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检索条件"主题词=Machine learning algorithms"

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Integrated machine learning Approach for Attack Detection in MQTT-Enabled Smart Home Systems

Integrated Machine Learning Approach for Attack Detection in...

引用

Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Abhay Kumar Ray Rupak Sharma Sunil Kumar Pandey Department of Computer Applications SRM-Institute of Science & Technology Modinagar Ghaziabad Department of IT Institute of Technology & Science Mohan Nagar Ghaziabad

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

With the worldwide rising demand for smart home technologies and home automation, which ensures better privacy and security by using IoT devices in these environments has become more important. This research paper discusses and explores the MQTT protocol, which is an efficient and lightweight messaging protocol commonly used in IoT systems for communication. To identify and mitigate cyber-attacks in smart home systems, this study applies a set of machine learning algorithms to analyze MQTT attack dataset, which aims to detect and classify various cyber-attacks using multiple machine learning approaches with high accuracy and combine their prediction to find out the final result. The study begins with an introduction of smart home, its components, basic introduction, architecture of MQTT protocol, and major attack vectors targeting MQTT-based systems, highlighting vulnerabilities that can be exploited by attackers. Afterward this research paper proposes and implementing a model which uses a set of machine learning algorithms on MQTT attack dataset to train and test models, these become capable to identify anomalies in MQTT traffic, which enables the detection of attacks such as dos attack, flooding packet attack, SlowITe Attack and brute force attack for unauthorized access of servers or systems. Feature selection and data standardization done, all trained and tested models demonstrate high accuracy and effectiveness in threat detection and classification on a comprehensive MQTT dataset. This study illustrates the effectiveness and good application of AI/ML-driven approaches for security leak prevention which is boosting the smart home environments security attack detection and contributing towards the secure IoT ecosystems.

关键词： Technological innovation Protocols machine learning algorithms Accuracy Smart homes Vectors Threat assessment Security Internet of Things Cyberattack

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EEG Based Identification of learning Disabilities using Deep learning algorithms

EEG Based Identification of Learning Disabilities using Deep...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： S Santhiya N Abinaya P Jayadharshini M N Dharshini B Dinesh Kumar K Sandeep Department of Artificial Intelligence Kongu Engineering College Erode India Department of Computer Science and Engineering Hindustan Institute of Technology Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A learning disability (LD) encompasses various learning challenges, characterized by difficulties in understanding words and poor reading skills. It affects many school-aged children, especially boys, often leading to poor academic performance and low self-esteem. Early detection and intervention are crucial in mitigating these adverse effects. Our research aims to develop a deep learning model to analyze EEG signals from individuals with learning difficulties, providing highly accurate results quickly. We employed Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) for component analysis of the dataset and used deep learning algorithms such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN) for classification. The goal is to determine the most effective combination of data preprocessing techniques and deep learning algorithms for detecting learning disabilities.

关键词： Deep learning machine learning algorithms Dyslexia Prediction algorithms Feature extraction Graph neural networks Electroencephalography Classification algorithms Convolutional neural networks Long short term memory

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Exploring machine learning to Predict Brain Tumour Disease

Exploring Machine Learning to Predict Brain Tumour Disease

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sukrati Sharma Sunaina Basu Dev Shivahare Kirti Singh Prabhishek Singh Vimal Bibhu Just Dial Bangalore Karnataka India Department of Mathematics Amity University Greater Noida Uttar Pradesh India School of computer science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of computing science & Engineering Bennett University Greater Noida Uttar Pradesh India School of Engineering & Technology Noida International University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The demand of AI is increasing day by day in the medical field. machine learning (ML) is subset of AI. ML has become popular tool and widely used to detect several medical diseases at higher accuracy. Promising accuracy of ML algorithms depend on training of digital images like CT, MRI, X-rays etc. In this paper, CNN model is proposed to classify brain tumour into malignant and non-malignant class and comparative study using diverse ML algorithm is conducted and their accuracy is compared. This paper explores role of ML in clinical diagnosis, accurate prediction and clinical analysis regarding brain tumour disease based on various brain tumour dataset like figshare, brats, Kaggle. Proposed CNN model has achieved 98 % promising accuracy.

