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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

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A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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DSPWE: distributed sentiment polarized word embedding for voluminous textual data

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9419-9433页

作者： Dhanani, Jenish Mehta, Rupa Rana, Dipti P. Department of Computer Science and Engineering S. V. National Institute of Technology Surat India

Sentiment analysis provides an insight of the public’s emotions towards governments, organizations, product developers etc. Word embedding is a group of feature learning techniques transferring the raw textual data into real-valued vectors, which can be used to analyze the sentiment of text. Word2vec is a prominent word embedding approach designed to learn the semantic specific low-dimensional word vectors using contextual information. It has demonstrated poor performance for voluminous text data due to in-memory processing of the entire vocabulary and associated word embeddings. Additionally, it does not effectively preserve the sentiment information, which results in the closeness of opposite sentiment words in embedding space causing performance loss. In fulfillment of the aforesaid challenges, this paper presents a novel Distributed Sentiment Polarized Word2Vec (DSPW2V) approach to compute the sentiment polarized word embedding in a distributed computing framework (i.e. MapReduce and Spark) over the cluster of commodity hardware. The word embedding and sentiment polarity are computed simultaneously to generate the sentiment polarized word embedding. The proposed approach is compared with existing non-distributed approach SPW2V (Sentiment Polarized Word2Vec), and existing distributed approach of word2vec from Spark’s MLLib library. Experiment results demonstrate the outperforming reduction in execution time with comparable accuracy and F1-Score, which reveal the superiority of the proposed approach. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： MapReduce

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A Survey on Fake News Detection Using Multivariate Feature Selection and Hybrid Deep Learning Approach 1

A Survey on Fake News Detection Using Multivariate Feature S...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Dongre, Aditya Kumar Kalaiarasi, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature selection combined with hybrid deep learning approaches to enhance the accuracy and robustness of fake news detection systems. The paper reviews various feature selection techniques, including chi-square, information gain, and correlation-based methods, that help in identifying the most relevant features for detecting fake news. These techniques are crucial in reducing dimensionality, improving processing time, and enhancing the performance of deep learning models. Furthermore, the study examines hybrid deep learning frameworks, which integrate multiple architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers to capture both linguistic and contextual information from news data. This survey provides a comprehensive analysis of recent advancements, highlighting key research contributions, datasets, and evaluation metrics used in fake news detection. The integration of multivariate feature selection with hybrid deep learning models offers a promising approach to addressing the complexities of fake news identification. Finally, the paper outlines the challenges and future research directions in improving model generalization, handling adversarial content, and adapting to evolving patterns of misinformation. © 2025 IEEE.

关键词： Adversarial machine learning

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Ultra-low-defect homoepitaxial micro-LEDs with enhanced efficiency and monochromaticity for high-PPI AR/MR displays

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PhotoniX 2024年第1期5卷 335-356页

作者： Yibo Liu Guobin Wang Feng Feng Mengyuan Zhanghu Zhengnan Yuan Zichun Li Ke Xu Hoi Sing Kwok Zhaojun Liu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of SciencesJiangsu Institute of Advanced SemiconductorsSuzhouChina

The issue of brightness in strong ambient light conditions is one of the critical obstacles restricting the application of augmented reality(AR)and mixed reality(MR).Gallium nitride(GaN)-based micro-LEDs,renowned for their exceptional brightness and stability,are considered the foremost contenders for AR ***,conventional heteroepitaxial growth micro-LED devices confront formidable challenges,including substantial wavelength shifts and efficiency *** this paper,we firstly demonstrated the high-quality homoepitaxial GaN-on-GaN micro-LEDs microdisplay,and thoroughly analyzed the possible benefits for free-standing GaN substrate from the material-level characterization to device optoelectronic properties and microdisplay application compared with sapphire *** GaN-on-GaN structure exhibits a superior crystal quality with ultra-low threading dislocation densities(TDDs)of~105 cm^(-2),which is three orders of magnitude lower than that of *** an in-depth size-dependent optoelectronic analysis of blue/green emission GaN-on-GaN/Sapphire micro-LEDs from 100×100 shrink to 3×3μm^2),real that a lower forward voltage and series resistance,a consistent emission wavelength(1.21 nm for blue and 4.79 nm for green@500 A/cm2),coupled with a notable reduction in efficiency droop ratios(15.6%for blue and 28.5%for green@500 A/cm^(2))and expanded color gamut(103.57%over Rec.2020)within GaN-on-GaN 10μm *** but not least,the GaN-on-GaN micro-display with 3000 pixels per inch(PPI)showcased enhanced display uniformity and higher luminance in comparison to its GaN-on-Sapphire counterpart,demonstrating significant potentials for high-brightness AR/MR applications under strong ambient light.

关键词： optoelectronic ambient epitaxial

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DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis

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SN computer science 2024年第5期5卷 1-21页

作者： Sinruwng, Ngamwal Yogita Pal, Vipin Verma, Anshul Department of Computer Science and Engineering National Institute of Technology Meghalaya Meghalaya Shillong 793003 India Department of Computer Science and Engineering National Institute of Technology Kurukshetra Haryana Kurukshetra 136119 India Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi New Delhi 110078 India Department of Computer Science Banaras Hindu University UP Varanasi 221005 India

