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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Takalkar, Atharva Shiragapur, Bahubali School of Computer Science Engineering and Applications D y Patil International University Pune India

ISBN: (纸本)9798350302288

This paper offers a thorough investigation into quantum cryptography, a security paradigm based on the principles of quantum mechanics that provides exceptional guarantees for communication and information protection. The study covers the fundamental principles of quantum cryptography, mathematical modelling, practical applications, and future prospects. It discusses the representation of quantum states, quantum operations, and quantum measurements, emphasising their significance in mathematical modelling. The paper showcases the real-world applications of quantum cryptography in secure communication networks, financial systems, government and defence sectors, and data centres. Furthermore, it identifies emerging domains such as IoT, 5G networks, blockchain technology, and cloud computing as promising areas for implementing quantum cryptographic solutions. The paper also presents avenues for further research, including post-quantum cryptography, quantum cryptanalysis, multi-party quantum communication, and device-independent quantum cryptography. Lastly, it underscores the importance of developing robust infrastructure, establishing standards, and ensuring interoperability to facilitate widespread adoption of quantum cryptography. This comprehensive exploration of quantum cryptography contributes to the advancement of secure communication, information protection, and the future of information security in the era of quantum technology. © 2023 IEEE.

关键词： Secure communication

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Machine Learning Algorithm for Sentiment Analysis on Tweets 2

Machine Learning Algorithm for Sentiment Analysis on Tweets

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saravanan, T.M. Jegadeesan, M. Selvaraj, P.A. Balaji, S. Kavinkumar, K. Abdullah, A.S.M. Department of Computer Applications Kongu Engineering College Tamil Nadu Perundurai638 060 India

ISBN: (纸本)9798350340235

Since there is so much user-generated content and there is a need to derive valuable insights from it, sentiment analysis on Twitter has become a hot study topic. This study introduces a sentiment analysis machine learning system created especially for Twitter tweets. The suggested approach uses machine learning techniques to automatically categorize tweets into categories with positive, negative, or neutral sentiment. The preparation phase of the algorithm first cleans and normalizes the tweets by removing noisy elements such special characters, URLs, and hashtags. The tweets are then represented as numerical feature vectors using techniques for feature extraction. To extract the semantic and contextual data included in the tweets, a variety of lexical and syntactic features are used, such as bag-of-words, n-grams, and part- of-speech tags. Next, a supervised learning approach is utilized to train a sentiment classifier using a training dataset of labeled tweets. In order to find the best classifier for Twitter sentiment analysis, experiments are conducted with a number of well-known machine learning techniques, including Random Forest. Methods for picking the most informative features are being investigated to enhance the performance of the classifier's. © 2023 IEEE.

关键词： bags-of-words hashtags Machine Learning parts-of-speech Random Forest

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Fake News Classification via CNN 11

Fake News Classification via CNN

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Garg, Sonal Sharma, Dilip Kumar GLA University Department of Computer Engineering and Applications India

ISBN: (纸本)9781665487320

The use of digital media allows people to consume daily news, opinions and information covering a large variety of topics. The availability of social media allows users to easily share their own views and opinions, but this online platform is also responsible for the sudden increase rate of fake news. Fake news may create serious damage to society, including creating anger, hype and depression and, in some cases, lead to death also. A different approach has been proposed in the past to combat phonic news. This paper used CNN deep-learning classifiers for misleading news detection. The proposed approach uses the Reuter dataset to perform experiments. The proposed model obtained the highest of 93.64% accuracy, which is higher in comparison to existing methods. © 2022 IEEE.

关键词： Digital storage

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An Effective Sentiment Analysis Classification for Opinion Prediction using Machine Learning Schemes

An Effective Sentiment Analysis Classification for Opinion P...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Saravanan, T.M. Jagadeesan, M. Selvaraj, P.A. Rithika, B.M. Karan, G. Tamil Selvan, R.C. Kongu Engineering College Department of Computer Applications Tamil Nadu Perundurai638 060 India

ISBN: (纸本)9798350348217

In the blooming of web technology, the rise of electronic commerce websites is rapidly increasing. Users started expressing their views similar to text reviews or comments. This research deals with the identification and extraction of the exact opinion of the customers across reviews, feedbacks, or comments. Pre-processing techniques are used to clean the dataset and the machine learning algorithm like k-nearest neighbor (KNN) is used as a solution to this problem, to have a good evaluation of sentiments (Positive, Negative, Neutral). In accumulation, the precision of the algorithm highly depends on the value of k and distance metric for classification. In our work we used k value of 6 and euclidean distance equation for classification of the KNN algorithm. The proposed KNN algorithm has been applied on publicly available standard review dataset repository, to show the exact nature and features based on customer's opinions, and the accuracy of determining the feasibility of the k-nearest neighbor algorithm of 98% is predicted. © 2023 IEEE.

