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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Geetika Sarda, Moksh Malik, Nikhil Singh, Harnoor Mishra, Rahul Grover, Somil Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032911571

Brain tumors are either cancerous or noncancerous masses of abnormal brain cells. Meningioma tumor, glioma tumor, and pituitary tumor are the three forms of brain cancers that are examined in this research. One essential diagnostic technique for the identification and diagnosis of brain tumors is brain tumor segmentation. Although manual brain tumor segmentation using MRI is possible, the accuracy and identification are not very good. The classification of abnormalities is a time-consuming task for physicians because it is not predictable and straightforward. However, tumor detection is difficult because tumors have complex appearances and boundary characteristics. In this work, we report on an automated method for detecting and diagnosing brain tumors from MRI images using EfficientNet. Specifically, we developed a preliminary report generation system that automatically provides the type of tumor, its corresponding ICD code, the tumor’s diameter and area, and the original input image with the tumor highlighted for visualization. This will be accomplished through the classification and segmentation of MRI images, with classification using Dense EfficinetNet to identify the tumor type and ICD code and segmentation using EfficientNet UNet to visualize the tumor and determine its diameter and area. A preliminary report for our paper is being created, which will use one MRI image of a brain tumor to diagnose tumor diameter and tumor area. © 2025 the Author(s).

关键词： Magnetic resonance imaging

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Personality Prediction Based on Contextual Feature Embedding SBERT

Personality Prediction Based on Contextual Feature Embedding...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Akber, Md Ali Ferdousi, Tahira Ahmed, Rasel Asfara, Risha Rab, Raqeebir Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665482585

Personality prediction defines an individual's interior self and provides an overview of their behavioral characteristics. Individuals can develop personally and professionally with its aid. Since its inception, the MBTI has become one of the most valuable instruments available due to its widespread application in a variety of fields. Typically, psychologists use questionnaires or conduct interviews with subjects to make predictions. However, because it is only a question-and-answer session, it is prone to error. In this paper, an implicit model is suggested in order to optimize the process using machine learning. The primary objective of this paper is to use sentence transformers to discern the context of user-written social media posts in order to automate the process. In our proposed work, various text pre-processing techniques, such as data cleansing, stopword removal, and data balancing techniques such as random oversampling, are utilized. The context of the text posts is determined using Sentence-BERT (SBERT), a pre-trained model created especially for sentence-level embeddings. Using the Myers-Briggs Type Indicator (MBTI) and a variety of machine learning techniques, such as Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN) and Random Forest (RF) Classifier, it is possible to predict people's personalities based on text. SBERT combined with the Random Forest Classifier performs outstandingly to predict the MBTI personality. © 2023 IEEE.

关键词： Support vector machines

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A Delay Sensitive Framework for Effective Healthcare using Machine Learning 10

A Delay Sensitive Framework for Effective Healthcare using M...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper proposes a delay-sensitive framework for the Healthcare Industry that can effectively predict diseases at an early stage. The framework utilizes Machine Learning (ML) techniques to integrate the Internet of Things (IoT), Cloud Computing, and ML in the medical field. The primary objective is to reduce the delay on the user's end while using Smart Healthcare services and provide the quickest response to the patient. The proposed framework can contribute to the better and optimal integration of emerging technologies in the Healthcare sector. The methodology involves collecting data from various sources, using ML algorithms, preprocessing feature extraction, and model training. The major findings indicate that the proposed framework can effectively predict diseases early and improve the quality of healthcare services. The interpretations and implications of the study highlight the importance of integrating emerging technologies in the Healthcare sector to provide better healthcare services to patients. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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A Novel Framework for Prediction of Heart Disease using Hybrid Classifier in the Cloud Environment 10

A Novel Framework for Prediction of Heart Disease using Hybr...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper presents a novel framework for predicting heart diseases in the cloud environment by employing hybrid classifiers. The dataset used in this research is the Cleveland heart disease dataset. Four hybrid classifiers were tested: Naive Bayes + Random Forest, Bayes Net + Random Forest, Naive Bayes + J48, and Naive Bayes + PART. The performance of the hybrid classifiers is evaluated by comparing accuracy, precision, recall, F. Measure, and PRC area. The results showed that the accuracy of the hybrid classifiers is significantly improved compared to the individual classifiers. The Naive Bayes + Random Forest hybrid classifier achieved the best results with the highest accuracy, F. Measure, and PRC area among the four hybrid classifiers. The proposed framework provides a useful approach to predicting heart disease in the cloud environment. This framework could diagnose heart diseases more accurately and improve healthcare services. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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An IoMT data security framework with Hyperledger Fabric for smart cities

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4875-4886页

作者： Samanta, Saikat Sarkar, Achyuth Kumari, Sangeeta Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

There is a high-risk privacy issue related to the Internet of Medical Things (IoMT) because of a lack of security in critical and sensitive information. We propose a framework for securing medical data in Healthcare 5.0 generated by the IoMT. Our framework uses Hyperledger Fabric (HF), a permissioned blockchain platform, to provide a decentralized and tamper-proof system for data management and sharing. The proposed framework includes modules for identity management, data encryption, and access control for Healthcare 5.0. Hyperledger is a Linux Foundation-hosted open-source project. The HF is one of the IBM-developed initiatives that eventually contributed to Hyperledger. The paper presents the architecture of the framework, as well as a prototype implementation and evaluation of its performance and security specific to consumer electronics for Smart Cities. This evaluation demonstrated that the proposed framework is efficient and effective at securing medical data in the IoMT, and could be used to develop secure and scalable healthcare systems in the future. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain technology Decentralized data management Healthcare data privacy Remote patient monitoring Smart healthcare

