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14th International Conference on Contemporary Computing, IC3 2022

作者： Bansal, Priya Jain, Shikha Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9781450396752

Due to the redefining of money and its price volatility, cryptocurrencies have become one of the most prominent phenomena in recent years. This research investigates how well public opinion on Twitter and news stories may be used to estimate cryptocurrency returns. Three models are designed and compared: LSTM based, LSTM-and-GRU-based, and LSTM and CNN-based models. Firstly, numerals and historical datasets of Bitcoins are used for all three models, which are further extended to Twitter and news datasets. An error score of 1015.17, 1106.71, and 3010.63 is obtained. Then, the proposed models are applied to the combined dataset of Twitter and news from Ethereum, and an error score of 47.85, 34.01, and 58.27 is obtained. Finally, the same methodology is applied to the combined dataset of Litecoin and obtained an error score of 9.45, 8.81, and 15.94. It is observed that LSTM with GRU generates the best results for all the datasets. © 2022 ACM.

关键词： Sentiment analysis

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Exploring Multimodal Framework of Optimized Feature-Based Machine Learning to Revolutionize the Diagnosis of Parkinson’s Disease: AI-Driven Insights

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-20页

作者： Khanom, Fahmida Mostafiz, Rafid Uddin, Khandaker Mohammad Mohi Department of Mathematics American International University-Bangladesh 408/1 Kuratoli Khilkhet Dhaka-1229 Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering Southeast University Dhaka China

Parkinson’s disease (PD), a progressive neurodegenerative disorder, poses significant challenges to healthcare systems worldwide due to its increasing prevalence and need for early detection to improve patient outcomes. This study introduces a robust diagnostic framework leveraging a multimodal feature selection approach, combining Minimum Redundancy Maximum Relevance (mRMR) with Recursive Feature Elimination (RFE), to optimize predictive accuracy in detecting PD. Using a dataset of 894 instances from the UCI Machine Learning Repository, the framework implemented comprehensive data preprocessing and feature selection techniques to refine input features. The Extra Trees classifier, integrated with the mRMR-RFE multimodal feature selection method, demonstrated superior performance over nine other machine learning models, achieving a perfect accuracy of 100%, with high specificity, sensitivity, precision, and F1 scores. This framework highlights the efficacy of ensemble learning methods and feature optimization in healthcare, ensuring computational efficiency and high predictive reliability. By addressing the challenges of dimensionality reduction and ensuring precise predictions, the proposed model sets a benchmark for machine learning applications in PD diagnosis. The results emphasize the potential of this framework to facilitate early detection and support clinical decision making, with significant implications for advancing AI-driven healthcare systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Extra trees classifier Machine learning Parkinson’s disease Recursive feature elimination (RFE)

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Exploring Quantum Machine Learning Algorithms for Enhanced Data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Babu, M. Arun, M. Pushparani, S. Muthukrishnan, A. Poongavanam, N. Rajalakshmi Engineering Çollege Department of Information Technology Thandalam Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Breast Cancer Classification using Random Forest Algorithm 12

Breast Cancer Classification using Random Forest Algorithm

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2023 12th International Conference on information and Electronics engineering, ICIEE 2023

作者： Batool, Amreen Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science Technology Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of

This study uses detect breast cancer based on Random Forest (RF). It is crucial to diagnose the illness to identify treatment solutions closely linked to patient safety. Breast cancer is diagnosed using past medical records and various classification methods used in data mining fields today. Each technique performs differently depending on the input feature types and model parameters. Neutral Networks have been proven to be more effective in data analysis and pre-diagnosis without requiring medical knowledge. The study reduces diagnostic variance and increases diagnostic accuracy by overcoming the limitation of individual models. The Random Forest model had a training and validation accuracy of 90% and 91%. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Boosting Accuracy of Fake Review Prediction Using Synthetic Minority Oversampling Technique 3

Boosting Accuracy of Fake Review Prediction Using Synthetic ...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Saxena, Bhawna Goyal, Shruti Kumari, Anjali Agarwal, Anushka Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9781665462006

In recent times prior to making a purchase, the vast majority read reviews about that product, and their decision is largely driven by the reviews. Deceitful online sellers often gather fake or spam reviews for their products or services, thereby reducing the effectiveness of online reviews. The review data is often imbalanced such that the fake reviews greatly outnumber the genuine reviews. An imbalance leads to a bias, as the model tends to mostly predict the majority class. To attain a high-quality classification outcome, the issue of imbalanced data should be resolved before applying the classification algorithms. This paper studies the performance of supervised machine learning classifiers pertaining to fake review detection. The approach put forward in this paper aims to improve the prediction accuracy of popular supervised learning classifiers Random - Forest, LightGBM, XGBoost, Naive Bayes, and Decision Tree on an imbalanced review dataset For boosting the accuracy of these classifiers, the Synthetic Minority Oversampling Technique is used for addressing the class imbalance problem. The performance of the classifiers has been studied by changing the oversampling parameters. The application of SMOTE showed a significant improvement in the classifier's prediction accuracy. © 2022 IEEE.

