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Enhancing Cloud Virtualization Security with a Z-Score Based Conjugate Self-Organizing Migration ML Model 15

Enhancing Cloud Virtualization Security with a Z-Score Based...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sidana, Sagar Thakur, Sujata Negi Sinha, Anurag Indhumathi, N. Raj, Petheru Al-Khayyat, Ahmed Modi, Vyom Mishra, Aditya Kumari, Anita Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India Graphic Era Hill University Department of computer science and engineering India Ignou School of Computing and information Science New Delhi India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Marwari Education Foundations Group Of Institutions Rajkot India Gla University Department of computer science and engineering Applications Mathura India Rkdf University Department f English Literature India

ISBN: (纸本)9798350370249

This study presents a new machine learning model for detecting malware in cloud computing settings called Conjugate Self-Organizing Migration (CSOM), which is based on Z-score. Feature vectors are standardized by the model through the use of Z-score normalization, which improves its flexibility with changing data distributions. By merging self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the threat of growing malware. Using Z-score normalization to guarantee correct representation of changing data, the imaging approach depicts the cloud environment as a dynamic, self-organizing landscape. Through conjugate migration techniques, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of tiny fluctuations in harmful actions. The Z-score-based CSOM model is effective in detecting malware at high accuracy rates in a variety of dynamic cloud environments, as demonstrated by the experimental results. The model demonstrates its flexibility in responding to changing cyberthreats with its strong performance in real-world settings. Finally, with its better accuracy and adaptability, the suggested Z-score-based CSOM model offers a potential approach for cloud computing virus detection. In order to handle the constantly shifting environment of cloud-based cyber threats, further research may examine scalability and more refinement. © 2024 IEEE.

关键词： Cloud computing Conjugate Self-Organizing Migration Machine learning Model Malware Detection Z-score

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Performance Comparison of Breathing Signal Classifiers Using Machine learning Techniques 14

Performance Comparison of Breathing Signal Classifiers Using...

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14th IEEE international conference on Control System, computing and engineering, ICCSCE 2024

作者： Razman, Nur Fatin Shazwani Nor Nasir, Haslinah Mohd Zainuddin, Suraya Brahin, Noor Mohd Ariff Mispan, Mohd Syafiq Pasya, Idnin Wahab, Nur Haliza Abdul Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka76100 Malaysia The University of Aizu Aizu-Wakamatsu Tsuruga Ikki-Machi Fukushima965-8580 Japan Skudai Johor81310 Malaysia

ISBN: (纸本)9798350364507

One of the most important indicators of a person's health status is their rate of respiration, which is why clinical exams should closely monitor it. In the medical field, respiratory signal classification plays a vital role in the diagnosis and management of a range of respiratory conditions. However, effectively categorising respiratory signals remains a considerable issue due to the complexity and diversity of these signals. This study presents a thorough analysis of machine learning models for the categorization of respiratory signals, using a comparative method to assess each model's performance. A variety of machine learning approaches, encompassing both conventional techniques investigated such as Random Forest, Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) to see how well the algorithm classifies the respiratory signals. Processing procedures, data modeling, data classification, and data analysis used in the classification process are all included in the paper. The findings of each model in terms of classification accuracy, computational efficiency, and interpretability are examined through thorough testing on benchmark datasets. SVM was found to execute 92% more accurately than the other ML technique. This study can be expanded to compare with the various deep learning approaches to obtain better classification capable of real time application. © 2024 IEEE.

关键词： Contrastive learning

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Leveraging Deep learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Thresholding based Smart Home Automation System using K-means 13

Thresholding based Smart Home Automation System using K-mean...

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13th international conference on Cloud computing, Data Science and engineering, Confluence 2023

作者： Katiyar, Aakash Singh, Akash Parvez, Mohammad Rajora, Nitin Sharma, Lokesh K. Singal, Gaurav Netaji Subhas University of Technology NSUT Computer Engineering Delhi India Bennett University Greater Noida India Netaji Subhas University of Technology NSUT Delhi India

ISBN: (纸本)9781665462631

Home automation is a technology that is often viewed as a luxury and not a necessity. The means of achieving home automation is always considered expensive and inconvenient in the long run. Due to this there is no success in achieving this technology. Our aim is to make life much easier, more comfortable, and much more convenient and reduce our daily workload. To achieve this, our approach was to make a highly compact and simplified product that would be affordable to middle-class families by using familiar applications like blynk and machine learning - for making the home automation personalized to every specific user. Not only that but also to take utmost care of the privacy of our users. © 2023 IEEE.

关键词： Machine learning

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Mobile application for interactive learning of human anatomy using augmented reality 30

Mobile application for interactive learning of human anatomy...

引用

30th IEEE international conference on Electronics, Electrical engineering and computing, INTERCON 2023

作者： Hernandez Castillo, Juan Carlos Calixto, Gino Salvador Quispe Garcia, Alvaro Antonio Aures Oliden, Daniel Alejandro Subauste Universidad Peruana de Ciencias Aplicadas Software Engineering Lima Peru

ISBN: (纸本)9798350315578

In the last decade, the push for digitalization due to the recent pandemic has led to the constant evolution of different fields. However, in the field of anatomy education, students' learning resources remain the same as they have been used in recent years, as does the lack of technological skills among educators in Peru. With the aim of providing a new alternative for learning human anatomy to the general public and promoting technology, the project aims to design, implement and validate a mobile application solution using augmented reality. This innovative approach helps to address a potential impact on the quality of education in the country. Our research demonstrates the feasibility and benefits of integrating AR into anatomy education such as improved enthusiasm for learning and retention of information versus traditional methods. All of this is reflected in the enhancement of comprehension and educational outcomes. © 2023 IEEE.

