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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是471-480 订阅

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DDoS Attack Detection on Cloud computing Services using Algorithms of machine learning: Survey

DDoS Attack Detection on Cloud Computing Services using Algo...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Chilla Sathvika Vuyyuru Satwika Yarrapothu Sruthi Maddali Geethika Suneetha Bulla Swathi K Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyber-attacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. this article is about DDoS attacks and detection of DDoS attacks using machine learning. there are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. these machine learning algorithms can be used to detect the DDoS attacks on doud. there are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. this paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology.

关键词： Support vector machines Cloud computing machine learning algorithms Heuristic algorithms Organizations Denial-of-service attack Feature extraction

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Hybrid ML Algorithms for learning Disability Forecast in School Going Children Using Python in machine learning Techniques

Hybrid ML Algorithms for Learning Disability Forecast in Sch...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Margaret Mary T V S Prakash Divya K S Amalorpavam George Kristu Jayanti College Bengaluru Karnataka India Affiliation Bangalore North University Sambhram Academy of Management studies Bengaluru Karnataka India

A learning disability is a neurological illness that impairs a child's ability to read, speak, and do a variety of other skills. the World Health Organization (WHO) estimates that learning disabilities impact 15% of youngsters [14]. the most important challenge for researchers to perform in order to identify learning disabilities early on is efficient prediction and accurate categorization. Our primary goal in this effort is to use soft computing to create a model for the prediction and categorization of learning disabilities. this study proposes a hybrid approach for enabling classification in order to enhance the performance of prediction and classification. this method incorporated classification's primary five techniques. Random Forest, Logistic Regression, Stochastic Gradient Descent, and K-Fold cross validation. In order to implement the system used python. Results analysis reveals the predict of learning disability in effectively

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Predicting Students' Academic Success using machine learning Algorithms

Predicting Students' Academic Success using Machine Learning...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Tanmay Nema Sushila Palwe Department of Computer Engineering and Technology MIT World Peace University Pune India

this research attempts to predict the academic success of a student using machine learning algorithms and a thorough strategy that takes into account a variety of variables that affect students' results. the study examines a large dataset that contains information on the socioeconomic situation, involvement, and academic success of the students. To create predictive models that is able to find out the student who is at an academic risk of quitting and offer timely interventions to enhance their academic performance and prevent dropout, machine learning methods like Classifier based on Random Forest, Classifier Based on Decision Trees, Classifier using Logistic Regression, Voting based Classifier, and others will be used. To enhance students' achievement and raise the standard of education, policy decisions and resource allocation can be made using the knowledge gathered from the analysis.

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A Novel Model for Prediction of Next Word using machine learning

A Novel Model for Prediction of Next Word using Machine Lear...

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international conference on ent computing and Control Systems (ICICCS)

作者： Prathima Chilukuri K Naveen Kumar Reddy K N V S Divyanjali L Javali Royal M Hari Chandana D Murali Krishna School of computing (IT) Mohan Babu University Tirupati Andhra Pradesh Sree Vidyanikethan Engineering College Tirupati

Prediction of next word is also known as language modeling and is an application of Natural Language Processing which helps in next word prediction. In the past, several studies employed various models to predict the outcomes, including federated text models and recurrent neural networks. In the suggested model, machine learning and TensorFlow techniques will be used to predict the five words. the text is understood by the machine learning algorithms, which then anticipate the next word to be used to finish the phrase. the proposed model helps the user to predict the next word. the model will be implemented using natural processing language algorithms and comparing the algorithms by using Wikipedia text files to train the model.

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the Use of machine learning for Digital Shadowing in thermal Spray Coating 7th

The Use of Machine Learning for Digital Shadowing in Ther...

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Petrovskiy, Andrey Arifeen, Murshedul Petrovski, Sergei Samara State Technical University Samara Russia School of Computing Robert Gordon University Aberdeen United Kingdom

ISBN: (纸本)9783031437885

Digital shadows in industrial IT environments are virtual copies of production or manufacturing processes based on historical or real-time data obtained from physical sensors, control or automation systems. there is only one-way interaction between the shadowed process and its virtual copy, which differentiates a digital shadow from a digital twin exchanging the data in both directions. For many industrial applications, however, building a digital shadow using historical data is a sufficient, but quite challenging, task requiring the deployment of the entire data analytics pipeline. the presented paper demonstrates how machine learning and some related AI-based approaches can assist in developing and effective usage of intelligent IT applications. thermal spray coating has been chosen as a use-case for demonstrating the applicability and feasibility of the chosen methodology for enhancing the operation of an industrial IT system supporting the coating process. the outcome of a comparative experimental studies demonstrated that artificial neural networks provide the most robust, versatile and generalisable technique for engineering data analytics in the chosen problem domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data Analytics

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A Study of Challenges and Limitations to Applying machine learning to Highly Unstructured Data

A Study of Challenges and Limitations to Applying Machine Le...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Sahib Singh Susheela Hooda Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In recent years, there has been a lot of curiosity about the use of machine learning algorithms to analyze unstructured data, including social media posts and videos. the limitations and difficulties posed by these technologies must be overcome in order to guarantee accurate and trustworthy results, despite the promise they hold. this study examines the restrictions and difficulties associated with using machine learning algorithms on unstructured data. We identify the main difficulties in working with unstructured data, such as the lack of standardization and the difficulty in handling noise and bias, through a literature review and case studies. We then look at methods for overcoming these obstacles, such as feature engineering, pre-processing techniques, and ensemble methods. the results of this study have implications for both theoretical and applied work in machine learning, particularly in the fields of feature engineering, model selection, and data cleaning. this paper's goal is to provide a thorough understanding of the constraints and difficulties associated with using machine learning algorithms on unstructured data, as well as to contribute to the creation of best practices for handling these data type

