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检索条件"任意字段=International Conference on Machine Learning and Computing"

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An Efficient Technique for Cloud Resource Management Using machine learning Model

An Efficient Technique for Cloud Resource Management Using M...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Vyas, Bhuman Saravanakumar, C. Deenadayalan, T. Credit Acceptance Canton48187 United States Government Polytechnic College Department of Computer Engineering Chennai600012 India VIT-Vellore SCORE Vellore India

ISBN: (纸本)9798350319200

Resource management is a vital process in the cloud for satisfying the customer requirement. Resources get the task from the user and perform necessary action. The task needs the data which are collected from various environments. The existing systems are not concentrating on the data but it maps the request to the resources. Resource level management provides the service response to the user. It suffers the data related issues, so it is solved by incorporate the efficient machine learning over the cloud. The proposed model classifies the dataset based on the workload condition namely GPU and CPU level. The model also developed related to the workload. The resources from the cloud are provisioning and allocate to the specific region with necessary parameters. Dynamic load assignment helps to the user for keeping the cost in their acceptable level. Various machine and deep learning models have been analysed related to workload parameters. It achieves high efficient resource monitoring in order to handle faulty resource and make then to active level. © 2023 IEEE.

关键词： Cloud computing Dynamic Resource Management machine learning Resource Monitoring Service Oriented Architecture

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Predicting DDoS Attacks: A machine learning Approach using ALDDoS Dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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Data Ingestion and Harmonisation for the Maritime Domain 30

Data Ingestion and Harmonisation for the Maritime Domain

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30th IEEE international conference on Engineering, Technology, and Innovation

作者： Rega, Bruno Figueiras, Paulo Grilo, Andre Khodamoradi, Amin Lourenco, Luis Agostinho, Carlos Jardim-Goncalves, Ricardo UNINOVA Ctr Technol & Syst CTS Caparica Portugal

ISBN: (纸本)9798350362442;9798350362435

The Maritime Industry is a massive business, connecting the entire world, as the main means of trading of essential goods. Nevertheless, there are challenges with the ever-increasing maritime traffic complexity, safety, performance, energy efficiency and automation. These challenges are driving the industry to embrace a digital transformation of the sector, with the application of state-of-the-art Artificial Intelligence, Big Data and High-Performance computing technologies. With the extremely large amount of data generated by shipping, it is possible to apply these technologies to model the ships and their behaviours, create digital twins of the ships, as well as to model the traffic patterns in the sea, make optimal route predictions, etc. However, due to the vast number of actors in the Maritime Industry, the large amounts of data generated by the different actors is wildly varied, heterogeneous and complex. To use this data to train machine learning models and Artificial Intelligence technologies, there is a need for all the data coming from the different actors in the industry to be homogenised into a single unified format. To accomplish this, the authors propose the creation of the VesselAI Data Ingestion and Harmonisation Services, a tool that enables ingestion and harmonisation of generic maritime datasets. This tool provides the ability to map a raw dataset of choice to a harmonised schema with the application of Natural Language Processing algorithms, with no need to use scripts or develop code.

关键词： Data Ingestion Data Harmonisation Maritime Industry machine learning Artificial Intelligence

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Prediction of Monkeypox Disease Using Biogeography Based Optimization Methodology

Prediction of Monkeypox Disease Using Biogeography Based Opt...

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2024 IEEE international conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Kalapaka, Abhinay Chamakuri, Bhararath Koduru, Pavan Raj Gudas, Suraj Dwivedi, Anshu Kumar SR University School of Computer Science and Artificial Intelligence Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350385199

Monkeypox, a rare viral illness predominantly found in Central and West Africa, presents significant public health concerns. Detecting and predicting monkeypox outbreaks early is essential for effective disease management. In this study, we propose a new method for predicting monkeypox using Biogeography-Based Optimization (BBO) alongside machine learning techniques. We used clinical features and demographic data dataset to train and evaluate our predictive model. BBO was employed to select the most relevant subset of features from the dataset, improving the model's performance. Our findings demonstrate the efficacy of our approach, achieving high accuracy in predicting monkeypox outbreaks. Moreover, we emphasize the importance of feature selection in enhancing the interpretability and applicability of predictive models for infectious diseases. This research contributes to advancing predictive analytics in epidemiology and offers valuable insights for disease surveillance and public health interventions. © 2024 IEEE.

关键词： Adversarial machine learning

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Using Neural Network Approaches to Classify Breast Cancer

Using Neural Network Approaches to Classify Breast Cancer

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

作者： Gupta, Atika Sar, Ayan Mehra, Nidhi Kapil, Divya Harbola, Aditya Choudhury, Tanupriya Graphic Era Hill University School of Computing Uttarakhand Dehradun248002 India School of Computer Science Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Cse Dept. Uttarakhand Dehradun248002 India University of Petroleum and Energy Studies School of Computer Science Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350373783

Breast cancer, a fatal tumor that affects both women and men, can be detected early and treated effectively to save lives. In this article, we aim to categorize breast cancer data as either benign or malignant tumors using various techniques such as Deep learning and machine learning. We employ Sequential Neural Networks, Logistic Regression, Random Forest, Decision Tree, and SVM for classification purposes. Upon comparing the results of each classifier, we determine that the Sequential Neural Network achieves the highest accuracy score of 98% in distinguishing between benign and malignant cancers. Accurate classification enables prompt detection of the disease, benefiting patients and doctors alike in their efforts to combat breast cancer. © 2024 IEEE.