关键词： Accuracy machine learning algorithms Magnetic resonance imaging Computed tomography machine learning Brain modeling Clinical diagnosis Medical diagnostic imaging Tumors Cancer

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Efficient Relay Selection in NOMA-Based Cooperative Networks: A Supervised learning Approach With Random Forest

Efficient Relay Selection in NOMA-Based Cooperative Networks...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Nishant Gaurav Krishan Kumar Abhishek Dhiman ECE Department NIT HAMIRPUR (H.P) INDIA CSE Department NIT HAMIRPUR (H.P) INDIA

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

In this research, we examine how machine learning is used to cooperative communication networks' relay selection, emphasizing transmission under non-orthogonal multiple access. We introduce a novel relay selection algorithm based on Random Forest and compare its performance with SVM method previously discussed in this context and enhanced two- stage max-min selection rule. According to the study results, the Random Forest algorithm shows improved outage probability and system throughput even when the signal regime is more difficult. The superiority of the presented algorithm lies in its ability to combine NOMA and OMA transmission sources in an intelligent way, as well as ensure adaptability even in more complex situations due to high-dimensional classification. This work provides readers with valuable experience in applying machine learning methods to improve communication systems.

关键词： Support vector machines NOMA machine learning algorithms Supervised learning Signal processing algorithms Signal processing Throughput Classification algorithms Relays Random forests

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Exploring machine learning and Deep learning for Brain Tumor Detection: A Review

Exploring Machine Learning and Deep Learning for Brain Tumor...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Uma Vishwakarma Pritaj Yadav Anurag Jain INDIA

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Brain tumor detection has emerged as a critical field of research, given the increasing incidence of tumors and their impact on patients' lives. Recent advancements in machine learning (ML) and deep learning (DL) have revolutionized medical imaging, offering enhanced accuracy and efficiency in diagnosing brain tumors. This review comprehensively explores various ML and DL techniques employed for brain tumor detection, with a focus on supervised and unsupervised learning methods. Key algorithms such as convolutional neural networks (CNNs), random forests, and support vector machines (SVMs) are evaluated in terms of their classification performance, segmentation capabilities, and diagnostic accuracy. Moreover, the paper highlights the role of pre-processing methods, feature extraction techniques, and the challenges of imbalanced datasets in achieving robust models.

关键词： Deep learning machine learning algorithms Accuracy Reviews Brain tumors machine learning Brain modeling Feature extraction Planning Medical diagnostic imaging

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machine learning Techniques for Eeg-Based Neuromarketing: A Systematic Literature Review

Machine Learning Techniques for Eeg-Based Neuromarketing: A ...

引用

International Conference on Web Research (ICWR)

作者： Aryan Azimi Mohammad Parsa Afshar College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Understanding consumer behavior has become important for companies as it can provide insights into consumer preferences and their future decisions, which can be useful in designing effective marketing strategies. Traditional data-gathering methods such as surveys, interviews, and focus groups have some limitations; so, neuromarketing-based approaches can be used to collect consumer brain data and reduce the biases of previous methods. Neuromarketing uses various techniques to measure brain activity, and one of them used in various research is EEG signals. Because of their characteristics, EEG signals have a significant application in neuromarketing. However, because these signals are often complex and analyzing them is time-consuming, machine learning and deep learning methods can be used to analyze user responses to stimuli in neuromarketing research more efficiently. This paper provides a systematic review of previous studies related to analyzing and processing EEG signals in neuromarketing using machine learning methods to have an overview of these methods, evaluate previous research, identify the gaps, and offer suggestions for future studies.

