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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13394 条记录，以下是121-130 订阅

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A Systematic analysis on Security and Anomaly Detection using machine learning in Cloud computing 6

A Systematic analysis on Security and Anomaly Detection usin...

引用

6th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Tanam, Aparna Raja, G. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798331505790

The term 'cloud computing' describes a way of using the Internet to access resources, software, and databases without being constrained by local hardware. Businesses adopting this technology can grow their operations more flexibly by outsourcing all or a portion of the management of their infrastructure to outside hosting companies. Cloud-based security services are increasingly being used by contemporary enterprises. The dynamic and unpredictable environment, particularly in online applications and apps for the general public, can provide a number of issues even when organizations fully resource their departments. These as-a-service business models let companies outsource a variety of time-consuming IT-related jobs. The traditional intended approaches have significant difficulties with data loss prevention, anomaly detection, and data analysis. Moreover, the current cloud computing system has shortcomings in the areas of causal effect analytics, anomaly detection, and data loss prevention. The machine learning technique greatly increases the effectiveness of anomaly detection in a cloud computing network. This research effort introduces a novel approach to deal with the system's constraints in its current state. The suggested work introduces proactive security mechanisms to stop the loss of sensitive data. A signature system based on machine learning is utilised to detect anomalies. © 2024 IEEE.

关键词： Cloud security

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A Review of Anomaly Detection using machine learning Techniques 1

A Review of Anomaly Detection using Machine Learning Techniq...

引用

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

作者： Kesharwani, Abhishek Shukla, Prashant United University Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350367720

Anomaly detection is different in different areas or context and the definition of anomaly also changes from problem to problem. Classical techniques like classification and clustering are not effective when it comes to identify anomaly in graph structured data. This paper examines various anomaly detection methods, focusing on classification, clustering, time series data, and problems related to graph-based structures. On the basis of existing studies, we have emphasized that anomaly detection algorithms applied on graphs using Graph Neural network are much accurate. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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A Survey on Evolutionary Algorithms Based machine learning Approaches for Medical Diagnosis

A Survey on Evolutionary Algorithms Based Machine Learning A...

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2024 IEEE international conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Hossen, Md. Jakir Ramanathan, Thirumalaimuthu Thirumalaiappan Raja, Joseph Emerson Multimedia University Faculty of Engineering and Technology Melaka Malaysia

ISBN: (纸本)9798331533311

The use of extended time series to gather and store huge datasets has been made simpler by information science and data capture technologies. In many fields, such as astronomy, the environment, economics, business, medical, data analysis, and knowledge mining, these datasets are growing in popularity. Finding hidden patterns, connections, and anomalies in massive databases using statistical analysis and machine learning is known as data mining. You can use this information to help with comprehending complex phenomena, generating predictions, and making decision. The evolutionary computational techniques and machine learning techniques play a main role in the data mining process. This paper reviews various machine learning approaches that are based on different evolutionary algorithms such as genetic algorithm, particle swarm optimization, and ant colony optimization for the classification of medical datasets. The comparison and discussion are made between different evolutionary algorithms-based techniques for the medical data classification process. The review of evolutionary algorithms-based classification approaches showed that the performance of evolutionary algorithms varied according to the medical datasets. The reviewed classification approaches also lead to the conclusion that evolutionary algorithms are essential for the data preprocessing and parameters tuning in machine learning based classifiers. © 2024 IEEE.

关键词： Adversarial machine learning

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Simplifying Quantum Multi-class Classification for the NISQ Era Using Error-Code Output Correction

Simplifying Quantum Multi-class Classification for the NISQ ...

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IEEE international conference on Electro Information Technology (EIT)

作者： Zielinski, Eric Mitra, Dipankar Univ Wisconsin Dept Comp Sci & Comp Engn La Crosse WI 54601 USA

ISBN: (纸本)9798350330656;9798350330649

Within the rapidly evolving realms of artificial intelligence and machine learning, quantum computing has emerged as a pivotal frontier. However, its progress is significantly hindered by the prevailing limitations of quantum hardware. This paper introduces an innovative quantum-classical hybrid algorithm, intricately designed for multi-class classification. The algorithm prioritizes memory efficiency, capitalizing on advanced amplitude encoding techniques and a specialized error-code output correction mechanism to reduce the quantum computational resources needed for data processing while maintaining exceptional speed and classification accuracy. Rigorous testing across a range of datasets, varying in complexities like feature count, class number, and data points, yields notable achievements, particularly in simpler datasets. Nevertheless, the presence of errors and occasional failures with more complex datasets highlights the ongoing need for refinement and optimization, positioning the algorithm toward practical applicability in the field.

关键词： Quantum computing machine learning (ML) Multi-class Classification NISQ and Quantum Circuits

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Bioacoustic data Augmentation Techniques for data-Driven Apiculture

Bioacoustic Data Augmentation Techniques for Data-Driven Api...

