGlow model as a vocoder to replace the original WaveNet module, which is used to simulate experimental research from the direction of the attack to achieve a better synthesis effect. After this experiment, this paper verifies the feasibility of the program and provides technical references for subsequent security technology development. © 2024 IEEE.

关键词： Differential privacy

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AI-Powered Diagnostic Tool for Retinal Disease Detection Using Convolutional Neural Networks 5

AI-Powered Diagnostic Tool for Retinal Disease Detection Usi...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Moovendran, Manikandan Kumar, K. K. Dhanush Sashanth, J.M. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India Chennai603203 India

ISBN: (纸本)9798350389609

Accurate and timely detection of retinal disease helps prevent severe vision impairment, although traditional diagnostics take plenty of time and are demanding to perform. This led to the design of the advanced diagnostic tool, in which AI and Convolutional Neural Networks (CNNs) will enhance the identification of retinal disorders. This system will analyze a retina image to detect among others, diabetic retinopathy and age-related macular degeneration, among others. Advanced deep learning algorithms enable the tool to rapidly identify the subtle patterns and anomalies in retinal scans, which even an astute human analyst might not identify, providing insightful and diagnostic support to healthcare experts. This AI-based approach leads to the acceleration of the diagnostic workflow and the minimization of errors, which elevates overall diagnostic accuracy and allows earlier intervention. This technology will contribute to making high-quality retinal care more accessible and efficient, finally arriving at the benefit of a patient at an earlier time of early detection and treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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The Progression of Competencies and Dispositions: How do Maturing Software Engineers Compare? 54

The Progression of Competencies and Dispositions: How do Mat...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM Computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior Software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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Contrastive Language-Entity Pre-training for Richer Knowledge Graph Embedding 4th

Contrastive Language-Entity Pre-training for Richer Knowled...

引用

4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Papaluca, Andrea Krefl, Daniel Lensky, Artem Suominen, Hanna School of Computing The Australian National University Canberra Australia Canberra Australia School of Engineering and Technology The University of New South Wales Kensington Australia School of Biomedical Engineering The University of Sydney Sydney Australia School of Medicine and Psychology The Australian National University Canberra Australia Department of Computing University of Turku Turku Finland

ISBN: (纸本)9789819787012

In this work we propose a pretraining procedure that aligns a graph encoder and a text encoder to learn a common multi-modal graph-text embedding space. The alignment is obtained by training a model to predict the correct associations between Knowledge Graph nodes and their corresponding descriptions. We test the procedure with two popular Knowledge Bases: Wikidata (formerly Freebase) and YAGO. Our results indicate that such a pretraining method allows for link prediction without the need for additional fine-tuning. Furthermore, we demonstrate that a graph encoder pretrained on the description matching task allows for improved link prediction performance after fine-tuning, without the need for providing node descriptions as additional inputs. We make available the code used in the experiments on GitHub(https://***/BrunoLiegiBastonLiegi/CLEP) under the MIT license to encourage further work. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Automated Radiology Report Generation from Chest X-ray Scans Using Deep Learning 6th

Automated Radiology Report Generation from Chest X-ray Scans...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Veer, V. Druva Priya, S. Baghavathi Tamilselvi, M. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India Information Technology Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9789819778614

A chest X-ray is a common diagnostic tool for many thoracic illnesses. Interpreting these images and coming up with accurate diagnostic results is a difficult and time-consuming task for radiologists. Recent results using deep learning approaches to automate the analysis of chest X-rays are promising. This paper provides a comprehensive description of deep learning techniques used to chest X-ray processing for the diagnosis of several diseases, including TB, pneumonia, and COVID-19. This paper provide a summary of the primary deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models, that are used for disease classification, localization, segmentation, and report generation from chest X-rays. This paper also discuss explainability techniques that are used to offer transparency and increase trust in model predictions. Along with existing issues, future directions are taken into consideration, such as multimodal learning, few-shot learning, and model evaluation. This paper explains the model which takes less time and give the optimal results. In order to facilitate future research into developing AI systems that are more accurate, understandable, and advantageous from a medical standpoint, this article provides an overview of the most current developments in deep learning techniques for automated chest X-ray diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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MEDIMIND MATE: The Healthcare ChatBot based on Machine Learning and Natural Language Processing 2

MEDIMIND MATE: The Healthcare ChatBot based on Machine Learn...

