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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是211-220 订阅

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Streamlining IoT Malware Detection:A Pipeline Based Approach 3

Streamlining IoT Malware Detection:A Pipeline Based Approach

引用

international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Naresh, G. Chandu, M. Mohan Spandana, H. Sai Naik, J. Tulasi Prasad Kumar, M. Anil Mohan Babu Univ Dept ECE Erstwhile SreeVidyanikethan Engn Coll Tirupati India SreeVidyanikethan EngineeringColl Dept ECE Tirupati India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Internet of Things (IoT) malware detection has become a significant cybersecurity challenge due to the expansion of the threat landscape brought about by the proliferation of IoT devices. IoT malware is growing more complex and dynamic every day, making traditional signature-based detection techniques ineffective against it. By using large-scale data generated by IoT devices to identify patterns and anomalies, machine learning (ML) techniques present a promising way to detect IoT malware. The process of developing effective machine learning models to identify malware for Internet of Things, however, can be difficult and time-consuming owing to the requirementfor meticulous preprocessing,feature engineering, model selection, and ***, a pipeline techniquecombined with machine learning to detect IoT malware is presented in a comprehensive way. In order to increase the resilience and accuracy of the detection system, specific methods are used like data cleaning, dimensionality reduction, ensemble learning and cross validation. Standard Dataset UNSW_NB15 is used to evaluate proposed models like XGBoost Classifier, AdaBoost Classifier, Random Forest, ExtraTrees Classifier and Stochastic Gradient Descent(SGD).

关键词： IoT Malware machine learning pipeline technique feature enhancement

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4th international conference on machine learning, advances in Computing, Renewable Energy and Communication, MARC 2023

4th International Conference on Machine Learning, Advances i...

引用

4th international conference on machine learning, advances in Computing, Renewable Energy and Communication, MARC 2023

ISBN: (纸本)9789819752300

The proceedings contain 33 papers. The special focus in this conference is on machine learning, advances in Computing, Renewable Energy and Communication. The topics include: Enhancing Power Quality in Grid-Tied Solar Photovoltaic systems;RF-TSVM: Random Forest-Based Transductive Support Vector machine for Classification and Prediction of Cancer Patterns;resource-Efficient Image Retrieval: A Study of Local Patterns Versus Deep learning Models;machine Translation of Chinese–Hindi Simple Sentences Using Moses;Innovative Approaches to Reduce Carbon Footprint and Air Pollution: The Role of AI, ML, Cloud Computing, and IoT;investigating Sensor Technology and Benefits of Intelligent Transport systems;student Attendance System by Quick Responsive Code;analysis of Brain Tumor Detection Using machine learning;a Review on Multiple Face Detection Techniques and Challenges;blockchain-Enabled Secure Identity Verification in Agri-Food Supply Chain;machine learning Approach for Diagnosis of Schizophrenia Using EEG Signals;Comparative Analysis of Web APIS: RESTful and GraphQL;AI-Based Vision Screening Tool for Keratoconus;synergizing Artistry and Technology by Unveiling the Integration of Matte Painting Techniques in Crafting Precise and Immersive Visual Effects Backgrounds;Heart Disease Prediction: A Comprehensive Exploration of Optimal Predictive AI;dynamic Animation Scaling: Design and Development of Adaptive Character Animations for Varying Sizes;framework for Assessment of Greywater-Assisted Composting Using IoT-Based Sensors;mood-Based Movie Recommendation System Using Sentiment Analysis;blockchain-Enabled Consensus Mechanisms for Data Integrity and Security in Edge and Cloud Computing Environments;AI-Based Question Paper Analysis and Generator with Authentication;pomegranate Leaf Fruit Disease Prediction Using machine learning;price Prediction Using machine learning Approaches.

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IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

IEEE International Conference on Recent Advances in Science ...

引用

2024 IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

ISBN: (纸本)9798350388602

The proceedings contain 177 papers. The topics discussed include: weather prediction model using ridge regression;continuous gesture recognition in human-computer interaction through real-time machine learning models;an effective implementation and comparative analysis of mobile robot path planning algorithms;advanced machine learning algorithms for predictive analytics in healthcare to enhance patient outcomes with data-driven insights;blockchain-powered decentralized resource allocation and management for cloud computing;an hand gesture smart translation system for differently abled people;smart iridology: deep learning for predictive health insights;and early detection of stem bleeding in coconut trees using image processing.