引用

2024 IEEE international conference on Future machine learning and data science, FMLDS 2024

作者： Dangwal, Kartikeye Ardekani, Iman Varastehpour, Soheil Sarrafzadeh, Abdolhossein Unitec Institute of Technology Deptartment of Computing and It Auckland New Zealand Creo Center of Excellence North Carolina a&t State University NC United States

ISBN: (纸本)9798350391213

This paper explores the potential of data augmentation techniques to improve the detection of bee activities using machine learning models applied to bioacoustic data. Traditional machine learning methods used in bee bioacoustics often face challenges due to the imbalance in recorded sounds, which can limit the effectiveness of these methods. To address this, we consider a variety of data augmentation techniques, including Generative Adversarial Networks and simpler methods like time-stretching, pitch-shifting, and noise addition. Although our primary focus is swarming detection, these methods are flexible and could be applied to other bee activity or health monitoring systems. While long- and short-term memory networks are used as an example, the proposed augmentation technique is versatile and can be utilized by any machine learning model applied to bee bioacoustics. Our results show significant improvements in model performance, including an increase in accuracy from 67% to 88%, demonstrating the value of data augmentation in enhancing bee bioacoustics analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing machine learning Algorithms for Temperature Forecasting and Energy Management Systems

Optimizing Machine Learning Algorithms for Temperature Forec...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra Sekhar, G. Kedhari Sailaja, P. MadhusudhanaRao, G. Srinivas, N. Sai Thrinath, B.V. Akash, G. Dept. of Computer Science Engg Aeronautical Engineering Hyd Visakhapatnam Dept of Computer Science Engg Institute Visakha Inst. of Engg. & Tech Dept of Electrical and Electronics Engg Vardhaman College of Engg Hyd. Dept of Electrical and Electronics Engg Mohan Babu University Tirupati Dept of Computer Science Engg Woxsen University Hyderabad

ISBN: (纸本)9798350373783

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the machine learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy. © 2024 IEEE.

关键词： Cloud computing data Centres Index: Energy Efficiency Privacy Security Thermal Forecast Virtual machines

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Malicious URL Detection: Comparative Study of machine learning Algorithms 15

Malicious URL Detection: Comparative Study of Machine Learni...

引用

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

作者： Jaswanthi, Thadakaluru Harshitha, Talluri Tanya, Simhadri Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Malicious URLs can be detected by leveraging lexical analysis and machine learning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspicious characters. Research on the detection of malicious URLs suffers challenges in keeping pace with the changing structural landscape of URLs. Adaptation to dynamic patterns and addressing the technical issues of efficient processing and scalability become important to stay one step ahead of the evolving threat landscape. This paper concentrates on addressing these challenges with the integration of machine learning and Lexical Analysis. Features extracted in this process are then fed into machine learning algorithms for the classification of URLs and are tested on unseen data. Research performance is measured using several metrics including Precision, Recall, F1-score and Accuracy during the Model evaluation. Out of all the models experimented, Extra Trees classifier stands out the best with 92% accuracy, showing effectiveness in accurate detection of malicious URL’s. ©2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Stroke Prediction: Leveraging Ensemble learning for Improved Healthcare 15

Enhancing Stroke Prediction: Leveraging Ensemble Learning fo...

引用

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

作者： James, Elizabeth Jacob, Lija Reddy, Kandula Balagangadhar Data Science Lavasa India

ISBN: (纸本)9798350370249

Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. © 2024 IEEE.

关键词： Bagging Ensemble learning machine learning Stacking Stroke Prediction

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A Deep learning Approach for In-Network Synchrophasor Missing data Recovery Using Programmable Network Switches

A Deep Learning Approach for In-Network Synchrophasor Missin...

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2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Guo, Xiaoyu Qu, Yanfeng Chen, Gong Jin, Dong Univ Arkansas Elect Engn & Comp Sci Fayetteville AR USA Illinois Inst Technol Comp Sci Chicago IL USA

ISBN: (纸本)9798350318562;9798350318555

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today's electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

关键词： Phasor Measurement Unit machine learning Programmable Network P4 Smart Grid

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An Intelligent Regression Approach for Weather Forecasting System Using machine learning 1

An Intelligent Regression Approach for Weather Forecasting S...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Das, Piyush Parmar, Pranjal Sahoo, Satyabrat Saluja, Ashish Pande, Sohan Silicon Institute of Technology Computer Science and Engineering Sambalpur India

ISBN: (纸本)9798350361780

Accurate weather forecasting is imperative across various sectors, impacting decisions in agriculture, transportation, and emergency planning. Conventional methods often struggle with the intricate patterns in weather data, limiting accuracy. In this study, we harness the power of machine learning to address these challenges and enhance the precision and reliability of weather predictions. Our main contribution lies in leveraging machine learning algorithms to analyze weather data effectively, aiming for superior accuracy compared to traditional methods. Through this approach, we strive to create a weather forecast model that offers more dependable predictions, thereby facilitating informed decision-making in diverse industries and daily life. © 2024 IEEE.

关键词： Weather forecasting

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