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检索条件"主题词=Machine Learning Algorithms"

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Unveiling machine learning Paradigms for Robust Malware Detection in Personal Data Security

Unveiling Machine Learning Paradigms for Robust Malware Dete...

引用

Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Tejanshu Bhandari Rohi Velgina Romould Mahendra Kumar Gourisaria Vinayak Singh Rajdeep Chatterjee Dayal Kumar Behera School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India GE Healthcare Bengaluru Karnataka India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

In the world of computers and software, data is an asset; a personal, valuable, and important component in every person’s life. Malware used to extract hidden and personal data is slowly spreading around the globe. Conventional signature-based malware detection techniques are finding it difficult to keep up with the constant growth of malware. A viable substitute is machine learning (ML), which can recognize intricate patterns and adjust to new threats. This study compares 11 different machine learning models, to find the best model that can detect malware. The algorithms are trained and tested on a dataset called TUNADROMD with and without using Principle Component Analysis (PCA). The Random Forest is found to be the best-performing model with and without PCA with accuracies of 0.99 and 0.98 respectively. The worst-performing model is found to be Logistic Regression both with and without PCA with accuracies of 0.98 and 0.81 respectively.

关键词： Logistic regression Accuracy machine learning algorithms Computational modeling Data security Forestry Malware

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Cyber Hacking Breaches Prediction and Detection Using machine learning 2

Cyber Hacking Breaches Prediction and Detection Using Machin...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Santha Sheela K Mrithula M Varshni Priya A Selvalakshmi C.B Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Cyber hacks threaten organizations of every kind around the world; thus, they surely require stringent methodologies that would predict and detect them. In this paper, we discuss machine learning approaches to enhance identification and forecasting of a cyber attack by analyzing historical data. Several machine learning approaches such as decision trees, support vector machines, and neural networks are applied in sequence to extract patterns and anomalies related to potential breaches. It develops predictive models for identifying vulnerabilities and discovers features from network traffic, system logs, and user behavior up in near-real time. Metrics of performance such as accuracy, precision, and recall evaluate these algorithms to compare the effectiveness of established and emerging attack vectors. The findings suggest that machine learning significantly enhances the speed and accuracy of breach detection when compared with traditional methods. This advanced technique, applied in an organization's framework of cybersecurity, predicts cyber threats effectively and neutralizes them to increase resilience in overall security.

关键词： machine learning algorithms Accuracy machine learning Organizations Telecommunication traffic Predictive models Feature extraction Prediction algorithms Vectors Threat assessment

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Cardio Prognosis: machine learning Approaches to Heart Disease Prediction

Cardio Prognosis: Machine Learning Approaches to Heart Disea...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Laxmipriya Rout Amritakhi Mohapatra Arpita Mohapatra Swarna Prabha Jena Bijay Kumar Paikaray Mangaldeep Chakraborty Department of CSE Centurion University of Technology and Management Odisha India Department of ECE Centurion University of Technology and Management Odisha India Department of CSE Center for Data Science Siksha ‘O’ Anusandhan (Deemed to be University) Odisha India Department of E&TC Bishnupur Public Institute of Engineering West Bengal India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Global mortality is high due to cardiac disease, which affects heart anatomy and function. Timely identification improves results and lowers healthcare costs. This study uses machine learning (ML) to analyze patient data risk variables and patterns to construct a heart prediction system. This approach will enhance cardiac disease prediction and reduce misdiagnosis. In general, the datasets are heterogeneous and scattered. Therefore, cardiovascular prognostic data is needed. Numerous studies have examined heart disease types, algorithms, applications, and treatments. This study uses Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest, Support Vector machine, and Logistic Regression to develop heart disease prediction models. Other algorithms include pipeline, cross-validation, hyperparameter optimization, and EDA. Logistic regression, with 99% accuracy, outperforms.

关键词： Heart Support vector machines machine learning algorithms Accuracy Medical services Predictive models Nearest neighbor methods Prediction algorithms Optimization Diseases

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SENSEI: A Novel Architecture of Deep Multi-Task Neural Network for Analyzing E-learning Lecturers’ and Students’ Satisfaction

SENSEI: A Novel Architecture of Deep Multi-Task Neural Netwo...

