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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sudar, K. Muthamil Nagaraj, P. Raghavendra, V. Jaashvitha, V. Jayasri, Y. Reddy, Yerva Dasaradha Ramanjaneya Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India

ISBN: (纸本)9798350361186

It has been proposed that physicians use machine learning-based lung cancer prediction algorithms to treat indeterminate pulmonary nodules that are discovered by accident or during screening. These methods might improve decisionmaking, decrease variability in nodule classification, and decrease the proportion of benign nodules that require unnecessary monitoring or investigation. In this work, we summarise the principal lung cancer prediction methods that have been put out so far and discuss some of the advantages and disadvantages of each. We discuss some of the challenges in creating and verifying such methods and outline the path to clinical acceptance. To ensure the best possible quality of input data for the deep learning models, the preprocessing step involves anatomical area segmentation, noise reduction, and image enhancement. The process of feature extraction involves using convolutional neural networks (CNNs) to extract intricate patterns and features from the lung pictures, which are then utilized to diagnose benign and malignant tumors. Typically, a deep neural network or an ensemble of models is used as the classification network to process this information and determine the final diagnosis. A faster and better outcome for patients may result from the recommended pulmonary cancer detection system's ability to assist medical professionals in accurately identifying malignant tumors in lung imaging. Deep learning is projected to be used in lung cancer detection, which will eventually result in earlier and more effective treatment, hence reducing the burden of this fatal condition. © 2024 IEEE.

关键词： Classification (of information)

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An Empirical Analysis and Overview of a Green 6-G Paradigms and its Various Implementation Applications 4

An Empirical Analysis and Overview of a Green 6-G Paradigms ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Rao, D.T.V. Dharmajee Sharma, Kuldeep Joshi, Sanjeev Kumar Rani, T Sudha Alsalami, Zaid Angadi, Sanjeevkumar Cmr Technical Campus Department of Cse Telangana Hyderabad India Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Uttaranchal Institute of Technology Uttaranchal University Department of Mechanical Engineering Dehradun248007 India Aditya Engineering College Department of Computer Science and Engineering Surampalem India The Islamic University Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

The advent of green 6G technologies holds promise for revolutionary improvements across different areas such as data transmission speed, latency, and traffic volume. Incorporating features like sub-terahertz communication and advanced multiple-input multiple-output (MIMO) technologies, Green 6 G networks aim to surpass the data transmission rates of 5 G while slashing latency to a mere fraction. However, this rapid expansion in network data transfer poses notable hurdles concerning data processing systems and energy usage. Despite strides in energy-efficient computing methods such as green cloud and edge computing, unresolved issues persist, including substantial energy consumption during data migration and idle server power usage. To tackle these challenges, this study proposes a novel strategy called 'In-Network Computing' (INC), which relocates processing tasks from hosts to network devices, leveraging idle resources to alleviate data transmission burdens and mitigate pressure on cloud computing resources. In contrast to edge computing, INC exhibits advantages in the 6 G landscape due to the enhanced computing capabilities and application awareness of network devices. The proposed approach prioritizes energy efficiency by minimizing data centre inputs and integrating host and network node computing environments through virtualization and software-based models. The paper introduces innovative implementations by implementing green 6 g paradigms, focusing on task scheduling and the creation of a prototype system utilizing low-power multicore processors. Experimental validation across diverse application scenarios demonstrates substantial reductions in system energy consumption and network traffic. © 2024 IEEE.

关键词： Data transfer

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Blockchain based prophecy of cardiovascular disease using modified XGBoost

Blockchain based prophecy of cardiovascular disease using mo...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Srivastava, Vibha Singh, Ashutosh Kumar Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj India

ISBN: (纸本)9781032669663

One of the global and complex disease known as Heart Disease (HD) plays a significant mantle in cardiology and healthcare. In recent times, the blockchain technology is widely used in medical sector for storing the medical records. Still there is a need for enhancing security range and the studies have failed to focus on both the security and HD prediction. Therefore, the proposed study introduced a secured Electronic Health Record (EHR) system over the blockchain environment by implementing encryption and decryption process using Advanced Encryption Standard (AES) algorithm and the HD prediction model is implemented with a Modified Empirical Loss (MEL) Function Extreme Gradient Boosting (XGBoost) classifier. The experimental evaluation is done by using Cleveland Dataset. The efficiency of preferred exemplary is determined with regard to performance metrics namely accuracy, encryption time, precision, decryption time, recall, and F1-score. © 2024 The Author(s).

