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IEEE India Conference (INDICON)

作者： Abhilash C B Kavi Mahesh Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad Dharwad India Dept. of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (纸本)9781665452724

Finding interesting association rules is a popular and current topic in data mining. The Apriori family of algorithms is built around two rule extraction measures: support and confidence. Even though these two measures are easy to compute, they yield many rules, the majority of which are redundant and may not be of interest to the user. Also, by themselves, support and confidence do not generate strong rules. Additional measures are required to mine interesting facts from data. Ontologies have become the fundamental building blocks for structuring and formalizing data. With the semantic structuring of information, the implicit relationship between data elements makes the analyst get important facts from the data. Our study proposes a novel framework for interestingness in data by combining domain ontology with semantic interestingness measures. The ontology-based method infers rules that are semantically enriched and strong. We analyze the quality of the rule considering the factors defined by the domain experts. It is observed that our methodology generates semantically enriched rules that are more acceptable to domain experts.

关键词： COVID-19 Current measurement Semantics Knowledge based systems Influenza Ontologies Resource description framework

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Traffic Sign Detection and Recognition Using YOLOv5 and Its Versions

Traffic Sign Detection and Recognition Using YOLOv5 and Its ...

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Cognitive Mobility (CogMob, IEEE International Conference on

作者： Aaron Joaquin Lebumfacil Patricia Angela Abu Dept. of Information Systems and Computer Science Ateneo Laboratory for Intelligent Visual Environments Ateneo De Manila University Quezon City Philippines

A Traffic Sign Detection and Recognition (TSDR) System, which helps navigate vehicles through computer vision and human-machine communication, has to perform quickly as vehicles using them travel at high speeds. During this study, a speedy one-stage detector such as YOLOv5, a deep learning model, was chosen to dive into. This study explores creating a TSDR model by comparing four different versions of YOLOv5, namely YOLOv5 Nano, Small, Medium, and Large. This study was accomplished by first creating a new traffic sign dataset. The four versions of the YOLOv5 algorithm were then trained with a 75–25 train validation split, and 24 models were created. Afterwards, the models were tested on a test set, and their metrics were tallied. Results showed that YOLOv5 Medium and Large offer a 10% increase in accuracy performance when compared to YOLOv5 Small, but due to the slower detection speed of YOLOv5 Large, the YOLOv5 Medium models are a better fit when it comes to the detection of traffic signs when prepared by a relatively small dataset. This study provides an overview of the performance of the different YOLOv5 versions in traffic sign detection and recognition that aims to contribute to the improvement of traffic sign detectors.

关键词： Measurement Deep learning Navigation Computational modeling Detectors Real-time systems Task analysis

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Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules

arXiv

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arXiv 2024年

作者： Rahman, Md Abdur Barek, Md Abdul Riad, A.B.M. Kamrul Islam Rahman, Md Mostafizur Rashid, Md Bajlur Ambedkar, Smita Miaa, Md Raihan Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States IDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to varioud machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. © 2024, CC BY.

关键词： Embeddings

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Using the recurrence plots as indicators for the recognition of Parkinson’s disease through phonemes assessment

Using the recurrence plots as indicators for the recognition...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios Skaramagkas Anastasia Pentari Dimitrios I. Fotiadis Manolis Tsiknakis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) Heraklion Crete Greece Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology (FORTH) Ioannina Greece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

Parkinson’s disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients’ life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice’s dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural Network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.

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ASD Detection using Higuchi's Fractal Dimension from EEG

ASD Detection using Higuchi's Fractal Dimension from EEG

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2021 IEEE International Conference on Biomedical Engineering, computer and Information Technology for Health, BECITHCON 2021

作者： Peya, Zahrul Jannat Ferdous, Md. Akhand, M.A.H. Zilani, Mohammed Golam Siddique, Nazmul Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh James Cook University QLD Australia Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9781665466219

Autism or Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder marked by repetitive and characteristic patterns of behavior as well as social communication and interaction impairments. Nowadays ASD is a great concern worldwide and its detection is an important issue for the better treatment. As ASD is neurodevelopmental disorder, brain signals play, especially electroencephalography (EEG), is shown potential sources for ASD detection. There are different approaches for ASD detection with processing and/or transforming EEG signals. This study investigated ASD detection employing fractal dimension measure on EEG data. Higuchi's Fractal Dimension (HFD) is measured in resting-state eyes-closed EEG recording of 25 subjects. It is identified that HFD is sensitive to the brain activity and ASD detection is possible from HFD values. © 2021 IEEE.

关键词： Electroencephalography

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Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering systems

arXiv

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arXiv 2025年

作者： Ji, Yuelyu Zhang, Hang Wang, Yanshan Dept. of Information Science University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Dept. of Health Information Management University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Medical Question-Answering (QA) systems based on Retrieval-Augmented Generation (RAG) are promising for clinical decision support due to their capability to integrate external knowledge, thus reducing inaccuracies inherent in standalone large language models (LLMs). However, these systems may unintentionally propagate biases associated with sensitive demographic attributes like race, gender, and socioeconomic factors. This study systematically evaluates demographic biases within medical RAG pipelines across multiple QA benchmarks, including MedQA, MedMCQA, MMLU, and EquityMedQA. We quantify disparities in retrieval consistency and answer correctness by generating and analyzing queries sensitive to demographic variations. We further implement and compare several bias mitigation strategies—including Chain-of-Thought reasoning, Counterfactual filtering, Adversarial prompt refinement, and Majority Vote aggregation—to address identified biases. Experimental results reveal significant demographic disparities, highlighting that Majority Vote aggregation improves accuracy and fairness metrics. Our findings underscore the critical need for explicitly fairness-aware retrieval methods and prompt engineering strategies to develop truly equitable medical QA systems. © 2025, CC BY.

关键词： Decision support systems

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md Abdur Rahman Hossain Shahriar Fan Wu Alfredo Cuzzocrea Dept. of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Center for Cybersecurity University of West Florida Pensacola FL USA Department of Computer Science Tuskegee University Tuskegee AL USA iDEA Lab University of Calabria Rende Italy

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities.

关键词： Support vector machines Logistic regression Accuracy Large language models Bidirectional control Transformers Encoding Multilingual Tuning Random forests

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Convolutional Neural Network-based Keyword Classification for Mixer Control

Convolutional Neural Network-based Keyword Classification fo...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Suz-Ting Wang Jeng-Dao Lee Wen-Ho Juang Ming-Hwa Sheu Shin-Chi Lai Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Doctor’s Program of Smart Industry National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This study proposed a keyword recognition of microphone feed-in, achieving the effect of allowing users to control the real-time mixer system intelligently. The proposed system structure includes a front-end circuit to capture voice and Mel-scale Frequency Cepstral Coefficients (MFCC) features in conjunction with a Convolutional Neural Network (CNN) algorithm. The testing results show that the lightweight CNN model achieves a keyword recognition accuracy rate of 96.4%.

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Online Task Scheduling Algorithm with Complex Dependencies in Edge Computing

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Procedia computer science 2022年 202卷 158-163页

作者： Lei Shi Zhaoxing Ma Yuqi Fan Yi Shi Xu Ding Zhehao Li School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Procince Hefei University of Technology Hefei 230009 China Virginia Tech Dept. of ECE Blacksburg VA 24061 USA

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time.

关键词： Edge computing Online scheduling Dependent task

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