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Fuzzy Logic-Based Deep learning for Human-machine Interaction and Gesture Recognition in Uncertain and Noisy Environments 1

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Anand, John Thamilarasi, V. Rayal, Ashish Gupta, Himanshu Kumar Jyothi, Kamboji Vishwakarma, Pramatma Greater Noida201310 India Department of Computer Science & Engineering Tamilnadu Salem-16 India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India Chaitanya Bharathi Institute of Technology Department of Mathematics AP Proddatur India Lovely Professional University Department of Computer Aplication Punjab 144411 India

ISBN: (纸本)9798331518578

Conventional deep learning methods have considerable hurdles when it comes to Human-machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments. © 2025 IEEE.

关键词： Contrastive learning

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Analyzing Employee Retention Factors using machine learning 4

Analyzing Employee Retention Factors using Machine Learning

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4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Islam, Md Rashedul Bala, Partho Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh Daffodil Institute of Information and Technology Department of Computer Science and Engineering Dhanmondi Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Computer Science and Engineering Dhaka8100 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance. © 2024 IEEE.

关键词： Artificial Intelligence Data Science Deep learning machine learning Monitoring Smartphone Sensors

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machine learning in Predictive Maintenance for Industrial Equipment 1

Machine Learning in Predictive Maintenance for Industrial Eq...

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1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Saurabh Pratap Singh Gupta, Anjali Parashar, Akshat Upadhyay, Dheeresh Deb, R.K. Gupta, Abhinav ICAPSR Dept. of Management New Delhi India Madhav University Dept. of Pharmacy Pindwara India Mangalayatan University Dept. of EEE Aligarh India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

The integration of machine learning into predictive maintenance strategies has revolutionized how industries manage equipment reliability & performance. This abstract examines the role of machine learning algorithms in predicting equipment failures & optimizing maintenance activities. By analyzing historical data, sensor information, & operational conditions, machine learning models can forecast potential failures, enabling preemptive maintenance actions that reduce unplanned down-times & extend equipment life. Techniques such as decision trees, neural networks, & clustering algorithms are explored for their effectiveness in detecting anomalies, predicting failures, & recommending maintenance schedules. This approach not only improves the efficiency of maintenance processes but also offers significant cost savings & safety enhancements. The research addresses practical challenges in deploying machine learning solutions, including data acquisition, model accuracy, & integration into existing maintenance systems. The abstract highlights the potential of these technologies to transform maintenance practices & drive future advancements in industrial equipment management. © 2024 IEEE.

关键词： Predictive maintenance

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Exploring machine learning Approaches for Analysis and Prediction in the Indian Stock Market 15

Exploring Machine Learning Approaches for Analysis and Predi...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Buche, Arti Chandak, M.B. Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

Predicting the Indian stock market with accuracy is a considerable challenge, mainly due to its nonlinear time series nature. Nevertheless, the advent of diverse machine learning methodologies has sparked an extensive investigation in this domain. Primarily, scholarly inquiries have concentrated on harnessing Supervised machine learning algorithms for predicting the prices of individual stocks and forecasting movements in stock indices. Furthermore, researchers have explored the utilization of Unsupervised machine learning algorithms and statistical approaches to anticipate stock prices. Another area of exploration involves constructing portfolios that maximize profits while minimizing risks. Moreover, the influence of social media, which generates an abundance of data, has become a crucial factor impacting experts' decisions to buy or sell stocks. This survey aims to identify the various approaches researchers employ to improve the accuracy of machine learning algorithms in analyzing the Indian Stock Market. It highlights the importance of taking into account all pertinent aspects of the stock market prior to making predictions. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Component-based Approach to Software engineering of machine learning-enabled systems 24

Component-based Approach to Software Engineering of Machine ...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Indykov, Vladislav Univ Gothenburg Chalmers Gothenburg Sweden