关键词： Decision trees

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An Effective Noise Removal Technique for Enhancing Ischemic Brain Stroke Computed Tomography Images 1

An Effective Noise Removal Technique for Enhancing Ischemic ...

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

作者： Puneeth, S.P. Poornima, B. PremaSudha, B.G. Patil, N.S. Puneetha, B.H. Jadekar, Ranjana B. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Information Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Siddaganga Institute of Technology Tumkur Department of Mca Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Business System Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Davangere Department of Computer Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

Ischemic stroke significantly affects millions of people motor and cognitive skill. Early identification of ischemic stroke helps in preventing loss of life. Usually, noise is present in Computed-Tomography (CT) during acquisition and the noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. Thus, denoising CT medical images is crucial in preserving information and restoring images contaminated with noise. The Convolution-Neural-Network (CNN) has been used to remove the noise with good effect;however, the performance resulted in a loss of clarity and fine details preservation that rendered the CT images unsuitable. In addressing this, presented an enhanced noise removal techniques namely Morphology-Aware CNN (MA-CNN) to enhance brain CT image of ischemic stroke patients for better segmentation, classification to enhance diagnosis. The MA-CNN performance is studied in term of Peak-signal-to-noise-ratio (PSNR);the overall result attained shows the proposed model exhibit higher PSNR in comparison with existing denoiser methods and assures good performance in maintaining the quality of the image and fine details' preservation. © 2024 IEEE.

关键词： Convolutional neural networks

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

IOT & Machine Learning Models to Develop The Architecture Fo...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Alotibi, Mohammed Masaad Rajeyyagari, Sivaram Al-Omari, Omaia Mohammed Alymani, Mofadal Planning and Digital Transformation Ministry of Health Riyadh Saudi Arabia College of Computing and Information Technology Department of Computer Engineering Shaqra Saudi Arabia College of Computing and Information Technology Department of Information Systems Shaqra Saudi Arabia

ISBN: (纸本)9798350317060

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people's quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure. © 2023 IEEE.

关键词： Internet of things

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Using Topic Modeling to Find Hidden Structures in the Language of Sesotho sa Leboa 8

Using Topic Modeling to Find Hidden Structures in the Langua...

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8th Conference on information Communication technology and Society, ICTAS 2024

作者： Masethe, Hlaudi Daniel Masethe, Mosima Anna Ojo, Sunday O. Owolawi, Pius A Tshwane University of Technology Department of Data Science South Africa Sefako Makgatho Health Sciences University Department of CSIT South Africa Durban University of Technology Department of Information Technology South Africa Tshwane University of Technology Department of Computer Engineering South Africa

ISBN: (纸本)9798350314892

Calls from customers are a great way for service providers to get input about their application process. These calls may also provide a plethora of previously undiscovered information about the queries and concerns of customers. Unfortunately, it might be difficult to properly examine these call data because they are usually unstructured. This study aims to extract valuable customer insights from recorded customer calls to a South African government workers medical scheme (GEMS) application procedure by utilizing Topic Modeling techniques, a branch of Natural Language Processing. The objective of the research is to examine popular Topic Modeling algorithms such as LDA, BERTOPIC, LSA, LSI, and HDP for call content analysis, and categorization, to gain insights into human behaviors and experiences. The research intends to give the business a thorough grasp of client wants, interests, and concerns by utilizing the power of these algorithms, ultimately enabling more efficient decision-making processes. Natural language processing makes heavy use of topic modeling approaches to derive subjects from unstructured text input. A widely used method in topic modeling allows topics to be automatically extracted from a large corpus of textual materials. BERTopic, a transformer-based architecture, outperforms all other conventional algorithms with a greater accuracy of 91%. © 2024 IEEE.

关键词： Modeling languages

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Deep Learning Approaches for Malware Detection in the Industrial Internet of Things: A Comprehensive Analysis 25

Deep Learning Approaches for Malware Detection in the Indust...

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25th International Arab Conference on information technology, ACIT 2024

作者： Samara, Ghassan Fadda, Mohammad Abu Al-Mohtaseb, Abeer Alazaidah, Raed Alrefai Mohamed, N. Odeh, Mahmoud Aljaidi, Mohammad Shuaib, Mohammed Mahmod Kanan, Mohammad Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Science Department of Mathematics Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This survey aims to identify hazards associated with the Internet of Things (IoT), focusing on malware that can infiltrate various devices, applications, and systems within the Industrial Internet of Things (IIoT). Such malware enables malicious actors to manipulate these systems. This research utilizes deep learning as a method for detecting malicious programs. Additionally, it provides hypotheses and background information on deep learning and the IIoT, along with an overview of the types of malware that can compromise IoT applications. Finally, the study presents findings related to the research hypotheses and relevant topics. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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