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Bird Call Recognition using Acoustic based Feature Selection approach in machine learning

Bird Call Recognition using Acoustic based Feature Selection...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Shivam Mishra Rumpa Ghatak Vishal Kumar Singh Shivam Kumar Singh Anisha Mahato Information Technology Techno International New Town Kolkata India

the recognition of bird calls has been a challenging task in the field of bioacoustics. With the advancement of machine learning algorithms, automatic bird call recognition has become an active research area. In this paper, acoustic feature selection is used which involves the extraction of relevant features from audio recordings of bird calls. then different classification algorithms are explored for the recognition of the bird calls. Evaluation of the approach is done on a dataset consisting of recordings from multiple bird species and compared it with other state-of-the-art machine learning algorithms. this research has the potential to contribute to the conservation of bird species and their habitats by enabling the efficient monitoring of bird populations. the data is classified into four different classes (namely Astfly, Bulori, Warvir and Woothr). All of which is found in the depths of Amazon Rainforest.

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Region-Wise COVID-19 Vaccination Distribution Modelling in Tamil Nadu Using machine learning 5th

Region-Wise COVID-19 Vaccination Distribution Modelling in T...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Gowtham, M. Pradeep Harini, N. Department of Computer Science Amrita Vishwa Vidyapeetham Amrita School of Engineering Ettimadai India

ISBN: (纸本)9789811986680

COVID-19 is increasing widely because of variants like Alpha, Beta and Omicron, etc. World Health Organization has suggested that increasing the vaccination rate is the only viable solution to mitigate the spread of this polymorphic virus. the motivation is to curtail the spread of the virus in districts (Tamil Nadu was chosen for experimentation) by developing a strategy based on population and vaccination statistics. Due to the varied populations in districts, vaccinating all the people in a district is not an easy task. Time series models such as ARIMA, FbProphet and LSTM are used for the prediction of vaccination rate and the expected number of positive cases in a given district. the accuracy of the models has been compared using MAPE. Interestingly, ARIMA exhibited higher accuracy than other models. the prediction of the scheme is used to optimize the vaccination distribution in the districts of a given state. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Analysis of Deep learning Architecture-Based Classifier for the Cervical Cancer Classification 7th

Analysis of Deep Learning Architecture-Based Classifier for ...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Chandraprabha, R. Singh, Seema Electronics and Comunication Engineering BMS Institute of Technology and Management Bangalore India Electronics and Telecomunication Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789811954818

the incidence and mortality rates of cervical cancer in low-income states are more due to the constrained healthcare resources. For quick screening and early diagnosis, a very efficient and accurate intelligent system has to be developed which can diminish the incidence and mortal rate. Various image processing with machine learning algorithms and deep learning algorithms are applied for the diagnosis of cervical cancer to provide technical support for the healthcare pathologist department. Deep learning architecture achieves the classification task at higher accuracy. this paper brings the analysis of a fine-tuned deep learning architecture applied for the cervical cancer diagnosis. the proposed pre-trained model Alexnet and GoogleNet are fine-tuned with transfer learning for the classification task. the dataset considered is the HErlev Dataset (standard) and real-time clinical data comprising of 917 images and 10 images, respectively. the proposed model is trained, validated, and tested with 06 epochs and 30 epochs for the standard dataset. Accuracy, error rate, precision, and area under the curve are evaluated to analyze the proposed architectures. An accuracy achieved for the proposed architectures with 30 epochs is 93.99 and 89.07%. the proposed architectures trained with 30 epochs are validated on the clinical data. An accuracy of 0.9508 and 0.5 for the 30 epochs is presented by the proposed architecture with clinical dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intelligent systems

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Applications and Challenges of machine learning Techniques for Smart Manufacturing in Industry 4.0

Applications and Challenges of Machine Learning Techniques f...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Kartik Kolhe Avinash. A. Somatkar Mrunalini S. Bhandarkar Kapil.B Kotangale Swapnil S. Ayane Sonal I. Shirke Department of Mechanical Engineering Vishwakarma Institute of Information Technology Pune India Department of Electronics & Telecommunication Engineering Pimpri Chinchwad College of Engineering Nigdi Pune

the fourth industrial revolution, Industry 4.0, is transforming the manufacturing industry by integrating advanced technologies such as IoT, AI, and ML into the manufacturing process. In particular, the application of machine learning techniques in smart manufacturing has the potential to revolutionize the way manufacturers approach production. this paper reviews the various applications of machine learning techniques in smart manufacturing, including predictive maintenance, quality control, and supply chain management. We also discuss the challenges associated with implementing machine learning techniques in manufacturing, such as data management and privacy concerns, lack of skilled personnel, and the need for effective collaboration between manufacturers and technology providers. Additionally, we provide insights into the future of machine learning in manufacturing, including the potential for increased automation, improved operational efficiency, and the development of new business models. Overall, this paper highlights the potential benefits and challenges associated with the application of machine learning techniques in smart manufacturing, providing valuable insights for manufacturers looking to integrate these technologies into their operations.

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