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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Kumar, M Vinod Ramesh, G.P. Pareek, Piyush Kumar Deepak, H.A. Babu, J. Ananda St. Peter's Institute of Higher Education and Research Department of Electronics and Communication Engineering Chennai India Nitte Meenakshi Institute of Technology Department of AI & ML Bengaluru India Navkis College of Engineering Department of ECE Hassan India Malnad College of Engineering Department of Information Science and Engineering Hassan India

ISBN: (纸本)9798350323795

Facial recognition is one example of the ways in which computer vision is being applied in modern, people-oriented utility apps. Facial recognition technology has improved in recent years, but it still lags behind other biometric authentication methods such as fingerprint and iris scanning or Radio Frequency Identification (RFID) cards in terms of accuracy. However, it is still frequently utilised due to the fact that the recognition procedure does not involve touching the device in any way. This research takes advantage of advances in facial recognition technology to offer an embedded device integrated ai and IoT-based automated attendance solution for use in smart classrooms. This study uses the ResNet architecture to explore a deep learning classification method for identifying student attendance. In this research, we used facial photos from students to train ResNet-18 and ResNet-50. Models educated to differentiate regular from irregular student attendance. Our photos were evaluated on 20%, 25%, and 40% of simulated and real-world datasets, respectively. Results from three types of testing data show that ResNet-50 outperforms ResNet-18 in rapports of accuracy, F1-measure, and mean accuracy value. This study makes use of the FER2013 dataset for labelling faces, as well as real-time student faces. This study uses a deep learning approach to show how dependable and repeatable student image analysis © 2023 IEEE.

关键词： Students

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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Implementation of Discrete HMMs for Isolated Spoken Word Recognition

Implementation of Discrete HMMs for Isolated Spoken Word Rec...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Ramachandrula, Pranav Sruthi, S. Jaswanth, Kunisetty Palaniswamy, Suja Sitaram, R.N.V. Amrita Vishwa Vidyapeetham Amrita School of Computing Department. of Computer Science and Engineering Bengaluru India 247.ai Bengaluru India

ISBN: (纸本)9798350311204

Isolated spoken words are acoustically separated from other spoken words by moments of silence. In this work, we have implemented Discrete Hidden Markov Models (DHMMs) for Isolated Spoken Words Recognition. Discrete HMMs have been successfully used in many temporal pattern recognition problems like speech recognition, handwriting recognition, gesture recognition, etc., for more than four decades. We have implemented a basic HMM from scratch in Java language and applied it to train and recognize isolated spoken words. We have tested our implementation in recognizing ten digits on a custom built speaker database. We have obtained an accuracy of 95 % and 82.5% for training and testing respectively. © 2023 IEEE.

关键词： Hidden Markov models

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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The Application of Ethernet and its Network Software in A computer Control System 2

The Application of Ethernet and its Network Software in A Co...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Pan, Dongying Wang, Chunhua Ju, Haoxue Pareek, Piyush Kumar Shandong Institute of Commerce and Technology Jinan China Dept. of Computer Science Engineering AI-ML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9781665491112

This paper, with the help of engineering examples to express the application of Ethernet in computer control system, proves that Ethernet has good reliability, then studies the principle of Ethernet local area network in transmission control, and finally introduces the specific application of DECNET network software for reference. © 2022 IEEE.

关键词： Ethernet

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Cryptographic Protocols for Securing Internet of Things (IoT) 15

Cryptographic Protocols for Securing Internet of Things (IoT...

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

作者： Manimekala, B. Aravind, T. Sounderrajan, T. Rajakumareswaran, V. Kondreddi, Krishnaveni Mohanambal, K. Christ University Department of Computer Science Yeshwanthpur Bengaluru India Vel TecRangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Cse School of Computing India Erode Sengunthar Engineering College Department of Ai and Data Science Tamil Nadu Thuduppathi India Erode Sengunthar Engineering College Department of Computer Science and Design Tamil Nadu Thuduppathi India Chaitanya Bharathi Institute of Technology Electrical and Electronics Engineering Ranga Reddy Telangana Gandipet500075 India Bannariamman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350370249

Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. © 2024 IEEE.

关键词： Authentication Communications Cryptographic EverDeveloping Gadgets Protocols

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ml-Based Financial Forecasting in ERP: Improving Budgeting Accuracy with Long Short-Term Memory Networks

ML-Based Financial Forecasting in ERP: Improving Budgeting A...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Al-Attabi, Kassem Rethik, Kumar Ranjusha, J.P. Sindhuja, N. Yadav, Sanjay Shivakalyan, B. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Engineering College Department of Computer Science & Engineering Surampalem India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This research examines the feasibility of using LSTM networks for financial prediction in frameworks such as the ERP system with an objective of enhancing budgetary prediction. Simple forecasting techniques do not take into account certain dynamics within the data that are non-linear, hence result in small forecast accuracy. Autoencoders can be employed to extract such features and LSTM networks capable of handling sequential data and long term dependencies could be used to anticipate the occurrence of emergencies. The process includes data culling that is composed of extensive datasets, data pre-processing, LSTM model development and training, and ERP system implementation. The findings illustrate that by extending the number of timesteps in LSTM models, it offers more reliability compared to conventional methods that help to improve decision-making and forecasting in finance. It is possible to upgrade them from time to time and make the models as accurate and as current as possible. These specific findings corroborate the overall proposition that discrete advancements in ml algorithms can converge to radically reshape a key area of ERP systems - financial forecasting. © 2024 IEEE.

