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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring BioClinical BERT’s NLP Capabilities with Explainability Techniques

Exploring BioClinical BERT’s NLP Capabilities with Explaina...

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Emerging Technologies in Computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Aditi Sankaranarayanan Diya Shetty Kirti Chetwani B R Shambhavi Dept. of Information Science and Engineering BMS College of Engineering Bengaluru India Dept. of CSE (Data Science) BMS College of Engineering Bengaluru India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Natural Language Processing has gained significant attention in the world of Information Technology and is experiencing rapid growth. Its applications span across various domains, with a notable surge in its utilization within the healthcare industry. This has fueled the development of language models that are trained on a large amount of clinical data, like Bio+Clinical BERT. However, there remains a crucial concern regarding the trustworthiness of NLP models. Traditional accuracy measures alone may not be sufficient to instill confidence in their reliability. In this study, we address this concern by exploring the application of Explainability to NLP tasks in the healthcare sector. We employ Explainability techniques to gain deeper insights into the underlying mechanisms and evaluate the trustworthiness of fine-tuned models based on Bio+Clinical BERT. Specifically, LIME and SHAP techniques have been utilized to produce explanations, which prove instrumental in enhancing our understanding of the models, as well as identifying their strengths and weaknesses.

关键词： Measurement Biological system modeling Computational modeling Medical services Predictive models Natural language processing data models

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Risk Estimation in a Markov Cost Process: Lower and Upper Bounds 41

Risk Estimation in a Markov Cost Process: Lower and Upper Bo...

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41st International Conference on Machine Learning, ICML 2024

作者： Thoppe, Gugan Prashanth, L.A. Bhat, Sanjay P. Bengaluru India Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ϵ-accuracy, either in expected or high-probability sense, requires at least Ω(1/ϵ2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs’ mean. Even in that case, our lower bound of Ω(1/ϵ2) improves upon the existing Ω(1/ϵ) bound (Metelli et al., 2023). Copyright 2024 by the author(s)

关键词： Cost estimating

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Real-Time Detection of Cattle Behavior Using YOLOv9 with AutoAugment and Mixup data Augmentation Techniques

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Transactions of the Korean Institute of Electrical Engineers 2024年第12期73卷 2326-2332页

作者： Lee, Su-Bin Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Dept. of Data Science Kangwon National University Korea Republic of Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

As the number of livestock farms in Korea decreases, the scale of individual farms has grown, necessitating advanced methods for efficient livestock management. Recent studies have increasingly applied computer vision and deep learning to monitor livestock behavior, health, and disease, aiming to enhance productivity and animal welfare. This study introduces a real-time cattle behavior detection system utilizing YOLOv9, a state-of-the-art object detection algorithm known for its high accuracy and efficiency. The YOLOv9-s model, optimized for real-time detection with minimal computational overhead, was employed. To further enhance detection accuracy, we incorporated AutoAugment, a data augmentation technique that automatically selects the optimal augmentation policies, and mixup, a method that improves model generalization by creating new training samples through linear interpolation of existing ones. The application of AutoAugment improved the mean Average Precision (mAP) from 0.926 to 0.941, while the addition of mixup raised the mAP to 0.947, representing a 2.1% performance increase. These results confirm the system's capability for accurate and efficient real-time detection of cattle behavior. Copyright © The Korean Institute of Electrical Engineers.

关键词： Livestock

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AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems

AI-Driven Energy Optimization: Household Power Consumption P...

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2024 International Conference on Microelectronics, ICM 2024

作者： Kolluru, Vinoth Kumar Challagundla, Yagnesh Chintakunta, Advaitha Naidu Roy, Bappadittya Bermak, Amine Renuka Devi, S.M. Stevens Institute of Technology Department of Data Science HobokenNJ United States University of Florida Engineering Education Dept. United States University of North Carolina Department of Computer Science and Engineering Charlotte United States School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India College of Science and Engineering Hamad Bin Khalifa Universityy Ar-Rayyan Qatar Dept. of Ece Hyderabad500104 India

ISBN: (纸本)9798350379396

Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) network models for estimating home power consumption using time-series data. The first model employs a standard LSTM technique with multivariate characteristics including global active power, voltage, and sub-metering variables. This technique strikes a balance between prediction accuracy and computing economy, making it ideal for resource-constrained applications. The second model provides an optimized version of the LSTM, which uses PyTorch and Ray Tune for hyperparameter optimization. The optimization focuses on tweaking learning rates, batch sizes, and LSTM layers to improve model accuracy and convergence speed. Using hyperparameter tuning, the Mean Squared Error (MSE) for global active power forecasts is decreased to 0.0018, proving its appropriateness for low-resource IoT systems. Both models are tested on a resampled real-world household electric power consumption dataset for effective training. The study emphasizes the advantages of multivariate time-series analysis and hyperparameter optimization, demonstrating that the optimized LSTM model can accurately predict energy consumption, enhance energy management in smart homes, and lower computing costs. Future work will look into combining more IoT data streams and real-world deployment for further improvement. The findings add to the expanding body of knowledge about energy optimization in IoT environments, addressing the crucial demand for effective, real-time energy management systems. © 2024 IEEE.

