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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sujatha, E. Shanthasheela, S. Gopirajan, P.V. Department of Computer Science and Engineering Thandalam Chennai602105 India Velammal Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350336009

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques. © 2023 IEEE.

关键词： Recommender systems

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Using Video Games as a Tool in Math Education

Using Video Games as a Tool in Math Education

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Omirgaliyev, Ruslan Mukhidenov, Diyar Sydyk, Ayazhan Astana It University Department of Computer Engineering Astana Kazakhstan Astana It University Department of Computer Engineering Astana201437 Kazakhstan Astana It University Department of Computational and Data Science Astana201078 Kazakhstan

ISBN: (纸本)9798350335040

Today, you can see how children are increasingly spending their free time playing desktop or mobile video games. The question then becomes whether a simple hobby can be turned into a rewarding learning pastime? In the introduction of the article, the current situation and the problem that video games can create and the direction in which it can be solved using video games will be considered. The literature review considers some interesting aspects and conclusions of earlier similar studies. The methodology will present plans for quantitative and qualitative surveys of the population and a proposal for the development of a gaming product for educational purposes. The results will show what the situation is and what people's opinion about the use of video games in education. All the data obtained will help in the development of an educational project. Summing up, we will talk about the meaning and significance of the project and similar ideas in the modern world. © 2023 IEEE.

关键词： Human computer interaction

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Three-class Severity Detection and Classification of Brain Tumor ROI Using YOLO-v9 4

Three-class Severity Detection and Classification of Brain T...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Jain, Garima Jain, Sanat Vishwakarma, Harshlata Jayanthi, J. Devi, M. Anousouya Bramhe, Vijendra Singh School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India School of Computer Science and Engineering Vit Bhopal University Sehore India Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350389128

A vital part of diagnosing and treating medical issues is the use of medical imaging for the detection and categorization of brain tumors. For this research, we looked into how well the YOLOv9 deep learning architecture identified ROIs and classified brain tumors using a dataset consisting of nine thousand annotated images. One of the main aim of our work was to find the region of interest (ROI) in brain scans as precisely as possible so that cancers could be located. The second goal was to use the ROIs to categorize the tumors as meningioma, pituitary, or glioma. Mean average precision (mAP) for all tumor classes is 0.85, and for meningioma brain tumors particularly it is 0.949, indicating competitive accuracy metrics for the model. In this trained the network with weights from a generic object identification task before initializing it with YOLOv9 architecture-based transfer learning techniques. After that, adjusted the model based on the unique features of medical imaging by fine-tuning it using our dataset of brain tumors. The significant mAP scores, especially for meningioma tumors, highlight the potential of this deep learning approach to improve clinical workflows in radiology and neurology. This study contributes to the growing area of deep learning applications. © 2024 IEEE.

关键词： Convolutional neural networks

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IoDseC++: authenticated key exchange protocol for cloud-enable internet of drone communication

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9529-9542页

作者： Amin, Ruhul Jayaswal, Sakshita Sureshkumar, Venkatasamy Rathore, Balram Jha, Ananya Abdussami, Mohammad Department of Computer Science and Engineering IIIT Naya raipur Sector 24 Chhattisgarh Naya Raipur493661 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamilnadu Coimbatore641004 India

In recent times, sensor node or smart camera can be embedded in the drones for the collection of important data with the help of Internet Technology. Several security threats/vulnerabilities may hamper the system while collecting data. Hence, strong protection against security attacks is essential in drone based data collection system. In this work, we have considered a drone based cloud enable architecture for collecting and sharing information. The aim of this paper is to design a lightweight security protocol which will provide secure communication and mutual authentication among the entities. The proposed protocol establishes temporary session key for each session in order to make the complete system security attacks free. We have simulated the proposed protocol for measuring security strength using Scyther simulation whose results show all the claimed parameters are private during protocol run. It also ensures the correctness of mutual authentication. The informal analysis confirms strong protection on all security threats related to proposed architecture. The performance analysis has also made which shows that the protocol is lightweight and achieves better efficiency in comparison with related published works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Drones