关键词： Lung cancer

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Predicting Mental States: machine learning Techniques for Personalized Mental Health Support 15

Predicting Mental States: Machine Learning Techniques for Pe...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hepzibah, Grace Babu, Nirmal Varghese Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India Division of Data Science and Cyber Security Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

As we know that individuals around the globe work difficult to keep up with this hustling world. In any case, due to this each person is managing with diverse wellbeing issues, one of the foremost known issue is sadness or stretch which may in the long run lead to passing or other brutal exercises. This project focuses on leveraging machine learning algorithms to predict psychological instability based on various attributes related to mental health. Developed as a web application using Django, users can upload datasets containing information such as age, gender, family history, and workplace environment. The data undergoes preprocessing, including handling missing values and label encoding. Subsequently, several classifiers including Decision Tree, Random Forest, SVC, MLP Classifier, KNeighbors Classifier, and Linear Discriminant Analysis are trained on the preprocessed data. Evaluation metrics such as accuracy, precision, recall, and F1-score are computed for each classifier. Users can also input personal data to obtain predictions about their psychological well-being. The system aims to offer insights into mental health conditions, enabling early detection and intervention. This project underscores the potential of machine learning in addressing mental health challenges through data-driven approaches. © Grenze Scientific Society, 2024.

关键词： Decision trees

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Research on Employee Innovative Behavior Feedback System Based on Web-based ID3 Algorithm 3

Research on Employee Innovative Behavior Feedback System Bas...

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

作者： Luo, Hongchi School of Management Shanghai University Shanghai200444 China

ISBN: (纸本)9798331519032

In the Internet era, the rapid development of technology has had a profound impact on the development of enterprises. With the rapid changes in the global economy, enterprises are faced with the serious challenges brought about by the uncertainty of market structure and demand. In this case, the innovative behaviour of employees has become a key factor to promote the development of enterprises, which is an important part of their innovative capacity. The study used the questionnaire star platform and offline distribution of questionnaires for enterprise employees with different gender, age, nature of the enterprise, job level and other attributes, and successfully distributed and returned 206 valid questionnaires, using SPSS 27, AMOS 25 and other analytical software to analyse the data for reliability analysis, validity analysis and correlation test, and using machine learning decision tree algorithm -ID3 algorithm to construct the decision tree model. This study can effectively help enterprises to recruit employees, through the comprehensive analysis of the attributes and innovative behaviour of employees, to achieve the accurate identification of employee potential, and then screen out high-quality talents with innovative ability for enterprises. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep learning machine learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Green machine learning for the IoT (GEMINI): A Position Paper

Green Machine Learning for the IoT (GEMINI): A Position Pape...

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international conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Lazzaroni, Luca Dabbous, Ali Nardello, Matteo Lebdeh, Muath Abu Berta, Riccardo Yildirim, Kasim Sinan Bellotti, Francesco Brunelli, Davide Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa16145 Italy Department of Industrial Engineering University of Trento Trento38122 Italy

ISBN: (纸本)9783031840999

The proliferation of IoT devices has significantly increased global energy consumption and carbon footprint due to the reliance on computationally intensive machine learning (ML) techniques. Traditionally implemented on high-end embedded devices or exploiting cloud computing, these applications further exacerbate environmental impacts. As IoT technology advances, there’s a shift towards smaller, low-power devices, necessitating decentralized computation. This shift has given rise to Tiny machine learning (TinyML), enabling ML tasks on edge devices. However, to fully exploit TinyML, integrated frameworks are essential for efficiently developing and deploying smart applications, especially on devices operating intermittently on harvested energy. The Green machine learning for the IoT (GEMINI) project aims to address these challenges by creating a framework for sustainable TinyML applications on battery-powered and batteryless energy-harvesting edge devices. GEMINI targets shifting computational workloads from the cloud to the edge, optimizing latency, bandwidth, energy use, and privacy. By integrating energy harvesting methods, zero-power communication protocols, and intermittent computing strategies, GEMINI seeks to ensure robust ML inference under intermittent power conditions. The project aims to include efficient data collection, ML model generation, and online learning directly on edge devices, reducing reliance on constant cloud connectivity. The open-source framework intends to offer a comprehensive toolkit for developing sustainable IoT solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Towards Poisoning of Federated Support Vector machines with Data Poisoning Attacks 13

Towards Poisoning of Federated Support Vector Machines with ...

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13th international conference on Cloud computing and Services Science (CLOSER)

作者： Mouri, Israt Jahan Ridowan, Muhammad Adnan, Muhammad Abdullah Bangladesh Univ Engn & Technol BUET Dhaka Bangladesh

ISBN: (纸本)9789897586507

Federated Support Vector machine (F-SVM) is a technology that enables distributed edge devices to collectively learn a common SVM model without sharing data samples. Instead, edge devices submit local updates to the global machine, which are then aggregated and sent back to edge devices. Due to the distributed nature of federated learning, edge devices are vulnerable to poisoning attacks, especially during training. Attackers in adversarial edge devices can poison the dataset to hamper the global machine's accuracy. This study investigates the impact of data poisoning attacks on federated SVM classifiers. In particular, we adopt two widespread data poisoning attacks for SVM named label flipping and optimal poisoning attacks for F-SVM and evaluate their impact on the MNIST and CIFAR10 datasets. We measure the impact of these poisoning attacks on the precision of global training. Results show that 33% of adversarial edge devices can reduce accuracy up to 30%. Furthermore, we also investigate some basic defense strategies against poisoning attacks on federated SVM.

关键词： Support Vector machine Poisoning Attack Outlier Detection Federated learning

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