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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 13417 条记录，以下是101-110 订阅

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MRI Based Health Detection Using machine learning Techniques 14

MRI Based Health Detection Using Machine Learning Techniques

引用

14th international conference on Cloud computing, data science and Engineering, Confluence 2024

作者： Ranjai, Rishab Gupta, Sumita Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science & Engineering U.P. Noida India

ISBN: (纸本)9798350344837

Health has a significant influence on one's wellbeing and quality of life. By assisting medical practitioners, suggesting possible therapies, and allowing early illness identification, machine learning techniques have the potential to completely transform the healthcare sector. This research assesses many machine learning approaches, such as Decision Tree classifier, Random Forest classifier, Logistic Regression, and Support Vector machine, for identifying heart health concerns. It also offers a Convolutional Neural Network (CNN) model for brain tumor diagnosis. CNNs may detect tiny anomalies that may escape human inspection because of their expertise in processing medical pictures, such as MRI scans. The suggested 11-layer CNN model for cancer diagnosis has a 97.7% accuracy rate, a 96.9% recall rate, and a 1.36% false positive rate. Furthermore, by evaluating large amounts of patient data to pinpoint those who are at risk, machine learning algorithms also significantly contribute to the prediction of cardiac disease. © 2024 IEEE.

关键词： Logistic regression

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POSTER: Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems 21

POSTER: Scalable K-Nearest Neighbors Implementation using Di...

引用

21st ACM international conference on computing Frontiers (CF)

作者： De Sio, Corrado Avignone, Andrea Sterpone, Luca Chiusano, Silvia Politecn Torino Dept Control & Comp Engn DAUIN Turin Italy

ISBN: (纸本)9798400705977

The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the machine learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.

关键词： Distributed Systems Embedded Systems K-NN machine learning SoC

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One Hop Routing Optimization Approach Using machine learning 19

One Hop Routing Optimization Approach Using Machine Learning

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Kaja, Siddardha Shakshuki, Elhadi Malik, Haroon Yasar, Ansar Acadia University WolfvilleNS Canada Marshall University WV United States Hasselt University Belgium

Every data packet must pass through a few intermediate nodes toreach its destination. Among other reasons, tremendous growth in internetdevices encourages those intermediate nodes to drop the data packets. Optimizing the data packet route is an effective solution to deal with packetloss. Advanced machine learning approaches have been identified as a powerful support tool for routing optimization in node networks. Cloud computing has kept pace with the continuously developing hardware infrastructure. Improved connection, processing power, and memory units enable real-time machine learning. This paper proposes and evaluates an approach for optimizing the packet path, by one hop, for intermediate nodes as a backup called Cloud Acknowledgement Scheme (CAS). It offers information on the transmission trend and the tendencies of certain adjacent nodes or groups of neighboring nodes in a network. The proposed CAS has been validated via a series of machine learning experiments using real-world node data. © 2024 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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An Intelligent machine learning Approach to Detect Spam in Social Media 5

An Intelligent Machine Learning Approach to Detect Spam in S...

引用

5th international conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mohandas, R. Prasanna, D.S. John Deva Meenakshi, N. Kaviyaraj, R. Nathiya, S. Arivazhagan, N. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350389609

machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Mental Health Status Prediction Models Using machine learning Algorithms 3

Optimizing Mental Health Status Prediction Models Using Mach...

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Dong Guangdong Polytechnic of Science and Technology Guangdong Zhuhai519090 China

ISBN: (纸本)9798331519032

In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals' mental well-being has become a pivotal research focus. Traditional predictive models for mental health often struggle with issues like the complexity of data processing and insufficient prediction precision. However, the application of machine learning algorithms introduces new approaches to address these challenges. In the age of artificial intelligence, creative works are high-quality data sources for machine learning, raising crucial questions about copyright regulations. Balancing these rules to foster innovation in both cultural and technological sectors is a pressing issue. This study employs various machine learning techniques, such as support vector machines, decision trees, and deep learning algorithms, to thoroughly examine and evaluate mental health data. As artificial intelligence technology evolves rapidly and reshapes its applications, new research themes and methodologies continuously emerge within machine learning, with advances in deep learning and reinforcement learning driving the field forward. This paper not only reviews the essential techniques in the optimization process but also explores future research avenues and potential applications, aiming to provide valuable insights for ongoing research and practical efforts in mental health prediction. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence (AI), machine learning (ML) &Deep learning (DL): A Comprehensive Overview on Techniques, Applications and Research Directions 2

Artificial Intelligence (AI), Machine Learning (ML) &Deep Le...

引用

2nd international conference on Sustainable computing and Smart Systems (ICSCSS)

作者： Mian, Syed Mohtashim Khan, Mohammad Shuaib Shawez, Mohd Kaur, Amandeep SRM Univ Dept Comp Sci & Engn Sonipat Haryana India Chitkara Univ Inst Engn & Technol Rajpura Punjab India TMU Moradabad India GNA Univ Dhak Khati Punjab India

ISBN: (纸本)9798350391558;9798350379990

The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human intelligence can be sufficiently defined for machines to emulate. machine learning (ML), a branch of AI, enables software programs to enhance their predictive accuracy without explicit programming, using historical data to forecast new output values. Deep learning, a subset of ML, involves training models to organize sounds, text, or images using neural networks and substantial labeled data. In some cases, deep learning models surpass human performance, achieving state-of-the-art accuracy. This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.