关键词： Sentiment analysis

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Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Environments: computer Integration in Smart Environment 2

Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Env...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Taneja, Dev Gupta, Pooja Taneja, Kavita Manipal University Jaipur Department of Computer and Communication Engineering India Panjab University Chandigarh Department of Computer Science and Applications India

ISBN: (纸本)9798350369960

Present Information Technology scenario is engulfed with exponential rise in mobile computing devices mostly embedded in diverse equipments of our daily life. This has led to evolutionary demand of new solutions to mobile computing for smart environments equipped with Internet of Things (IoT). A plethora of challenges are emerging to ensure effective internet connectivity for such mobile devices. These devices are mostly equipped with sensors and demand continuous computing and data transfers. The globe is evolving to humancentred society, i.e., Society 5.0 loaded with innovations of Big Data, IoT, Artificial Intelligence and much more to make social life comfortable and sustainable. Hence, this trend is the need of the hour but has brought a range of new challenges to MANETs for mobile computing in smart environments. This paper explores the MANET-IoT computing trends and provides a detailed analysis of the challenges for computing in the smart environment having MANET-IoT as backbone for mobile communications. © 2023 IEEE.

关键词： Internet of Things (IoT) Mobile Ad Hoc Network (MANET) Mobile Computing Society 5.0

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A Survey of Machine Learning for Assessing and Estimating Student Performance 5th

A Survey of Machine Learning for Assessing and Estimating St...

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Avneet Bhatia, Munish Department of Computer Science and Engineering Lovely Professional University Punjab India Department of Computer Applications NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789811998751

Educational data mining (EDM) contributes cutting-edge methodologies, strategies, and applications to the advancement of the education system, hence playing a crucial part in its development. Utilising machine learning and data mining approaches to explore and utilise educational data, the current advancement gives essential tools for comprehending the student learning environment. Academic institutions in the twenty-first century operate in a highly competitive and complicated environment. Among the prevalent issues faced by universities are performance analysis, the provision of a high-quality education, systems for evaluating the performance of students, and the planning of future activities. Student intervention programmes must be created in these universities in order to address the academic difficulties encountered by students. From 2009 through 2021, the relevant EDM literature relative to predicting student attrition and students at risk is examined in this review. According to the review’s results, several machine learning (ML) methodologies are used to discover and address the fundamental challenges of forecasting students at risk and student withdrawal rate. Furthermore, the bulk of studies make use of data from student college/university database and online learning portals. It was determined that ML techniques play crucial roles in forecasting students at risk and withdrawal rates, hence boosting student performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

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Big Data Mining and Analytics 2023年第4期6卷 513-525页

作者： Ankit Kumar Kamred Udham Singh Manish Kumar Department of Computer Engineering and Applications GLA UniversityMathura 281406India School of Computing Graphic Era Hill UniversityDehradun 248002India Department of Electronics and Communication Engineering GLA UniversityMathura 281406India.

The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be *** UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep *** used a dataset with 13 primary characteristics to carry out the *** vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary *** programming is used to process the *** research initiatives have used machine learning to speed up the healthcare *** also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection *** the accuracy and confusion matrix has resulted in some favorable *** get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.

关键词： artificial intelligence support vector machine logistic regression cleveland dataset supervised algorithm human sensing

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Enhancing Heart Disease Prediction Through VGG16 and CapsNet 5

Enhancing Heart Disease Prediction Through VGG16 and CapsNet

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hemalatha, S. Kavitha, T. Sivabalaselvamani, D. Naveena, N. Haripriya, M. Kongu Engineering College Department of Computer Applications Erode Perundurai India Presidency University School of Information Science Bengaluru India

ISBN: (纸本)9798331518097

This research investigates the application of deep learning algorithms, specifically Capsule Networks and Convolutional Neural Networks (CNNs) based on VGG16, for the early prediction of cardiac issues. By analyzing a diverse dataset of health-related information, we aim to improve the accuracy of binary classification in predicting the presence or absence of heart disease. The study compares the performance of CapsNets and VGG16 CNNs, with VGG16 demonstrating superior accuracy at 99.8% and CapsNets achieving 98% precision. This research contributes to the advancement of predictive analytics in healthcare, enabling more accurate and timely diagnosis of cardiac conditions and paving the way for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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