关键词： Cryptography

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research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Enhancing Protein Structure Generation Through Deep Learning Techniques 3

Enhancing Protein Structure Generation Through Deep Learning...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Sri, Vattikuti Sowjya Nikitha, V. Reddy, Vennapusa Sujay Sai Vikram, M. Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798350361186

Protein structure prediction is a critical task in molecular biology, with far-reaching implications for innovation, disease understanding, and drug discovery. Deep learning is one successful way to deal with this difficult issue. The purpose of this work is to investigate how deep learning technologies can be used to increase the precision and effectiveness of protein structure formation. We examine numerous deep learning models and approaches in depth, emphasizing their advantages and disadvantages in terms of protein structure prediction. Our study includes a comparison of current deep learning-based approaches, demonstrating their usefulness on both benchmark datasets and real-world examples. We discuss how deep learning models are built by using inter-residue interactions, secondary structure prediction, and sequence data. In addition, we investigate how generative adversarial GANs could be utilized to improve protein structure formation by using complex data distributions. We show how deep learning may dramatically improve the precision of protein structure prediction, ultimately producing more dependable models, through extensive experiments and case studies. In addition, we discuss how deep learning models may be interpreted and applied to the field of protein folding, providing an understanding of both their strengths and weaknesses. © 2024 IEEE.

关键词： Deep neural networks

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Modified attention based cryptocurrency price presage with convolutional Bi-LSTM

Modified attention based cryptocurrency price presage with c...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Srivastava, Vibha Singh, Ashutosh Kumar Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj India

ISBN: (纸本)9781032669663

Predicting stock price is important for identifying the fluctuations in stock market which leads the investors to make profit. As predicting the future price tends to be less accurate in the existing studies, this study considers feature extraction process through the proposed Modified Attention with Convolutional Bi-LSTM for predicting the market value of next day. The stock datasets of Bitcoin and Ethereum are loaded and feature scaling method performs the data pre-processing operations. Further, pre-processed data are considered for feature extraction which is given as input into the classifiers of train and test split method. The comparison of suggested with the prevailing datasets are performed with the estimation of Mean Absolute Error, Mean Squared Error and Root Mean Squared Error and the outcome shows efficacy of suggested mechanism with reference to accuracy. © 2024 The Author(s).

关键词： Feature extraction

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Sentimental Analysis on Zomato Restaurant Reviews using Bi-LSTM

Sentimental Analysis on Zomato Restaurant Reviews using Bi-L...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepti Chaudhari Sanjeevkumar Angadi Saili Sable Uma Patil Dipamala Chaudhari Kavita Jadhav Computer Science and Engineering Nutan College of Engineering and Research Pune India

Sentiment analysis is a vital aspect of understanding public opinion and sentiment towards products and services. This paper presents a sentiment analysis work focused on Zomato restaurant reviews in Bangalore, aiming to classify restaurants into positive, negative, or neutral sentiment categories based on customer reviews. Bi-LSTM and Bi-GRU models are employed to capture contextual information in the sequential data of reviews. Additionally, sentiment analysis techniques, including Word2Vec, VADER, and Sentiment Intensity Analyzer, are integrated to enhance the sentiment classification process. Through rigorous experimentation, the performance of these models and techniques is evaluated. The proposed models demonstrate promising accuracy rates in sentiment classification. By enhancing and expanding the sentiment analysis framework, this paper contributes to a deeper understanding of public sentiment towards Zomato restaurants in Bangalore. The insights derived from this study can facilitate informed decision-making for both restaurant owners and customers, ultimately improving the dining experience and customer satisfaction with accuracy of 98.6%.

