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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Snehalatha, N. Mohana Kumar, S. Kavitha, H. Nisha, S. Gowda, Puttabore Krishna Prasad, Sj Jss Academy of Technical Education Dept. of Computer Science and Engineering Bangalore India Bangalore India Siddaganga Institute of Technology Dept. of Information Science Tumukur India Bangalore India Ramaiah Institute of Technology Dept. of Me Bangalore India Visvesvaraya College of Engineering Dept. of Ec University Bangalore India

ISBN: (纸本)9798350372892

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities © 2024 IEEE.

关键词： Deep neural networks

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A Dementia Diagnosis Technique Based on AI and Hardware Acceleration

A Dementia Diagnosis Technique Based on AI and Hardware Acce...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Khalil, Kasem Khan Mamun, Mohammad M. R. Sherif, Ahmed Elsersy, Mohamed Imam, Ahmad Abdel-Aliem Abouzaid, Kamal University of Mississippi Electrical and Computer Engineering Department MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. Al Ain United Arab Emirates William Carey University College of Osteopathic Medicine Mississippi MS United States

ISBN: (纸本)9798350322347

The effects of Alzheimer's disease (AD) are devastating, both personally and within the patient's family, as the disease progresses slowly over many years. It could significantly affect illness consequences and outcomes if detected early and accurately. Non-invasive medical testing often uses blood bio-samples because they are easy to collect and relatively cheap to analyze. This paper proposes a federated learning (FL)-based diagnostic model for AD using blood bio-samples. We have used blood bio-sample data sets downloaded from the ADNI website to test and compare the efficacy of our models. According to the enormous data collected for early AD detection, we used a hardware acceleration scheme to implement our FL model to fasten the training and testing operations. The hardware-Accelerator method is implemented using VHDL and Altera 10 GX FPGA. The simulation results show the proposed algorithms reach an accuracy and sensitivity of 89% and 87%, respectively, for early detection while taking less time to train than other state-of-The-Art algorithms. The proposed algorithms also consume 52-172 mW, which makes them suitable for constrained devices. © 2023 IEEE.

关键词： Diagnosis

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Study of the contextual factors of a project affecting the build performance in Continuous Integration

Study of the contextual factors of a project affecting the b...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent systems, AICERA/ICIS 2023

作者： Benjamin, Jetty Mathew, Juby Jose, Rubin Thottupurathu Amal Jyothi College of Engineering Dept. of Computer Applications Kerala Kanjirappally686 518 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Amal Jyothi College of Engineering Dept. of Computer Science and Engineering Kerala Kanjirappally686 518 India

ISBN: (纸本)9798350303452

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure. © 2023 IEEE.

关键词： Decision trees

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Embracing errors is more effective than avoiding them through constrained coding for DNA data storage 59

Embracing errors is more effective than avoiding them throug...

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59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Weindel, Franziska Gimpel, Andreas L. Grass, Robert N. Heckel, Reinhard Technical University of Munich Dept. of Computer Engineering Munich80333 Germany Eth Zurich Dept. of Chemistry and Applied Biosciences Zurich8093 Switzerland

ISBN: (纸本)9798350328141

DNA is an attractive medium for digital data storage. When data is stored on DNA, errors occur, which makes error-correcting coding techniques critical for reliable DNA data storage. To reduce the number of errors, a common technique is to include constraints that avoid homopolymers (consecutive repeated nucleotides) and balance the GC content, as sequences with homopolymers and unbalanced GC contents are often associated with larger error rates. However, constrained coding comes at the cost of an increase in redundancy. An alternative is to control the errors by randomizing the sequences, embracing the extra errors, and paying for them with additional coding redundancy. In this paper, we determine the error regimes in which embracing errors is more efficient than constrained coding. We find that constrained coding is inefficient in most common error regimes for DNA data storage. Specifically, the error probabilities for homopolymers and unbalanced GC contents must be very large for constrained coding to achieve a higher code rate than unconstrained coding. © 2023 IEEE.

关键词： DNA

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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification 6

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

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6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation

Secured Entry Control: An IoT and Deep Learning based Mask D...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Abida Sultana Hasibul Islam Peyal Md. Injamul Islam Md. Nazmul Abdal Shafiun Miraz Md. Mahin Rahaman Kontho Zannatul Mawa Leion Abdul Al Mamun Dept. of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering Uttara University Dhaka Bangladesh Dept. of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science and Engineering Tejgaon College Dhaka Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of $98.96 \%$. The model’s reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of $98.96 \%$, which is close to the optimal AUC score of $100 \%$. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system’s ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT.