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International Conference on Informatics and Computing (ICIC)

作者： Sulis Sandiwarno Dana Indra Sensuse Harry Budi Santoso Deden Sumirat Hidayat Muhammad Mishbah Faculty of Computer Science Universitas Indonesia Depok Indonesia Universitas Mercu Buana Jakarta Indonesia Researcher at National Research and Innovation Agency (BRIN) Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

E-learningtechnology is widely acknowledged as a crucial component of education. Researchers conduct extensive studies to analyze lecturers’ and students’ effectiveness and satisfaction of e-learning systems via interaction called usage and usability (i.e., SUS) metrics. In a recent empirical study, users’ interactions with an e-learning environment were examined based on a predefined task model that describes low-level interactions. However, the usage-based and usability metrics do not consider opinions of users related to e-learning. Additionally, machine learning algorithms have been widely used for this purpose to analyze satisfaction via opinions. However, they ignore analyzing users’ satisfaction through usage and SUS metrics, and emotions. In this study, we introduce a novel architecture of multi-task deep neural network coined as Satisfaction and Emotion Neural System for E-learning Improvement (SENSEI) for analyzing users’ satisfaction. The SENSEI combines deep neural network including CNN and BiLSTM algorithms to analyze satisfaction. The experimental results indicate that our proposed model effectively analyzes users’ satisfaction by achieving an average F1-score of $85.51 \%$.

关键词： Measurement Electronic learning machine learning algorithms Education Bidirectional long short term memory Computer architecture Artificial neural networks Multitasking Usability Informatics

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Research on the Application of machine learning in Wet Waste Treatment Project

Research on the Application of Machine Learning in Wet Waste...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Yanling Niu Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. Shanghai China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Among the various stages of wet waste treatment, anaerobic fermentation is particularly important. In this study, machine learning methods were employed to predict methane production using the random forest algorithm, and gas production was predicted using the gradient boosting tree algorithm. These predictive results will be fed back to the production process to improve processing efficiency. Data from the SCADA system were collected and cleaned, and normalized for model training. The experiments demonstrated that these machine learning models can provide accurate and reliable predictions.

关键词： Radio frequency Methane machine learning algorithms Production Predictive models Prediction algorithms Boosting

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Predicting Initial Coin Offering Success Using machine learning and Human Capital Analysis

Predicting Initial Coin Offering Success Using Machine Learn...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Khouloud Safi Eljil Farid Naït-Abdesselam LIPADE EA 2517 Université Paris Cité Paris France

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Initial Coin Offerings (ICOs) have emerged as a groundbreaking method for blockchain startups to raise capital, allowing projects to generate significant funding through the issuance of digital tokens. Despite their potential to raise millions, often outpacing traditional approaches like Initial Public Offerings (IPOs), ICOs remain high-risk investments due to limited regulatory oversight and a lack of standardization. This study investigates the impact of human capital on ICO success, employing machine learning techniques, including advanced boosting algorithms, to enhance predictive accuracy. Using data from 500 ICOs and analyzing over 5,524 LinkedIn profiles of project team members and advisors, we demonstrate that human capital factors can substantially inform success predictions. Our model achieves a notable 72% prediction accuracy, offering valuable insights amid the inherent volatility and unpredictability of ICO markets. These findings underscore the role of human capital in ICO outcomes, providing a data-driven framework for stakeholders to assess ICO prospects.

关键词： machine learning algorithms Accuracy Social networking (online) Heuristic algorithms Standardization Predictive models Prediction algorithms Boosting Stakeholders Investment

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Sign Language Recognition Using machine learning

Sign Language Recognition Using Machine Learning

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M Soundarya M Yazhini Ns Thirumala Sree Nm Sornamalaya C Vinitha Department of IT Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The community of the deaf and hearing-impaired uses sign language as the primary but not exclusively dominant mode of communication. A strong system that can translate spoken language into sign language and vice versa must be built for the hearing and hearing-impaired groups to communicate easily and mutually. Currently, a sensor-based approach utilizing glove sensors and built-in sensors, along with CNN (Convolutional Neural Network) algorithms for image processing, is employed for recognizing sign language gestures. The proposed method for recognizing sign language is using LSTM (Long Short-Term Memory), a kind of RNN that can recognize long-term dependencies, is used in the development. Apart from single data points like photos, which can process the full data stream The system can be utilized for machine translation and speech recognition, among other things. The suggested system offers a substantial contribution in that it uses advanced technology to facilitate communication between the hearing and hearing-impaired groups. The system provides precise gesture identification in sign language by LSTM algorithms, removing obstacles to efficient communication. By achieving accuracy levels between 90 and 100%, the system can effectively comprehend the complex nature of sign language by utilizing LSTM algorithms to capture long-term dependencies in sequential data.