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Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

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Intelligent Automation & Soft Computing 2023年第2期35卷 1889-1906页

作者： D.Doreen Hephzibah Miriam Deepak Dahiya Nitin C.R.Rene Robin Computational Intelligence Research Foundation(CIRF) Chennai600023TamilnaduIndia College of Computer and Information Sciences Majmaah UniversityMajmaah11952Saudi Arabia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of CincinnatiCincinnati45221OHUnited States Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044TamilnaduIndia

Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in *** image secrecy is highly vulnerable to various types of *** a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data *** resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare *** blockchain algorithm preserves the security of the medical image by performing hash *** execution of this research is carried out by MATLAB *** suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound *** the simulation,94.9%of malicious communications were recognized and identiﬁed effectively,according to the total outcomes *** suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

关键词： Healthcare system cybersecurity security data sharing blockchain

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An Intelligent Blockchain-Oriented Digital Voting System Using NEAR Protocol

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SN computer science 2023年第5期4卷 643页

作者： Snehlata Shukla, Pallavi Singh, Ashutosh Kumar Tiwari, Saloni Rishabh Dwivedi, Vijay Kumar Department of Computer Science and Engineering United College of Engineering & Research UP Prayagraj 211010 India

According to today’s social environment, implementing a secure digital system is a challenging task;a traditional voting system (i.e., ballot system) does not offer ambiguity regarding the counting of electoral votes. Voting frauds may fail because of many reasons like pooling booth fraud, and fake voter IDs, so to solve this problem, we need a secured digital voting system, and the best solution implemented is Blockchain. This technique conquers all the problems of the traditional voting system. And this technology is used to provide a highly secure, decentralized, and provide P2P network to verify, process, and record all persons. This opens all possibilities to construct a secure network as well as a decentralized system that does not need third-party involvement. To the best of our knowledge, this is the first attempt to implement blockchain NEAR protocol for digital voting systems. In this paper, our main aim is to build a secure digital voting system based on this blockchain protocol which offers the privacy of a person, security, flexibility, and fairness of the current voting scheme. It also decreases the cost of hosting a countrywide election. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Digital voting NEAR protocol Security

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BAHBench: A Unified Benchmark for Evaluating Bio-Acoustic Health with Acoustic Foundation Models

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IEEE Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Xu, Weixiang Dong, Zhongren Peng, Jing Wang, Runming Zhang, Zixing Hunan Normal University School of Information Science and Engineering Changsha410081 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China

Acoustic foundation models, through self-supervised learning on large amounts of unlabeled speech data, can acquire rich acoustic representations. In recent years, these models have demonstrated substantial potential in audio-based health-related tasks, remarkably enhancing the efficiency and quality of healthcare services and contributing to the advancement of smart healthcare. However, there is currently a lack of systematic research and exploration on the performance of acoustic foundation models in health-related tasks. Furthermore, inconsistencies in evaluation methods and experimental setups hinder fair comparisons between different methods, severely impeding progress in this field. To address these challenges, we establish a unified Benchmark for evaluating Bio-Acoustic health via acoustic foundation models, namely BAHBench. BAHBench encompasses 6 distinct health-related tasks and evaluates 12 acoustic foundation models within a unified evaluation framework and parameter settings, enabling fair comparisons across different models. Our objective is to explore the effectiveness of current acoustic foundation models in health-related tasks. Thus, we discuss the impact of model size and data diversity on performance, and investigate feature selection and efficient fine-tuning strategy. Experimental results show that different health-related tasks benefit from features from different layers of the foundation model, while LoRA fine-tuning further enhances the model's performance on downstream tasks. Our goal is to provide clear and comprehensive guidance for future researchers. The code related to this study will be available to the research community to promote transparency and reproducibility. © 2013 IEEE.

关键词： Electronic health record

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