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Hybrid Deep Learning Models with Genetic Algorithm Optimization for Enhanced Kidney Tumor Detection 5

Hybrid Deep Learning Models with Genetic Algorithm Optimizat...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajagopal, K. Kumari, V. Sheeja Saraswathy, S. Kumar, V. Suresh Ponmaniraj, S. Deepa, A. Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Chennai Thandalam India Saveetha Institute of Medical and Technical Sciences Simats University Department of Computer Science and Engineering Chennai Thandalam India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Institute of Ece Saveetha Institute of Medical and Technical Sciences Simats University Department of Computational Intelligence Chennai Thandalam India Saveetha Engineering College Department of Computer Science and Engineering Chennai Thandalam India

ISBN: (纸本)9798350379945

Accurate and timely segmentation of kidney tumors from medical images is crucial for effective treatment and prognosis. This research study proposes a novel hybrid deep learning framework incorporating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for enhanced kidney tumor segmentation. To optimize model performance, Genetic Algorithms (GA) are employed to fine-tune hyperparameters and network architecture. The proposed method effectively captures both local and global image features, leading to improved segmentation accuracy compared to existing techniques. Experimental results on a publicly available dataset demonstrate the superiority of our approach in terms of recall, F1-score, and precision. This research contributes to the advancement of computer-aided diagnosis for kidney cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

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computer Systems science & engineering 2023年第9期46卷 2933-2945页

作者： K.S.Bhuvaneshwari Ahmed Najat Ahmed Mehedi Masud Samah H.Alajmani Mohamed Abouhawwash Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Engineering Lebanese French UniversityErbil44001Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityPO Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

The immediate and quick spread of the coronavirus has become a life-threatening disease around the *** widespread illness has dramatically changed almost all sectors,moving from offline to online,resulting in a new normal lifestyle for *** impact of coronavirus is tremendous in the healthcare sector,which has experienced a decline in the first quarter of *** pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical *** current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification.A proposed multistage model helps to identify Covid from Computed Tomography(CT)scan *** first process uses a convolutional neural network(CNN)for extracting the feature from the CT *** Pearson coefficient filter method is applied to remove redundant and irrelevant ***,theGrey wolf optimizer is used to choose optimal *** analysis proves that this determines the optimal characteristics to detect the deadly *** proposed model’s accuracy is 14%higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image(SARS-CoV-2 CT)*** COVID CT image dataset is 22%higher than the existing krill herd and bacterial foraging optimization *** proposed techniques help to increase the classification accuracy of the algorithm in most cases,which marks the stability of the stated *** analysis reveals that the proposed classification technique to predict COVID-19 withmaximumaccuracy of 98%outperforms other competitive approaches.

关键词： COVID-19 feature selection CT image classification features extraction

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review Materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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A Grassmannian Manifold Self-Attention Network for Signal Classification 33

A Grassmannian Manifold Self-Attention Network for Signal Cl...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Wang, Rui Hu, Chen Chen, Ziheng Wu, Xiao-Jun Song, Xiaoning School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9781956792041

In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensions remains a major challenge, as these features have a submanifold structure. Linear subspace, the basic element of the Grassmannian manifold, has proven to be an effective manifold-valued feature descriptor in statistical representation. Besides, the Euclidean self-attention mechanism has shown great success in capturing long-range relationships of data. Inspired by these facts, we extend the self-attention mechanism to the Grassmannian manifold. Our framework can effectively characterize the spatiotemporal fluctuations of sequential data encoded in the Grassmannian manifold. Extensive experimental results on three benchmarking datasets (a drone recognition dataset and two EEG signal classification datasets) demonstrate the superiority of our method over the state-of-the-art. The code and supplementary material for this work can be found at https://***/ChenHu-ML/GDLNet. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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