ISBN: (纸本)9798400705915

machine learning (ML) - enabled systems capture new frontiers of industrial use. The development of such systems is becoming a priority course for many vendors due to the unique capabilities of Artificial Intelligence (AI) techniques. The current trend today is to integrate ML functionality into complex systems as architectural components. There are a lot of relevant challenges associated with this strategy in terms of the overall system architecture and in the context of development workflow (MLOps). The probabilistic nature, crucial dependency on data, and work in an environment of high uncertainty do not allow software engineers to apply traditional software development methodologies. As a result, there is a community request to systematize the most relevant experience in building software architectures with ML components, to create new approaches to organizing the process of developing ML-enabled systems, and to build new models for assessing the system quality. Our research contributes to all mentioned directions and aims to create a methodology for the efficient implementation of ML-enabled software and AI components. The results of the research can be used in the design and development in industrial settings, as well as a basis for further studies in the research field, which is of both practical and scientific value.

关键词： machine learning software architecture software quality

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Ensuring privacy of data in machine learning 6

Ensuring privacy of data in Machine Learning

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6th IEEE international conference on Recent advances in Intelligent Computational systems, RAICS 2024

作者： Reshmi, T. Divakaran, Sumesh College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350381689

machine learning is one of the most popular advancements in technology which is being widely used in various domains, including healthcare, avionics, automotive, business, education, etc. A machine learning approach works by learning the required knowledge from the data to be supplied by a client system. In a typical scenario, a client entrusts a third-party agency to develop a machine learning application and shares the data to the developer to enable the development of a machine learning application. This is badly affecting the privacy of data as a third-party is getting access to sensitive data from a client system. Therefore, an effective data encoding technique is required to ensure the privacy of sensitive data while enabling a third-party agency to develop a machine learning application on the encoded data. Existing data masking/encoding techniques such as pseudonymization, anonymization and substitution are badly affecting the machine learning process as the modification they do for masking data is preventing a machine learning approach to learn the required knowledge. Another approach known in the literature is Fully Homomorphic Encryption. But, there is no tool available based on this technique which enables a client system to mask sensitive data before giving it to a third-party machine learning developer. We failed to obtain the expected outcome from an off-the-shelf machine learning classifier when we tried to classify a benchmark dataset masked using an available implementation of Fully Homomorphic Encryption. Since enough details about the implementation is not available, we could not find and fix the issues which is causing this problem. We propose a technique based on which we implemented a tool which provides an easy-to-use Graphical User Interface enabling the client to mask sensitive data before giving it to a third-party agency for developing a machine learning Application. Our tool enables a client to mask sensitive data through few mouse click

关键词： Adversarial machine learning

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Customer Sentiments Towards Delivery Services in Bangladesh: A machine learning-Based Sentiment Analysis

Customer Sentiments Towards Delivery Services in Bangladesh:...

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2024 international conference on advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Bitto, Abu Kowshir Arman, Md. Shohel Saha, Rakhi Moni Jahan, Hasnur Mahmud, Imran Das, Aka Daffodil International University Department of Software Engineering Dhaka1207 Bangladesh Premier University Department of Computer Science Chattogram4203 Bangladesh

ISBN: (纸本)9798350350289

Bangladesh has a large population, which is causing the delivery system to grow up, day by day. Therefore, several companies that provide these delivery services usually referred to as 'Currier Service', are growing gradually. In this paper, we focus on the top delivery service companies in Bangladesh. We analyze customer sentiment based on reviews and comments collected from their social media pages. We collected data of customers' comments from the verified social media pages of Sundorban Currier Services, Redx Currier Services, and Pathao Delivery System. We used a combination of Natural Language Processing and machine learning models to categorize Bangla sentiment reviews based on N-gram features. We used the Unigram, Bigram, and Trigram methods to identify the number of negative and positive reviews on real-life social media platforms and to predict whether new reviews were negative or positive. We have found that the Bigram feature is the best for this analysis because it has the highest accuracy of 90.72%. Using the Bangla NLP approach, the machine learning models are categorized in negative and positive reviews with the sentiment analysis method. © 2024 IEEE.

关键词： Sentiment analysis

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