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Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine Learning Models 5th

Production Classification in E-Commerce Based on Product Des...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Bist, Yuvraj Gurbaxani, Paramesh Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The rapid growth of the internet has increased people's reliance on it for expressing opinions on products and stores. Text sentiment analysis is now a key research area. Deep learning methods are commonly used for text classification, but they suffer from issues like information loss and weak context. This paper enhances the existing models to simplify the process, educe training time, and improve overall recall and accuracy in text sentiment classification. With the rapid development of artificial intelligence, traditional manual techniques are fading, and AI algorithms are driving the swift progress of text sentiment classification. Product categorization is critical in e-commerce since it influences search, recommendations, inventory, and consumer experience. NLP approaches and models such as TF-IDF and Word2Vec are effective for categorizing products based on their textual descriptions. This literature study investigates the research evolution, problems, and upcoming trends in using TF-IDF and Word2Vec for product classification in e-commerce. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Adversarial machine learning

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Trustworthy Explainable Recommendation Framework for Relevancy

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computers, Materials & Continua 2022年第12期73卷 5887-5909页

作者： Saba Sana Mohammad Shoaib Department of Computer Science University of Engineering and TechnologyLahore54000Pakistan

Explainable recommendation systems deal with the problem of‘Why’.Besides providing the user with the recommendation,it is also explained why such an object is being *** helps to improve trustworthiness,effectiveness,efficiency,persuasiveness,and user satisfaction towards the *** recommend the relevant information with an explanation to the user is *** systems provide the top-k recommendation options to the user based on ratings and reviews about the required object but unable to explain the matched-attribute-based recommendation to the user.A framework is proposed to fetch the most specific information that matches the user requirements based on Formal Concept Analysis(FCA).The ranking quality of the recommendation list for the proposed system is evaluated quantitatively with Normalized Discounted Cumulative Gain(NDCG)@k,which is better than the existing *** is provided qualitatively by considering trustworthiness criterion i.e.,among the seven explainability evaluation criteria,and its metric satisfies the results of proposed *** framework can be enhanced to accommodate for more effectiveness and trustworthiness.

关键词： Explainable recommendation data analysis formal concept analysis(FCA)approach

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A Comparative Study in Image Fusion Using Orthogonal and Biorthogonal Wavelet 12th

A Comparative Study in Image Fusion Using Orthogonal and Bio...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Pandy, Saroj Kumar Kumar, Ankit Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Harnessing the Combined Power of Artificial Intelligence and Machine Learning for Diagnosis of Brain Tumor 5th

Harnessing the Combined Power of Artificial Intelligence and...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Dholaria, Jay Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The process of diagnosing diseases stands as a pivotal conduit for converting observed clinical evidence into precise disease appellations. Brain cancer, characterized by the uncontrolled growth of abnormal cells within the brain or its associated structures, is a relentless and complex disease that continues to pose significant challenges in the field of oncology. This abstract provides a concise overview of ongoing research efforts aimed at advancing our understanding of brain cancer and developing innovative approaches for early detection and more effective therapeutic interventions. Focusing on the domain of Brain Cancer, the author explores the application of Machine Learning (ML), Artificial Intelligence (AI), and Soft Computing (SC) algorithms to enhance the detection and diagnosis of brain cancer. In conclusion, the objectives of these explorations transcend mere technical validation. Rather, they are intrinsically motivated by the quest to discern the pragmatic feasibility and clinical utility of these advanced algorithms. Amidst the uncertainty and complexity inherent to cancer diagnosis, these algorithms emerge as beacons of decision-making support. The crucible of risk is where they are evaluated, offering insights into the adaptability, robustness, and potential pitfalls associated with their application. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Diseases

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Detecting COVID-19 and Lung Diseases Utilizing Parallel CNN Model on Chest X-Rays Enhanced with GradCAM Visualization 2

Detecting COVID-19 and Lung Diseases Utilizing Parallel CNN ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Kibria, Hafsa Binte Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

The emergence of COVID-19 has underscored the urgency of accurate medical diagnosis, particularly in the context of chest X-ray image classification for various lung conditions, including COVID-19, normal cases, viral pneumonia, and lung opacity. In light of the pandemic's impact, there is a pressing need to develop resource-efficient solutions for automated classification, specifically tailored for resource-constrained devices. This article addresses this need by proposing a Lightweight Parallel CNN (Convolutional Neural Network) architecture capable of effectively categorizing the aforementioned multi-class cases. Leveraging a comprehensive dataset of 21165 images, our model achieves a decent accuracy of 93.21% for four-class classification. Notably, the proposed architecture maintains efficiency with fewer than 783k parameters, demonstrating its potential for deployment on devices with limited computational resources. This research contributes to the advancement of accessible and accurate medical diagnostics, catering to the demand for streamlined and reliable solutions amidst challenging circumstances. © 2024 IEEE.

关键词： COVID-19

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