关键词： machine learning (ML) Artificial Intelligence (AI) Computation data science Convolution Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Artificial Neural Networks (ANNs)

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A Dual Transfer Framework for Cooperative Spectrum Sensing in Cognitive Radio

A Dual Transfer Framework for Cooperative Spectrum Sensing i...

引用

international conference on computing, Networking and Communications (ICNC)

作者： Li, Lusi Ning, Rui Wang, Ziyu Hao, Shuai Old Dominion Univ Dept Comp Sci Norfolk VA 23509 USA

ISBN: (纸本)9798350371000;9798350370997

Introducing machine learning (ML) into cooperative spectrum sensing (CSS) in cognitive radio has yielded effective data-driven solutions for the spectrum shortage problem. Significant quantities of labeled data are required for ML-based CSS model training. However, collecting and annotating data repeatedly under dynamic spectrum environmental conditions is time-consuming, costly, and impractical. To this end, we propose a novel non-parametric dual transfer framework for CSS (DTCSS) to solve the poor generalization sensing performance caused by insufficient labeled data in the target environment with different wireless signals and propagation. DTCSS features a unique design that employs a two-stage learning approach. The offline training stage aims to transfer domain-level and class-level knowledge from the existing environment to the target environment and train a target detector. The objective of the online sensing phase is to utilize the trained detector to deduce the spectrum status of the target environment. DTCSS is robust and effective without hyperparameter tuning. Results from simulations indicate that DTCSS can achieve competitive sensing performance.

关键词： Cooperative spectrum sensing machine learning dual transfer learning non-parametric training robustness

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An Empirical Study on AI-Powered Edge computing Architectures for Real-Time IoT Applications 48

An Empirical Study on AI-Powered Edge Computing Architecture...

引用

48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Yasmin, Awatif Mahmud, Tarek Debnath, Minakshi Ngu, Anne H. H. Texas State Univ San Marcos TX 78666 USA

ISBN: (纸本)9798350376975;9798350376968

AI-Powered Edge computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machine learning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machine learning prediction on the server using the TensorFlow native model format has achieved the best model accuracy without draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.

关键词： service framework Edge computing IoT Applications Software Architectures Fall Detection

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Quantum-Inspired Optimization Algorithms for Scalable machine learning in Edge computing 4

Quantum-Inspired Optimization Algorithms for Scalable Machin...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Goyal, Rohit Kumar, Krishan Sharma, Vivek Bhutia, Rudramani Jain, Arpit Kumar, Munish School of Engineering and Computing Dev Bhoomi Uttarakhand University Uttarakhand Dehradun India Bhagat Phool Singh Mahila Vishwavidyalaya Department of Ece Haryana Sonipat India School of Computer Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering A.P. Vaddeswaram India

ISBN: (纸本)9798350387490

The convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edge devices poses challenges due to limited resources such as power, memory, and computational capability. Quantum-inspired optimization algorithms (QIOAs) offer a promising alternative to traditional optimization techniques, providing superior performance in constrained environments. This paper explores the application of QIOAs for scalable machine learning models in edge computing. We propose a novel hybrid quantum-inspired approach, benchmark it against classical algorithms, and demonstrate its efficacy in various edge computing environments. Experimental results show improved accuracy, reduced latency, and enhanced resource utilization. © 2024 IEEE.

关键词： Optimization algorithms

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A review on Ensemble learning based maximal frequent pattern mining over Cloud

A review on Ensemble learning based maximal frequent pattern...

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2024 IEEE international Students' conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Anil, Ghatage Rahul Peter, J. Selvin Paul Jitkar, B.D. Srm Institute of Science & Technology Department of Computing Technology Tamil Nadu Kattankulathur India D.Y.Patil College of Engg. & Tech. Department of Cse Maharashtra Kolhapur India

ISBN: (纸本)9798350348460

In digital era vast quantity of data is produced by smart machines and it is challenging work to store and evaluate the Big data. The frequent pattern mining in Big data plays extremely vital part in industry for decision making process. Ensemble learning is a common machine learning categorization technique that employs multiple learning algorithms to resolve a problem more accurately and predictably in big data. The present system experiences several difficulties, including performance, scalability, parallelism, privacy, and security. This paper examines the overview of several new techniques, algorithms and methodologies in ensemble learning, cloud computing, frequent pattern mining and privacy preservation. This study also makes an attempt to find out appropriate Big data processing framework that are used to generate frequent patterns over cloud using ensemble learning. This paper concludes by giving challenges and future direction in pattern mining using ensemble learning. © 2024 IEEE.

关键词： Cloud computing Ensemble learning Frequent pattern mining

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