关键词： Training Image sensors Sign language machine learning algorithms Face recognition Symbols Auditory system

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Developing Verifiable Computations and Homomorphic Encryption to Promote Federated learning

Developing Verifiable Computations and Homomorphic Encryptio...

引用

International Conference on Advances in Information Technology (ICAIT)

作者： Vikas Maurya Indrajeet Kumar Nitin Kumar School of CSE Galgotias University Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This paper proposes creative solutions to address security issues in federated learning, particularly regarding malicious nodes and confidentiality protection for the global model. It employs recently created cryptographic techniques such as homomorphic encryption and zero-knowledge virtual machine (ZKVM). By utilizing ZKVM, it demonstrates integrity verification for multi-class machine learning algorithms with local computing testers. The use of fully homomorphic encryption preserves the confidentiality of the global model during computation and transmission. The study shows a novel federated learning framework and analyses its performance using the IRIS dataset, achieving 90% model accuracy. Despite potential computational cost from encryption, the system shows high security and predicts further efficiency improvements with advancements in cryptography.

关键词： Training Adaptation models Privacy Accuracy machine learning algorithms Federated learning Computational modeling Computational efficiency Homomorphic encryption Protection

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SoulNourish: A Comprehensive machine learning Based Recommendation Yoga Prediction System Based on Health and Lifestyle Data

SoulNourish: A Comprehensive Machine learning Based Recommen...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Kirankumar M Saikiran P Sabarivasan N Manoj Kumaran S Senthil Kumar Thangavel K Somasundaram B Senthil Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Amrita Darshanam-International Centre for Spiritual Studies Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The modern sedentary lifestyle has escalated health risks, underscoring the importance of tailored health solutions like yoga. This paper pioneers a method to determine yoga'srelevance for individuals by analyzing their lifestyle data. Using machine learning algorithms like Random Forests, Gradient Boosting, SVMs, and Neural Networks and KMeans with Convo-lution Neural Networks. We process data on activity, diet, stress, and sleep to evaluate yoga's potential benefits. We implementthe algorithms for recommending the customized yoga routines. Our experimental results show that Decision Trees give goodaccuracy in identifying candidates for yoga, offering a novel perspective for personalized health interventions and underscoring machine learning's role in enhancing public health.

关键词： machine learning algorithms Neural networks Predictive models Prediction algorithms Data models Decision trees Public healthcare Expert systems Stress Random forests

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Detection of Rumors in Social Network Based on machine learning Algorithm

Detection of Rumors in Social Network Based on Machine Learn...

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Delhi Section Conference (DELCON), IEEE

作者： Shivangi Mishra Ashwani Kumar Avinash Kumar Sharma Yojna Arora Computer Science & Engineering SRM Institute of Science & Technology New Delhi India School of Computer Science Engineering & Technology Bennett University Greater Noida UP India School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In this time many people who use smartphones like to read news online on social media platforms. There are various ways to give the news. The problem is determining how to verify the facts and stories that are scattered across social media websites like Facebook Pages, WhatsApp Groups, and Twitter. People should not consider such claims and should not act as though they believe the information to be true. This paper discusses method for detection of rumors in social network. The desire is to stop spreading rumors, especially in a developing nation like India, and focus on reporting accurate news. A model for identifying bogus news is provided in this manner. In order to create a classifier that can draw conclusions about data based on the corpus's content and utilize a text classification method, employing a classification model, and investigate the results, we are using a corpus of labelled true and fake news items. In this research paper we have used decision tree with 99.63 percent accuracy.

关键词： Freeware Accuracy machine learning algorithms Social networking (online) Text categorization Data models Decision trees Fake news Internet telephony Smart phones

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