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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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Machine Learning-Based Sentiment Analysis of Twitter COVID-19 Vaccination Responses

Machine Learning-Based Sentiment Analysis of Twitter COVID-1...

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Shrivastava, Vishal Sudhanshu, Satish Chandra Department of Computer Science and Engineering Arya College of Engineering and IT Jaipur India

ISBN: (纸本)9789819955206

The COVID-19 pandemic has caused significant fear, anxiety, and complex emotions or feelings in a large number of people. A global vaccination campaign to end the SARS-CoV-2 epidemic is now in progress. People’s feelings have become more complex and varied since the introduction of vaccinations against coronavirus. The use of social media platforms such as Twitter enables users to communicate with one another and share information and perspectives on a wide variety of topics, spanning from local to international concerns, from global to personal. Twitter will prove to be a helpful source of information that can be tracked regarding views and sentiments regarding the SARS-CoV-2 vaccination. To better understand public views, concerns, and emotions that may influence the achievement of herd immunity targets and limit the pandemic’s impact, this study uses deep learning to identify the themes and sentiments in the public about COVID-19 immunization on Twitter. Moreover, this paper consists of a detailed explanation of the sentiment analysis with their challenges, classification, approaches, applications, and VADER. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： COVID-19

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Exploring Sentiment in Tweets: An Ordinal Regression Analysis

Exploring Sentiment in Tweets: An Ordinal Regression Analysi...

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Shrivastava, Vishal Dolly Department of Computer Science and Engineering Arya College of Engineering and IT Jaipur India

ISBN: (纸本)9789819955206

The fundamental goal of sentiment analysis is to find and categorize any views or feelings that are communicated in a text. Nowadays, discussing thoughts and expressing feelings through social networking sites is widespread. Consequently, a vast amount of data is generated every day, which can be mined successfully to extract valuable information. Performing sentiment analysis on such data can be useful for producing an aggregated view of particular products. Due to the prevalence of slang and misspellings, sentiment analysis on Twitter is frequently a challenging undertaking. Additionally, we are constantly exposed to new terms, which makes it more difficult to assess and compute the sentiment compared to traditional sentiment analysis. Twitter limits a tweet's length to 140 characters. Consequently, obtaining important information from brief messages is another obstacle. Knowledge-based approaches and machine learning can significantly contribute to the sentiment analysis of tweets. The amount of data produced by people, i.e., users of a certain social site, is growing exponentially as a result of changing behavior of various types of networking sites like Snapchat, Instagram, Twitter, etc. The purpose of this paper is to determine the emotions underlying these posts. We have decided to use Twitter as our platform for this. In this study, we investigate the views expressed by Twitter users concerning certain companies. By computing a basic sentiment score and then categorizing them as positive or negative, the corporation would be provided with critical feedback about its products from individuals around the world. The proposed LSTM model has proved to be 93% efficient in comparison with previous models which were accurate up to 86%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Sentiment analysis

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Diagnosis of Dementia Using Eye Movements 2

Diagnosis of Dementia Using Eye Movements

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Khuzaima Hifsa, N. Krishna, S. Shivakumar, N. Shetty, Swati S. Madhu, C.K. Department of Computer Science and Engineering Malnad College of Engineering Hassan India

ISBN: (纸本)9798350383867

A wide spread neurological condition called dementia is characterized by problems with day-to-day functioning, memoryloss, and cognitive ***'s diagnostic techniques usually rely too much on subjective evaluations, leading to unreliable results. In order to solve this issue, this abstract proposes a comparative analysis approach that combines eye movement research and machine *** methods combined with state-of-the-art machine learning models such as Random Forest, Decision Tree, Naive Bayes and Support Vector Machines (SVM), Gradient Boost and Logistic Regression can identify distinct patterns of eye movements associated with *** methodology offers a significant opportunity to increase the accuracy and efficiency of dementia detection using machine learning algorithms. Non-invasive techniques like tracking eye movements support early intervention efforts and provide valuable in sights in to cognitive *** innovative strategy may improve patient outcomes and assistance. © 2024 IEEE.

关键词： Logistic regression

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Combating Fake News in the Digital Age: A Review of AI-Based Approaches 9

Combating Fake News in the Digital Age: A Review of AI-Based...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Vishnupriya, G. Allen Jeriel, K. Anish, R.N.S. Ajay, Gnana Alan Giftson, J. Easwari Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350394474

The increasing spread of false information requires advanced detection methods. This study provides a thorough examination of the various strategies used to identify fake news, ranging from traditional approaches to cutting-edge technologies. The strengths and limitations of artificial intelligence, deep learning, and natural language processing techniques were evaluated. The combination of multimodal analysis, artificial intelligence and graph-based models has been identified as an emerging trend that improves the effectiveness of classification systems. Challenges, such as adversarial attacks and biased training data, highlight the ongoing need for development in this area. Additionally, this paper explores new trends, including user behavior analysis and collaborative cybersecurity strategies, while discussing their potential impact on detecting counterfeit news. A comparative analysis was used to assess the suitability of these methodologies across diverse contexts. The conclusion of the review emphasizes the dynamic nature of detecting false information and underlines the crucial requirement for interdisciplinary cooperation to safeguard against deception in the constantly evolving digital realm. © 2024 IEEE.

关键词： Behavioral research

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Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm 1

Predictive Analysis of Liver Disease Using Random Forest Mac...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vetrithangam, D. Kumar, Nallabariki Praveen Arya, Neha Tiwari, Amrita Raju, G. Jaya Srinivas, Gollapalli Veera Satya Chandigarh University Department of Computer Science & Engineering Mohali India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Aditya College of Engineering and Technology Department of Mechanical Engineering Surampalem India

ISBN: (纸本)9798350376517

The proposed system's objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient's health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model's results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component's error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups - the healthcare organizations - so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients' outcomes and make more efficient use of the health care facilities. © 2024 IEEE.

关键词： Contrastive Learning

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A cloud based interactive framework for emergency medical data sharing 4th

A cloud based interactive framework for emergency medical da...

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4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Taranum, Ayesha Manishankar, S. Padmaja, K. Balarengadurai, C. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysore India Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Kodakkara Thrissur India Department of Computer Science and Engineering GSSS Institue of Engineering and Technology for Women Karnataka Mysore India

ISBN: (纸本)9781032426853

Pandemic bringing a change in the medical system and medical infrastructure. This requires a complete revamping of medical data collections and storage. In such a scenario there has to be a system which enables the efficient transport of a patient to a distant hospital with minimal human support. In such a remote telehealth service, the inclusion of a system which is capable of sending and receiving medical data like live image of patients, X-Rays and multimedia images would result in better and fast pre-hospitalization. In such an emergency situation, the information has to be exchanged between the ambulance and the doctor in the least time possible. An efficient image compression algorithm will ensure the faster communication. The proposed framework is a combination of mobile application an interactive OpenTok API, cloud based platform Heroku and Firebase. The proposed system ensures a dedicated high speed data sharing environment based on cloud and mobile API to make an interactive framework for Healthcare Industry. © 2024 The Author(s).

关键词： Image compression

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