关键词： Mean square error

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Analysis of route efficiency in city bus transportation vulnerable areas

Analysis of route efficiency in city bus transportation vuln...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Yoon, Namsik Choi, Innjun Kang, Sung Woo Inha University Dept. of Industrial Engineering Incheon Korea Republic of Inha University Dept. of Industrial Engineering&Data Science Incheon Korea Republic of

ISBN: (纸本)9781665480451

City buses are important transportation for citizens who especially living in transportation vulnerable areas. However, public transportation in these areas continuously decreases because of low population density. The low demand puts these areas behind service priorities, The backlog of service priorities exacerbates public transportation, hereby decreasing the population in these *** order to solve the deterioration of public transportation services in transportation vulnerable areas, the optimal route is calculated through the genetic algorithm-based approaches. The methodology automatically creates novel bus routes based on the demands and existing transportation resources. In this paper, each route is regarded as a living organism, and both demand networks and road networks are constructed as an evolutionary environment so that each organism can compete and evolve with each other. In order to verify the methodology, the case study is conducted based on actual data-sets from local government, bus companies, telecommunication company(SK Telecom) in Incheon, Republic of Korea. The second largest island (Yeongjongdo) in Incheon is selected as a transportation vulnerable area by the Incheon institute (public research center for Incheon metropolitan city). The optimal route from the methodology increases the route efficiency without supplementing additional buses to handle the demands, hereby improving the vicious cycle of transportation vulnerable areas. © 2022 IEEE.

关键词： Genetic algorithms

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Application of Convolutional Neural Networks for Cervical Cancer Detection in Women's Uterus 2

Application of Convolutional Neural Networks for Cervical Ca...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Ramesh, Janjhyam Venkata Naga Visalakshi, J. Kishore, Ganjikuntla Uday Samuel, John Neerugatti, Vikram Yashika, S. Graphic Era Hill University Department of CSE Uttarakhand India CMR Institute of Technology Dept. of CSE -AIML Telngana India CMR College of Engineering & Technology Dept. of CSE-Data Science Telngana India MLR Institute of Technology Department of CSE Telngana India Dept. of CSE Banglore India

ISBN: (纸本)9798350367461

HPV infection is frequently the cause of cervical cancer, the fourth most prevalent type of cancer that mostly affects women's cervixes. Early vaccination of women may be the key to its eradication, but this effort is fraught with difficulties, particularly in middle-class or lower-income nations where access to healthcare is scarce. In this paper, a novel method for detecting cervical cancer using medical image processing techniques and Convolutional Neural Networks (CNNs) is presented. A serious health risk is cervical cancer, especially in areas with poor access to medical facilities. By using CNNs to analyze medical pictures, such as pap smears and cervical scans, the suggested approach seeks to overcome this difficulty by enabling the early identification of abnormalities in the cervical region that may be signs of cancer or precancerous lesions. data collection, preprocessing, segmentation, feature extraction, model training, and evaluation are all steps in the workflow. The CNN model shows promising performance in reliably diagnosing cervical cancer by automatic feature extraction from labelled datasets. The efficacy of the suggested strategy is confirmed by evaluation measures including area under the ROC curve, sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Cervical cancer convolutional neural network diagnosis medical image processing pap smear

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Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Philip Paul Arunodhayam, T. Vadde, Akhil Raj Kumar, Thimmampali Prasanna Silas, Salaja Gandu, Shalem Preetham Harshith Kumar, Chamarti Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India College of Engineering and Computing George Mason University Data Analytics Engineering VA Fairfax22030 United States

ISBN: (纸本)9798350306927

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks. © 2023 IEEE.

关键词： Energy efficiency

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inQuery- Online AI Application Formatting and Management System

inQuery- Online AI Application Formatting and Management Sys...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Aishwarya Singh Smriti Tejjus S. Bhat Uditi Sharma Yash Sharma CSE (Data Science) ABES Engineering College Ghaziabad India Dept. of CSE (Data Science) ABES Engineering College Ghaziabad India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Written applications, with standardized fields like date, subject, sender and receiver, serve various purposes such as leave requests, misconduct appeals and suggestions. Surprisingly, even in today’s tech-driven world, these applications often remain paper-based, leading to repetitive tasks like leave requests and complaints, which can be time-consuming and inefficient. Managing paper applications also poses challenges like misplacement and physical storage *** methodology addresses this by designing the application’s UI in Figma, exporting assets to Android Studio and creating a new project. In Android Studio, we implement the UI and functionality using these design assets, integrating Firebase services for authentication, real-time database and storage. Artificial Intelligence will be used for formatting the applications. We then test the application and ensure ongoing maintenance and *** observations reveal that many institutions still rely on traditional application methods. We recommend institutions adopt online application writing and management systems, as demonstrated by our Android app, “inQuery: An Application for Applications,” simplifying and enhancing this process.

关键词： Generative AI Education Receivers Writing Real-time systems Filling Safety

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