关键词： Data accuracy

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GRAPHTRaiL: translating GNN predictions into human-interpretable logical rules 24

GRAPHTRAIL: translating GNN predictions into human-interpret...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Burouj Armgaan Manthan Dalmia Sourav Medya Sayan Ranu Department of Computer Science & Engineering IIT Delhi India Department of Computer Science University of Illinois Chicago Department of Computer Science & Engineering and Yardi School of AI IIT Delhi India

ISBN: (纸本)9798331314385

Instance-level explanation of graph neural networks (GNNS) is a well-studied area. These explainers, however, only explain an instance (e.g., a graph) and fail to uncover the combinatorial reasoning learned by a GNN from the training data towards making its predictions. In this work, we introduce GRAPHTRaiL, the first end-to-end, post-hoc, global GNN explainer that translates the functioning of a black-box GNN model to a boolean formula over the (sub)graph-level concepts without relying on local explainers. GRAPHTRaiL is unique in automatically mining the discriminative subgraph-level concepts using Shapley values. Subsequently, the GNN predictions are mapped to a human-interpretable boolean formula over these concepts through symbolic regression. Extensive experiments across diverse datasets and GNN architectures demonstrate significant improvement over existing global explainers in mapping GNN predictions to faithful logical formulae. The robust and accurate performance of GRAPHTRaiL makes it invaluable for improving GNNs and facilitates adoption in domains with strict transparency requirements.

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Predictive Analysis Using Modified Inception Net and Support Vector Machine for Forecasting Copra Yield in Agriculture 3

Predictive Analysis Using Modified Inception Net and Support...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, M.G. Raviprakash, M.L. Al-Attabi, Kassem Sathyavani, B. Sundaram, S. Meenakshi Kalpataru Institute Of Technology Department Of Computer Science And Engineering Tiptur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

One of the major economic sectors in various states is copra agriculture, with a significant portion of its global production coming from countries such as India, Indonesia, and Sri Lanka. However, the severity of copra issues can vary depending on factors such as moisture levels, fungal presence, and the quality of the copra, leading to imbalanced data and potential overfitting in copra image analysis. Threshold-based segmentation is involved to identify different segments of copra based on factors like moisture content. Higher moisture content may indicate areas prone to fungal growth or poor drying conditions, while segments with uniform colour and texture may signify high-quality copra. The proposed approach involves using the Modified Inception Net (MIN) for feature extraction and Support Vector Machine (SVM) for hyperparameter tuning. SVM handles the kernel function in nonlinear and high-dimensional spaces, enhancing its capabilities and mitigating overfitting. However, the MIN-SVM combination may require significant computational resources, particularly during training and parameter tuning. The proposed MIN-SVM method achieves promising performance metrics, including a Mean Absolute Error (MAE) of 0.071, Mean Square Error (MSE) of 0.0125, Matthews Correlation Coefficient (MCC) of 0.620 and R2 value of 0.9410. This performance is compared to existing methods such as Back Propagation Algorithm (BPA), K-means and SVM. © 2024 IEEE.

关键词： Support vector machines

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Skeletal Fragility Detection using Deep Learning

Skeletal Fragility Detection using Deep Learning

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Rama Krishna Reddy V. Sravya Reddy V. Sriram Sathvik N. Muthukumaran Shaik Mahaboob Subhani Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In the field of diagnosing health there has been a shift, in focus from simply identifying bone fractures to a broader evaluation of skeletal fragility. Skeletal fragility involves not detecting fractures but also assessing the health and resilience of the skeletal system, which is crucial for preventing future fractures. While an automated network (DNN) model has achieved impressive accuracy of 92.44% in distinguishing between healthy and fractured bones iti s important to consider additional factors when measuring skeletal fragility. These supplementary factors include bone density, composition, and comprehensive markers of bone health like vitamin D levels. To create a framework for assessing skeletal fragility and not just bone fractures it is necessary to expand the model’s scope by incorporating these essential additional factors and indicators. This holistic approach has the potential to provide a nuanced perspective, on bone health and the likelihood of future fractures. Ultimately this can significantly improve care by facilitating measures.

关键词： Deep learning Image processing Noise Surgery Manuals Bones Data augmentation

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