关键词： Deep learning Surveys machine learning algorithms Neuromarketing Predictive models Brain modeling Electroencephalography Reliability Systematic literature review Overfitting

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Remote Sensing and IoT based Global Crop Disease Detection and Treatment System using machine learning

Remote Sensing and IoT based Global Crop Disease Detection a...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Ummaka Venkatesh Ummaka Subba Rao N. Sugitha ECE Saveetha Engineering College

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

A global crop growth monitoring system based on remote sensing technologies, Arduino-based hardware and machine learning for accurate plant disease diagnosis and management has been proposed. It consists of a set of sensors such as soil moisture and humidity connected to an Arduino platform, which monitors environmental factors critical for the sustenance of crops. The data is sent in real time to a web page through an Internet of Things (IoT) network that allows the monitoring or even decision making from remote. In this method, a MATLAB-based machine learning algorithm is implemented on the system which predicts diseases in crops like Pepperbell (Bacterial Spot), Potato (Early Blight) and Grapevine (Esca or Black Measles) with very high accuracy. The system is designed to automatically identify and treat crops affected by disease conditions, using pump motors to dispense specific medicines when the appropriate condition is identified in the plant. By avoiding chemicals for disease management, it maximizes the efficiency of disease management with a focus on sustainability in agriculture and minimizing the usage of chemical inputs. In this manner, the adaptation and expansion of crop monitoring strategies in different environments by utilizing remote sensing, machine learning, and IoT are facilitated with the help of this system which ultimately supports sustainable food security on a global scale while also optimizing crop yield.

关键词： machine learning algorithms Accuracy Crops machine learning Sensor systems Sensors Monitoring Remote sensing Chemicals Diseases

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Trust-Based Data Fusion and machine learning for Underwater Sensor Networks

Trust-Based Data Fusion and Machine Learning for Underwater ...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Anita Kanavalli Shilpa Shashikant Chaudhari Bharathi S Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Underwater Wireless Sensor Networks (UWSNs) are essential for a wide range of marine applications, from environmental monitoring to underwater exploration. However, significant obstacles such signal attenuation, multipath propagation, and sensor node mobility make it difficult to sustain flawless communication in dynamic underwater environments. In UWSNs, handover prediction becomes essential for maintaining continuous communication and maximizing the effectiveness of data transfer. This research presents a novel method for precise handover prediction in UWSNs by utilizing machine learning algorithms and real-time maritime data. The system attempts to predict Energy Trust, Environment Trust, and Communication Trust events by examining environmental parameters and past handover events, improving communication efficiency and reliability in dynamic underwater environments.

关键词： Wireless sensor networks Technological innovation machine learning algorithms Data integration Handover Data models Real-time systems Robustness Environmental monitoring Ensemble learning

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Analysis of Ensemble machine learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Siddharth Chaurasia Satyam Kesharwani Shanu Sharma Swati Sharma Bharti Chugh Dept. of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder Data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud.

关键词： Industries machine learning algorithms Europe Forestry Credit cards Fraud Task analysis

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Predictive Analytics in Cardiology: machine learning Techniques for Heart Disease

Predictive Analytics in Cardiology: Machine Learning Techniq...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Vishal Kumar I B Darshan B S Trupthi Rao Ashwini Kodipalli Dept. of Artificial Intelligence and Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

According to recent studies by the WHO, there is a growing prevalence of conditions associated with coronary heart disease, resulting in approximately 17.9 million deaths annually. With an expanding population, early identification and treatment of such issues become increasingly challenging. Nevertheless, advancements in machine learning techniques have shown promising results in addressing these challenges. Hence, the focus of this paper is to develop a machine-learning model for predicting heart disease based on relevant features. To achieve this, the UCI Heart Disease Prediction dataset is utilized, comprising 14 parameters related to heart health. Various machine learning algorithms including Random Forest, Support Vector machine (SVM), Naive Bayes, and Decision Tree are employed in the model's development. Additionally, we explore correlations among the dataset features using established machine-learning techniques to enhance predictive accuracy. The results indicate that Random Forest outperforms other algorithms in terms of both accuracy and computational efficiency. As a decision support tool, this model holds promise for aiding clinical practitioners in their facilities.

关键词： Heart Support vector machines Accuracy machine learning algorithms Computational modeling Training data Vectors Partitioning algorithms Random forests Diseases

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