关键词： Augmented reality

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Grid Search Hyperparameters Tuning with Supervised Machine learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Medical Prediction App using Random Forest 15

Medical Prediction App using Random Forest

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Benitha Christinal, J. Sneha, N. Brindha, S. Panimalar Engineering College IT Department Chennai India

ISBN: (纸本)9798331300579

This study proposes a machine learning- based diseases prediction algorithm to predict multiple diseases within a single prediction system. This algorithm analyzes an undisclosed patient datasets and applies various features and selection techniques to determine the most notable factors for diseases prediction. Three different machine learning models like logistic regression, Random forest andSVM are used in the process of training and testing the datasets. The indicated by the random forest model is more effective than the other models, achieving an overall accuracy of 85%. The accurate result provided by this model can help the healthcare to identify the high-risk patients and offer targeted prevention and treatment plans. However, further validation and refinement are required toensure the algorithm's effectiveness across different healthcare settings and diverse populations. © Grenze Scientific Society, 2024.

关键词： Prediction models

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HMLM: An Intelligent Artificial Intelligence Assisted Strategy to Identify UPI Frauds Based on Hybrid Markov learning Methodology

HMLM: An Intelligent Artificial Intelligence Assisted Strate...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bharath, S. Prasad, G. Lakshmi Vara Sujatha, V. Hemajothi, S. Mani, D. Sharada Merlin, N.R. Gladiss Rajalakshmi Institute of Technology Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem AndhraPradesh Ongole India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem Andhra Pradesh Ongole India R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331543617

The proliferation of Unified Payments Interface (UPI) transactions in the digital age has considerably improved financial inclusion, but it has also increased the vulnerability to fraud. This manuscript introduces a novel approach that employs Artificial Intelligence (AI) to detect UPI frauds. The approach is based on deep learning and is referred to as the Hybrid Markov learning Methodology (HMLM). To evaluate the suggested system, it is cross-checked against the traditional Hidden Markov Model (HMM). Our approach combines the benefits of modern artificial intelligence methods with Markov models to improve the accuracy of fraud detection. Combining deep learning algorithms with probabilistic state transitions allows the hybrid Markov learning Methodology to dynamically analyze transaction patterns and behaviors. By modeling both historical and real-time transaction data, our approach improves the identification of unusual activity and fraud patterns that conventional algorithms may ignore. From experimental data, we prove our approach minimizes false positives and improves the rate for fraud detection against the current options available. Our work advances the current state of fraud detection systems using more flexible and robust fraud detection systems, thus enabling security and confidence in UPI transactions. © 2024 IEEE.

关键词： Hidden Markov models

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The Landscape of Deep Distributed computing: An In-Depth Analysis 10

The Landscape of Deep Distributed Computing: An In-Depth Ana...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Bhagat, Gautam Mishra, Khushboo Dutta, Tanima Uttar Pradesh Varanasi India

ISBN: (纸本)9798350382471

In the last decade, there has been a significant upsurge in the demand for artificial intelligence. This remarkable growth can be attributed to the advancements in machine and deep learning techniques, coupled with the harnessing of hardware acceleration capabilities. However, achieving higher prediction accuracy and extending the applicability of machine learning to complex tasks necessitates a substantial volume of training data. Smaller machine learning models can effectively train with limited data, but when we transition to larger models such as neural networks, the demand for training data increases exponentially due to the rising number of parameters. As the necessity to process training data has outpaced the growth of computing power in individual machines, there's an urgent need to distribute the deep learning workload across multiple machines, transforming centralized systems into distributed ones. These distributed systems introduce new challenges, with efficient parallelization of the training process and the establishment of a coherent model being of primary concern. This paper provides an extensive overview of the present state of the art. It does so by delineating the challenges and prospects associated with distributed deep learning in contrast with the conventional centralized deep learning approach. Furthermore, it delves into the methodologies employed in deep distributed computing and furnishes a survey of the current systems operating within this domain. © 2023 IEEE.

关键词： Deep Distributed computing Deep learning Distributed computing

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Youtube Transcript Summarizer 5

Youtube Transcript Summarizer

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Panthagani, Vijaya Babu Duggempudi, Vijaya Deepika Reddy Kommera, Naga Lakshmi Vakkalagadda, Nikhita Vignan's Foundation for Science Technology and Research Dept. Computer Science and Engineering A.P Guntur India

ISBN: (纸本)9798350395235

In the modern world, a video transcript summarizer offers a wide range of applications. It draws attention to the crucial points in the video. YouTube videos are watched in large quantities by people for a variety of reasons, including enjoyment, education, obtaining crucial information, and pursuing personal hobbies. Finding a video with any meaningful information about a subject is incredibly tough because most videos are just buffers for unimportant information. The main goal in the majority of situations is to get useful information from the video. In order to generalize YouTube video transcripts for abstractive text summary without sacrificing the essential details and substance, this research has developed a video summarization system based on Natural Language Processing and Machine learning. The goal of this project is to make the writing for the videos shorter. © 2024 IEEE.

关键词： Machine learning

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