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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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Sarcasm Detection in Newspaper Headlines 4

Sarcasm Detection in Newspaper Headlines

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. This study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 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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Fraud Detection In Ethereum Transactions: A Machine learning Approach 1

Fraud Detection In Ethereum Transactions: A Machine Learning...

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1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Neogi, Anandarupa Mukhopadhyay, Disha Jaiswal, Anubhav Kumar, Ankush Misra, Bitan Techno International New Town Computer Science and Engineering Kolkata700156 India

ISBN: (纸本)9798350367720

This study explored the use of machine learning models to detect fraudulent transactions made over Ethereum, a type of cryptocurrency. The dataset included data from 9840 transactions which included both fraudulent and legitimate transactions and included various parameters such as average, minimum and maximum amounts received and sent, the number of unique addresses to and from which ether was sent and received, the time duration between the transactions and the remaining balance after the transactions. The transaction amount lies in a range of 12000.0 to 52000.0. Four machine learning models, namely Decision Tree, Random Forest Classifier, Catboost Classifier and XGBoost Classifier, are evaluated for fraud transaction detection. XGBoost has emerged as the most promising model, achieving near perfect accuracy (99.65%) and high precision, recall and F1-scores. Notably the robust performance of XGBoost suggests it's potential for accurately detecting fraudulent transactions in Ethereum. However, comparatively weaker performance is observed in models like the Decision Tree and Random Forest. This study underscores the significance of machine learning in enhancing fraud detection in Ethereum transactions and advancement of the field of cybersecurity in blockchain technologies by providing a useful and scalable fraud-detecting instrument. © 2024 IEEE.

关键词： Ethereum

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Research on Data Drift and Class Imbalance in Android Malware Detection 20th

Research on Data Drift and Class Imbalance in Android Malwar...

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20th EAI international conference on Mobile and Ubiquitous Systems - computing Networking and Services (EAI MobiQuitous)

作者： Liu, Zhen Wang, Ruoyu Peng, Bitao Wang, Changji Gan, Qingqing Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China South China Univ Technol Informat & Network Engn Res Ctr Guangzhou 510041 Peoples R China

ISBN: (纸本)9783031639883;9783031639890

In the Android ecosystem, malware detection is still a nontrivial task. Existing works have recently applied convolution neural networks (CNNs) for detecting Android malwares. However, data drift and class imbalance are still open problems in this field. The distribution of malware data may vary significantly if data are represented by unstable features, leading to data drift problems. The model may not be able to effectively detect malwares on the future data. In addition, the class imbalance may degrade a model on identifying a specific type of malwares with fewer training samples. To handle both of the two problems, this paper presents a new Android malware detection framework. Specifically, we devise a data distribution-aware feature learning framework for learning features with a stable distribution to handle data drift. We further devise a new loss function for CNN to handle the class imbalance problem. Using our loss function, this model can reinforcement learn the minority class samples and hard samples. The experimental results on the real datasets revealed that our method outperforms existing works for Android malware detection on the datasets with data drift and class imbalance problems.

关键词： Android malware detection data drift class imbalance feature learning CNN

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The Application of Multisim Simulation in the Course of 'Signal and System' 4

The Application of Multisim Simulation in the Course of 'Sig...

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4th international conference on Electronics, Circuits and Information engineering, ECIE 2024

作者： Zhang, Jing Tao, Binbin Wanjiang University of Technology School of Telecommunications Engineering Anhui Ma'anshan China Wanjiang University of Technology School of Innovation and Industrial Engineering Anhui Ma'anshan China

ISBN: (纸本)9798350388312

The theory of the course 'Signals and Systems' is abstract, with complex formulas, numerous concepts, and difficult content. Students face numerous difficulties in learning, making it difficult for them to connect theory with practical applications. In order to deepen students' understanding and application of complex theories and improve teaching effect, Multisim circuit simulation software is applied in this paper to the teaching of the course 'Signals and Systems', and the understanding of the theory is deepened through the example simulation. Through Multisim, the circuit simulation of periodic signal decomposition and frequency shift characteristics in the course are carried out. The theory is presented to students intuitively and vividly, so that students can not only have a good understanding of the theory, but also realize the connection between theory and practice, and enhance their ability of circuit simulation. © 2024 IEEE.

关键词： Students

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Parity-Based Amplitude Embedding for Rational Quantum Dataset Preprocessing 1

Parity-Based Amplitude Embedding for Rational Quantum Datase...

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1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Prasetiadi, Agi Mambo, Masahiro Kanazawa University Graduate School of Natural Science and Technology Kanazawa Japan Kanazawa University Institute of Science and Engineering Kanazawa Japan

ISBN: (纸本)9798350366778

Quantum dataset preprocessing for amplitude embedding is understudied despite its importance for achieving good generalization in quantum machine learning models. Amplitude embedding, known for its compression capabilities, has garnered attention in recent research. Combined with the Variational Quantum Classifier, they form a potential combination for high performance in quantum machine learning, outperforming many classical models. However, the full normalization of amplitude embedding has fundamental issues, such as inconvertibility for specific values and irrational amplitude distribution due to normalization. To overcome these challenges, we propose a novel and straightforward dataset preprocessing solution using guardian parity designed for amplitude embedding. This method ensures the normalized dataset remains recoverable to its original MinMax scaled values, simultaneously enhancing dataset quality and improving accuracy when employed in quantum machine learning models. Experimental results show that the proposed parity-based amplitude embedding significantly impacts the accuracy of machine learning models compared to the original amplitude embedding method. © 2024 IEEE.

关键词： Quantum computers

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Assessment Technology for Physiotherapy Practices using Deep learning 2

Assessment Technology for Physiotherapy Practices using Deep...

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2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Deep learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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A Systematic Literature Review on the Feasibility of Dialogue learning Based on Generative AI 24

A Systematic Literature Review on the Feasibility of Dialogu...

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2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Tian, Yusen Tu, Binghao College of Educational Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9798400711237

This study systematically reviews the feasibility of dialogue learning based on generative AI. Research on mixed reality and generative AI in enhancing teaching effectiveness is increasingly in-depth both domestically and internationally. The development and application of mixed reality and generative AI are accelerating, evolving classroom structures and methods. In the context of rapid social, economic, and technological development, students require more personalized and innovative education, which dialogue learning interventions based on generative AI can effectively address. Through a systematic literature review method, 128 relevant studies from the past decade were systematically analyzed to explore the feasibility of dialogue learning in the context of generative AI. The study finds that the application of generative AI in dialogue learning not only enhances learning outcomes but also excels in personalized learning and timely feedback. However, the diversity and operability of technological implementation and application scenarios require further exploration. This research provides references and insights for future studies by related scholars. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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