关键词： COVID-19 Temperature sensors Deep learning Workability Accuracy Autonomous systems Computational modeling

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Speech Emotion Recognition using a Novel Feature Vector based on Voiced Probability and Speech Characteristics 6

Speech Emotion Recognition using a Novel Feature Vector base...

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6th IEEE Conference on Information and Communication Technology, CICT 2022

作者： Shah, Asfahan Bhowmik, Tanmay Bennett University Dept. of Computer Science and Engineering Greater Noida India Pandit Deendayal Energy Univeristy Dept. of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9781665473125

Emotion recognition in speech is one of the fast growing fields in artificial intelligence and machine learning. Feature selection is an essential process for speech emotion recognition and hence in this study a feature vector is proposed which is based on various characteristic of speech such as Zero Crossing rate, Fundamental Frequency and Mel Frequency Cepstrum Coefficients. The novelty that is introduced in this feature vector is using Probability of whether a frame is voiced or not This probability is concatenated with the rest of vector. The feature vector was tested on various classifiers such as CNN, SVM, Decision Tree, Logistic Regression. The result showed that the novel feature vector performed better as compared to conventional features used which is MFCC and its derivatives. Besides testing this feature vector against MFCC and its derivatives, feature vector (combined with a classifier) was also tested against other system of SER based on ML. The results showed that combination of feature vector and CNN were good and even outperformed some state of art techniques. © 2022 IEEE.

关键词： Emotion Recognition

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High Impedance Fault Detection in Distribution Networks using Unsupervised Learning Technique 2

High Impedance Fault Detection in Distribution Networks usin...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Bhatnagar, Maanvi Yadav, Anamika Swetapadma, Aleena National Institute of Technology Dept. of Electrical Engineering Raipur India KIIT Deemed to Be Universityname School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350320923

An algorithm is proposed in this article that detects High Impedance Faults (HIFs) in distribution networks using a novel approach. It is often difficult to detect HIFs in distribution grids because of the small magnitude of fault current and its non-linear variations. The purpose of this study is to investigate the possibility of automatically learning latent features from raw, unlabeled voltage and current data with the help of unsupervised feature learning. In order to identify transient behavior associated with HIFs and other typical faults, signal features are extracted using an autoencoder. A quadratic discriminant analysis (QDA) is then used to detect the presence of fault in the system. A fault detection accuracy of 98% is achieved by the proposed methodology. A modified IEEE 33 Bus benchmark system is used to validate the developed technique. © 2023 IEEE.

关键词： Unsupervised learning

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Machine Learning Approaches for Early Diagnosis of Breast Cancer: A Comparative Study of Performance Evaluation

Machine Learning Approaches for Early Diagnosis of Breast Ca...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Al Mamlook, Rabia Shresth, Sujeet Gharaibeh, Tasnim Almuflih, Ali Saeed Al-Mawee, Wassnaa Bzizi, Hanin University of Al-Zawiya Dept. Industrial Engineering Libya Trine University Indian United States Wayne Stata University Dept. Industrial Engineering MI United States Kalamazoo College Dept. of Computer Science Kalamazoo United States King Khalid University Dept. Industrial Engineering Saudi Arabia Western Michigan University Dept. of Computer Science Department of Computer Science KalamazooMI United States Western Michigan University Department of Biomedical Science KalamazooMI United States

ISBN: (纸本)9781665493765

Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80% compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features. © 2023 IEEE.

关键词： Diagnosis

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Developing Enhanced Conversational Agents for Social Virtual Worlds

arXiv

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arXiv 2025年

作者： Griol, David Sanchis, Araceli Molina, José Manuel Callejas, Zoraida Dept. of Computer Science Universidad Carlos III de Madrid Spain Dept. of Languages and Computer Systems University of Granada Spain

In this paper, we present a statistical model for spoken dialog segmentation that decides the current phase of the dialog by means of an automatic classification process. We have applied our proposal to three practical conversational systems acting in different domains. The results of the evaluation show that is possible to attain high accuracy rates in dialog segmentation when using different sources of information to represent the user input. The statistical model developed with human-machine dialog corpora has been applied to human-human conversations and provides a good baseline as well insights in the model limitation. © 2025, CC BY-NC-ND.

关键词： Chatbots

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