引用

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

作者： Choudhary, Aditi Prasad, Guru M.S. Rawat, Anshika Upadhyay, Khushi Gowda, Mahima M. Jain, Amith K. Dehradun India School of Computing & Information Technology Reva University Bengaluru India Department of Electronics and Communication Engineering SDM Institute of Technology Dehradun India

ISBN: (纸本)9798350383867

While one' s health is unquestionably a valuable asset in the modern world of hustle and bustle, it is also susceptible to several threats. Regular visits to healthcare specialists are frequently impeded by the demands of busy schedules. Healthcare chatbots have proven important in addressing this widespread issue. The proposed paper successfully solves this issue by evaluating four distinct algorithms for disease prediction, out of which the random forest classifier produced the highest accuracy of 99.6%, so the chatbot made use of the random forest model, which was trained on a dataset that included illnesses and the symptoms that went along with them. It then uses cosine similarity to predict symptoms accurately to identify possible illnesses. The chatbot also provides information about nearby doctor locations in addition to further specifics regarding the anticipated disease. © 2024 IEEE.

关键词： Adversarial machine learning

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Evolving ATM Networks for High Volume Transactions 5th

Evolving ATM Networks for High Volume Transactions

引用

5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Impa, B.H. Alam, Intekhab Kavitha, R. Prajapati, Sandhya School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

ATM networks are evolving to guide excessive extent transactions and offer new features which includes increased safety, scalability, and cost performance. To lessen value, Banks are utilizing personal cloud environments to host their ATMs, whilst banks are looking for to deploy new offerings that assist their customers provide a higher enjoy. To enhance protection, banks are searching at encrypting data, and leveraging biometric authentication methods. Banks also are seeking to extend their offerings utilization of analytics to better goal purchaser advertising campaigns. Sooner or later, banks are adopting the use of micro services and disbursed architectures to enable building an extra scalable infrastructure that can scale horizontally with minimal effort. A majority of these advances are main to ATM networks which are efficient, comfy and dependable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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A Deep CNN-Based Approach for 10-Class with Two-Channel EMG Signal Classification 4th

A Deep CNN-Based Approach for 10-Class with Two-Channel EMG ...

引用

4th International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2023

作者： Triwiyanto Yulianto, Endro Rahmawati, Triana Chai, Rifai Department of Medical Electronics Technology Poltekkes Kemenkes Surabaya Surabaya Indonesia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9789819714629

Electromyography (EMG) signals have emerged as vital tools for prosthetic control, motor function assessment, and rehabilitation technology. Traditional methods, relying on manual feature extraction, struggle with high-dimensional EMG data. Deep learning, specifically Convolutional Neural Networks (CNNs), has shown promise in addressing these challenges. However, there is a need to explore CNNs’ effectiveness, optimal architecture, real-time processing, and data augmentation for EMG signal classification. This study aimed to introduce a CNN-based approach tailored to classify ten-class, two-channel EMG signals. Eight participants provided EMG data by performing ten distinct finger and hand movements. Data were collected using two-channel EMG sensors, amplified, sampled at 4000 Hz, and filtered. A CNN architecture with eight layers was designed, incorporating one-dimensional convolutional layers, max pooling, global average pooling, dropout layers, and a dense output layer. The CNN-based approach demonstrated promising results. It exhibited high accuracy in classifying EMG signals for most classes, with an overall accuracy of approximately 91%. It excelled in the thumb (THU) and thumb-ring (TH-R) classes. However, some classes, like thumb-middle (TH-M) and thumb-index (TH-I), showed room for improvement in terms of recall. This study introduced an innovative CNN-based approach to tackle the challenges of classifying ten-class, two-channel EMG signals. It addressed critical research gaps and significantly advanced EMG signal processing, particularly for prosthetic control and rehabilitation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Word Based Tic-Tac-Toe Using Reinforcement Learning 15

Word Based Tic-Tac-Toe Using Reinforcement Learning

引用

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

作者： Niveaditha, V.R. Sheba Sulthana, P.R. Bharathi Mohan, G. Doss, Srinath Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350370249

This paper introduces a novel approach in playing Tic Tac Toe by replacing traditional Xs and Os with synonyms sourced from a lexical database, WordNet. Leveraging Reinforcement Learning (RL), our agent learns optimal strategies based on the meanings of these words, enhancing gameplay with language understanding. Through the integration of Natural Language ToolKit (NLTK) to download WordNet, and Gensim for importing Word2Vec model, coupled with Q-learning algorithm, our system dynamically adapts its gameplay to the semantic context of the words occupying the game board. This abstracts the game into a domain where linguistic comprehension influences strategic decision-making, presenting an innovative fusion of language and gaming. © 2024 IEEE.

关键词： Reinforcement learning

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Mitigating Credit Card Fraud: A Machine Learning Perspective 2

Mitigating Credit Card Fraud: A Machine Learning Perspective

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2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Bhushan, Shivam Gupta, Indrajeet Angayarkanni, S.A. Balakiruthiga, B. Pandimurugan, V. Rajaram, V. School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India Acharya Institute of Technology Computer Science and engineering Bangalore India

ISBN: (纸本)9798350366570

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape © 2024 IEEE.

关键词： Random forests

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