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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu Bhat, Niyati Kanna, B. Rajesh Rajalakshmi, S. Jeyavathana, R. Beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 IEEE.

关键词： Time series analysis

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning 53

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Wasi, Sefatul Alam, Saadia Binte Rahman, Rashedur Amin, M Ashraful Kobashi, Syoji Independent University Center for Computational & Data Sciences Department of Computer Science and Engineering Dhaka Bangladesh1299 Bangladesh University of Hyogo Graduate School of Engineering 2167 Shosha Hyogo Himeji Japan

ISBN: (纸本)9781665464161

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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On the robustness of median sampling in noisy evolutionary optimization

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science China(Information sciences) 2021年第5期64卷 32-44页

作者： Chao BIAN Chao QIAN Yang YU Ke TANG State Key Laboratory for Novel Software Technology Nanjing University Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and EngineeringSouthern University of Science and Technology

Evolutionary algorithms(EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective evaluations are usually inaccurate, because noise is almost inevitable in real world, and it is a crucial issue to weaken the negative effect caused by noise. Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value. In this work, we introduce a novel sampling method, median sampling, into EAs, and illustrate its properties and usefulness theoretically by solving OneMax, the problem of maximizing the number of 1 s in a bit string. Instead of the mean, median sampling employs the median of the evaluation results as an estimate. Through rigorous theoretical analysis on OneMax under the commonly used onebit noise, we show that median sampling reduces the expected runtime exponentially. Next, through two special noise models, we show that when the 2-quantile of the noisy fitness increases with the true fitness, median sampling can be better than mean sampling; otherwise,it may fail and mean sampling can be better. The results may guide us to employ median sampling properly in practical applications.

关键词： evolutionary algorithms noisy optimization median sampling computational complexity runtime analysis

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Proof of Activity Protocol for IoMT Data Security

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computer Systems science & engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

Development of Phylum Chordata Sound Recognition System usin...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Eshilokun, Oluwafikunmi Adegbenga Oladejo, Sunday Oladayo Ekwe, Stephen Obono Cset University of South Africa Departments of Computer Science and Electrical Engineering Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Cape Peninsula University of Technology Department of Electrical Electronic and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798350327816

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%. © 2023 IEEE.

关键词： Catfish Deep Learning Gender Prediction Machine Learning Audio Data Analysis Sound Recognition

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Evaluating AI-Based Deep Learning Models for Enhancing English Reading Comprehension Skills in Students 8

Evaluating AI-Based Deep Learning Models for Enhancing Engli...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nimma, Divya Arunadevi, V. Manu, Mehul Selvi, V. Prema, S. University of Southern Mississippi Ummc Computational Science United States Dr. Mahalingam College of Engineering and Technology Department of Science and Humanities Pollachi India Bhimtal India Graphic Era Deemed to be University Uttarakhand Dehradun India Mother Teresa Women's University Department of Computer Science Kodaikanal India Panimalar Engineering College Department of English Chennai India

ISBN: (纸本)9798350376425

In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models increases, enhancing the current systems' shortcomings becomes crucial. There are difficulties in existing models precisely due to the fundamental problem of striking a balance between the level of detail achievable and the amount of computational resources that a model requires for accurate estimation in practical settings. Current models, present several problems including but not limited to overfitting, average performance on out-of-sample data and less applicability for subtle linguistic features. Such obstacles limit their capability to execute in real conditions, where high degree of accuracy and precision is paramount. To overcome these challenges, this study introduces a novel framework that employs the BERT model which was developed to capture bidirectional context and achieve greater depth of the semantic relations between words. The proposed approach is designed to improve the model's effectiveness to a considerable extent through the use of mechanisms such as attention as well as transformer architecture included in BERT. The proposed BERT model demonstrates exceptional results, achieving an accuracy of 99.10% and a precision of 98.30%. These metrics reflect the model's superior ability to understand and generate accurate language representations, surpassing existing models in both effectiveness and efficiency. This advancement underscores the potential of BERT to address critical challenges in NLP, offering a promising solution for applications requiring high precision and robust language comprehension. © 2024 IEEE.

关键词： Semantics

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An Enhanced Energy Efficient Clustering for Biomedical Wireless Sensor Networks

An Enhanced Energy Efficient Clustering for Biomedical Wirel...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Vennira Selvi, G. Hl, Chandrakala Sheeja Kumari, V. Ponmaniraj, S. Anbazhagan, K. Nanthini, S. Presidency University Rajanukunte Yelahanka School of Information Science Bengaluru India Presidency University Rajanukunte Yelahanka School of Computer Science and Engineering Bengaluru India Saveetha School of Engineering Simats Department of Computational Intelligence Chennai India Saveetha School of Engineering Simats Department of Data Science Chennai India

ISBN: (纸本)9798350317060

Biomedical Wireless Sensor Networks (BWSN) is major technique for Health Care applications for providing quality of life with minimum cost. To enforce the quality of medical care provided to the citizen, such networks should be integrated with existing network infrastructures. The requirement of quality of service should be concentrated on development stages and are monitored for the duration of network operation. The life time of the network is most relevant feature for ensuring the quality of medical care requirements. In order to increases the network lifetime, maintaining the remaining energy of each sensor node with reliable communication link throughout the network. An energy aware clustering for biomedical wireless sensor network (EACBWSN) is proposed for increasing the network lifetime by minimizing the energy consumption among sensor nodes and provides a reliable communication link between sensor nodes. The proposed algorithm efficiently prolong the lifetime of the network when compared with existing algorithm. © 2023 IEEE.

关键词： Sensor nodes

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PocketLG: Protein Binding Pocket Identification Using Protein Language Model and Graph Transformer

PocketLG: Protein Binding Pocket Identification Using Protei...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Li Chang, Heng Sun, Zehua Li, Wenjie Zhu, Li Wang, Han Changchun University of Technology College of Computer Science and Engineering Changchun China Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China General Hospital Ningxia Medical University The Radiology Department Ningxia China

ISBN: (纸本)9798350386226

Protein pocket is a special region on the surface of a protein that commonly interacts with other molecules, especially small molecule compounds. Accurately identifying and understanding the structure character of protein pockets, that can accelerate the development of new drugs, as well improve existing drugs to reveal disease mechanisms. In this study, we propose a deep learning method named PocketLG, to identify the potential binding pockets integrating a Protein Language model and Graph Transformer. The method constructs the pocket identification task as a regression problem, which can more accurately identify the real pocket boundaries. In addition, We propose a data sampling strategy and construct a pipeline to support the data sampling effort. This strategy has the benefit of augmenting the data, allowing the model to better learn the intrinsic characteristics of the pocket structure, even for pockets with unknown boundaries This method can sample the unknown pocket by dividing different sample sizes and then determine the boundary of the unknown pocket by the regression value, it can be a direct help for downstream tasks. Experimental results show that our deep learning model can identify binding pockets on proteins with 91 percent success rate. This work provides a new technical route for protein binding pocket prediction research, which can greatly contribute to the development of the pharmaceutical industry. PocketLG source code, pre-trained models and data preprocessing code are available at https://***/NENUBioCompute/PocketLG. © 2024 IEEE.

关键词： Deep Learning Drug Discovery Protein pockets

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Image-Based Extraction of Prescription Information using OCR-Tesseract 2

Image-Based Extraction of Prescription Information using OCR...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ponnuru, Mahesh Ponmalar, Sridevi P. Likhitha, A. Sree, Tanu B. Chaitanya, Guna G. School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India School of Computer Science Vellore Institute of Technology Amaravathi522237 India

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. © 2024 Elsevier B.V.. All rights reserved.

关键词： Optical character recognition

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