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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research attempts to identify stock market bubbles using technical indicators combined with machine-learning processes. If not detected early on, stock bubbles arising from overvaluation and speculation can incur huge losses. The study used Technical Indicators such as the Relative Strength Index(RSI), Moving Average Convergence Divergence(MACD), Bollinger Bands, and Volume Indicators to catch the market trends. These features were analyzed using Support Vector Machine, Logistic Regression, and Random Forest models with a 99.84% test accuracy and AUC equal to 1.0 obtained with Random Forest, which proved to be, in this research, the most reliable for bubble detection. The study compares, in detail, the results, including Confusion Matrices and Receiver Operating Characteristic curves, validating the findings. The approach here would be a robust and data-driven framework for investors and analysts to recognize bubbles, facilitating better decision-making by risk minimization. The results also contribute to further developments in the role of machine learning in equity markets. © 2025 IEEE.

关键词： Marketplaces

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Predicting Stock Prices with K-Nearest Neighbors using Moving Average Techniques 1

Predicting Stock Prices with K-Nearest Neighbors using Movin...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research analyzes the effectiveness of the K-Nearest Neighbors algorithm combined with moving average techniques, Five-day and Ten-day Exponential Moving Averages, specifically EMA-5 and EMA-10, to predict stock prices. The study will focus on the adjusted closing price to deal with the autocorrelation issue that is common in time series data. The performance of the models was evaluated based on four key metrics: mean absolute error, mean squared error, root mean square error, coefficient of determination, overfitting risk test and generalization capability test. It was shown from the results that KNN models integrated with EMA-5 and EMA-10 offered high accuracy, one of which, with an R2 coefficient indicating the capacity to capture stock price trends with an accuracy of up to 99%. The research pointed out how short-term EMAs, combined with machine-learning algorithms, can substantially improve stock price prediction accuracy. It gives useful insights into stock price behaviour, therefore providing a decisive precedent for future works in creating a viable method for selecting trading strategies using machine-learning tools and moving averages. © 2025 IEEE.

关键词： Time series

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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Sentiment Analysis of Twitter Tweets for COVID- 19 Pfizer Vaccines 7

Sentiment Analysis of Twitter Tweets for COVID- 19 Pfizer Va...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Shah, Milind Vasant, Avani Gandhi, Kinjal Panesar, Sonia Department of Computer Science & Engineering Gujarat Vadodara India Baroda University School of Computer Science Engineering & Technology Department of Computer Science & Engineering Gujarat Vadodara India

ISBN: (纸本)9798350397253

Coronavirus pandemic, also referred to as COVID-19, broke out in 2019 and caused extensive illnesses. Many experts in the medical field are of the opinion that the epidemic first appeared in Wuhan, China, before it spread to other areas of the world. Washing one's hands, covering one's face, and socially isolating oneself from others are only some of the preventative methods that decision-makers have advocated. Early in 2021, a number of nations, notably the United States, started giving their citizens the COVID-19 vaccination. Since the World Health organization proclaimed that COVID-19 was causing a pandemic, there has been a concerted effort on a worldwide scale to create vaccinations against the virus. Because people often use social media, and Twitter in particular, to share their knowledge and attitudes on global topics such as COVID-19-vaccination, Twitter is an excellent tool for researching public perspectives. This research study performs a sentiment analysis of Pfizer vaccines on the Twitter platform. The dataset contains recent tweets about Pfizer and BioNTech vaccines. In the context of this research, we have investigated people's sentiments regarding vaccinations manufactured by Pfizer. Additionally, the dataset required to build this machine -learning model may be downloaded from Kaggle. The tweets acquired from Twitter have been examined using machine learning algorithms to determine the various sentiments that are included within the tweets. The results show 40.9 % of Tweets with positive segments, while 48.9 % with negative and 10.2% seemed neutral. In terms of model performance evaluation, this study has used different ML classification algorithms to see which classifier gives the best model accuracy. © 2023 IEEE.

关键词： COVID-19

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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Sentimental Analysis of Amazon Reviews using Map Reducer Technique in Hadoop 18

Sentimental Analysis of Amazon Reviews using Map Reducer Tec...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Byali, Manasi S. Kanakaraddi, Suvarna Badagi, Tulsa Chikaraddi, Ashoka Kle Technological University Department of Computer Science Engineering Hubli India Kle Technological University School of Computer Science Engineering Hubli India

ISBN: (纸本)9789380544519

Sentiment analysis (SA) is an active and dynamic aspect of text mining that focuses on the automated analysis of subjectivity, opinions, and sentiments in textual material. The study explores new SA domains such as resource development, emotion recognition, and transfer learning. The study combines rule-based classification, machine learning, and supervised learning in a novel hybrid technique. The usefulness of this hybrid method is demonstrated through experiments on multiple datasets, including MySpace comments, product reviews, and movie reviews, resulting in improved classification efficacy as judged by both macro and micro-averaged criteria. The study also supports an additional semi-automatic approach in which classifiers cooperate to enhance overall performance. In this article, we have applied SVM, Decision tree, and Linear SVM to the Amazon review dataset and got the output in the form of performance metrics such as CPU-time and maximum memory usage. and we found that Depending on the linearity of the data, a linear SVM or a Decision Tree is more appropriate;select a linear SVM for clearly defined separations and a Decision Tree for intricate, non-linear interactions, customizing the model to the features of the dataset and the goals of the research. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Hadoop Linear Regression MapReduce RDBMS (Relational Database Management System) SVM (Support Vector Machine)

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Advanced U-Net++ Architecture for Precise Brain Tumor Segmentation in MRI Images: A Robust Solution for Medical Image Analysis

Advanced U-Net++ Architecture for Precise Brain Tumor Segmen...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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Extensive analysis of IoT Architecture and Access Control Policies in Fog Enabled Environment 2

Extensive analysis of IoT Architecture and Access Control Po...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Lalit, Mohit Maurya, Sant Kumar Khurana, Shiraz Gupta, Nishant Computer Science and Engineering Department Chandigarh University Punjab Mohali India Computer Science and Engineering Department School of ICT Gautam Buddha University Greater Noida India Computer Science and Engineering Department Sharda University Greater Noida India

ISBN: (纸本)9798331541217

Cloud technology provides solutions to a variety of software platform and data accessibility issues. However, as the industrial revolution 4.0 progresses, innovations such as healthcare 4.0 smart buildings, IoT enabled transportation services, and many other new paradigms necessitate more secure data management solutions. Meanwhile, Fog Computing (FC) arises as a supplement to cloud computing, providing a variety of solutions to issues such as low latency and real-time data transfer. However, security concerns remained, and to address such issues, a regressive access control mechanism approach study was necessary. This article addresses methods to access control mechanisms for preserving privacy and confidentiality during communication in a cloud-fog computing environment. The research goal is to investigate various access control approaches used in the recent past for fog computing enabled environments. © 2024 IEEE.

关键词： Cloud platforms

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