In response to the global COronaVIrus Disease of 2019 (COVID-19) pandemic, widespread vaccination campaigns were initiated worldwide. To minimise Adverse Drug Reactions (ADRs) linked to the vaccines, extensive pre-clinical trials and post-marketing surveillance activities were undertaken in a traditional setting. However, there has been a limited exploration of social media data for this purpose. As social media data is composed of real-time user experiences on vaccines, its analysis is pivotal for understanding vaccine safety. This work proposes a framework named Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis (DARCVAA). It employs four Deep Neural Networks (DNN) based classification models to detect ADRs of COVID-19 vaccines from Reddit’s posts. Further, it uses the Apriori algorithm to extract associations between vaccines and ADRs to comprehensively understand their relationships. The statistical significance of the extracted associations has been evaluated in terms of support and confidence. The proposed framework has been applied to a dataset collected from Reddit’s platform from September 2020 to July 2021 and annotated with the help of a medical expert. The experimental results showed that the proposed framework has outperformed six state-of-the-art detection models, which include Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT), Vaccine Adverse Events-Mine (VAEM-Mine), Vaccine Adverse Events (VAE), Support Vector Machines (SVM) and Naıve Bayes (NB) in respect to precision, recall, F1-score and accuracy. The identified ADRs have been validated from the official surveillance reports, and the validation results have proven the efficacy of the proposed framework toward ADR detection and capturing possibly emerging ADRs. The analysis of detected ADRs in terms of frequent ADRs, ADRs unique to vaccines and genders and associations o

关键词： Adverse reactions of vaccines BioBERT COVID-19 Deep neural networks Social media

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Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud

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SN computer science 2024年第6期5卷 722页

作者： Gadad, Veena Sowmyarani, C.N. Dayananda, P. Department of Computer Science and Engineering R V College of Engineering Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education

Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones. © The Author(s) 2024.

关键词： Anonymization Data privacy Information loss Multiple sensitive attributes Privacy threats

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A study of learning models for COVID-19 disease prediction

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2581-2600页

作者： Jain, Sakshi Roy, Pradeep Kumar School of Information Technology Vellore Institute of Technology TN Vellore632014 India Department of Computer Science and Engineering Indian Institute of Information Technology Surat Gujarat Surat394190 India

Coronavirus belongs to the family of Coronaviridae. It is responsible for COVID-19 communicable disease, which has affected 213 countries and territories worldwide. Researchers in computational fields have been active in proposing techniques to filter the information and recommendations about this disease and provide surveillance in controlling this outbreak. Researchers used Chest X-ray images, abdominal Computed Tomography scans, and Tweet datasets for building machine learning and deep learning-based models for COVID-19 predictions and forecasting purposes. Accuracy, sensitivity, specificity, precision, and F1-measure are the five primary evaluation criteria researchers employ to evaluate the quality of their study. This article summarises research works on COVID-19 based on machine learning and deep learning models. The analysis of these research works, along with their limitations and source of datasets, will give a quick start for future research to arrive at a defined direction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Coronavirus

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Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system

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International Journal of computers and Applications 2022年第10期44卷 991-1001页

作者： Fathima, M. Dhilsath Samuel, S. Justin Natchadalingam, R. Kaveri, V. Vijeya Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSN Engineering College Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Heart disease and diabetes are global health issues that affect people worldwide. Diabetes is becoming a significant concern, and Diabetes patients have a substantially higher risk of heart disease morbidity and mortality than people without diabetes. These conditions are associated with hospitalizations and emergency room visits, which raises healthcare expenses. An important strategy to improve health care outcomes and reduce unnecessary costs is to identify and anticipate them in patients. Clinical Decision Support Systems (CDSS) assess patient data from clinical datasets to help disease prediction and enhance treatment options for heart disease and diabetes, and other disorders. According to the literature, most CDSS have used machine learning algorithms for predicting heart disease and diabetes. These algorithms performed worthily, but the accuracy of these machine learning (ML) algorithms is lacking, especially in medical data, which contains numerous complex attributes such as resting blood pressure, serum cholesterol, fasting blood sugar, and thalassemia value. This proposed work developed a majority voting ensembled feature selection (MVEFS) technique and customized deep neural network (CDNN) to develop a CDSS for heart disease and diabetes prediction. This deep neural network-based CDSS best performing than ML-based CDSS. There are several input attributes in the clinical dataset. Some attributes are not associated with disease and have negative consequences when used in clinical data analysis for disease prediction. As a result, feature selection is essential for removing unimportant features. The feature selection significantly minimizes system learning time, which improves CDSS performance efficacy. The MVEFS selects the associated heart disease and diabetes-related features from the clinical dataset. The classifier execution time, accuracy, sensitivity, precision, specificity, and F1-score are the performance metrics used to evaluate the proposed CDSS.

关键词： Forecasting

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A Disease Prediction Framework Based on Predictive Modelling

A Disease Prediction Framework Based on Predictive Modelling

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Kaur, Harmohanjeet Shah, Pooja Muhuri, Samya Kumari, Suchi Computer Science & Engineering Department Thapar Institute of Engineering and Technology Punjab India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

The rise of chronic diseases has become a major public health challenge globally. Early prediction and prevention of these diseases can help reduce their prevalence and improve patient outcomes. The proposed disease prediction system, which is based on predictive modeling, may anticipate the user’s illness by using the user’s symptoms as input. The framework evaluates the symptoms taken as input by the user and generates the likelihood of developing the disease. The disease prediction framework based on machine learning (ML) techniques can help in a more accurate diagnosis than conventional methods. In the current manuscript, we have designed a disease prediction methodology using multiple ML techniques. The proposed framework also has the potential to enhance disease surveillance and support public health interventions, including disease management and resource allocation. The accuracy of our approach is shown over the benchmark data sets, which consist of more than .230 diseases. The suggested diagnostic algorithm outputs the disease name that a person might be experiencing based on the symptoms taken into consideration. The proposed framework provides a scalable and effective solution for public health decision-makers to manage chronic diseases and improve patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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