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A machine learning Approach to Analyze Cloud computing Attacks 5

A Machine Learning Approach to Analyze Cloud Computing Attac...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Rani, Sangeeta Tripathi, Khushboo Arora, Yojna Kumar, Ajay Amity University Amity School of Engineering & Technology Gurgaon Manesar India JECRC University School of Engineering & Technology Rajasthan Jaipur India

ISBN: (纸本)9798350398267

A Cloud computing model enables convenient, on demand network access, otherwise known as cloud computing (CC) or 'the Cloud'. Cloud computing is delivered via internet, using cloud.' Everything from mobile applications to data centers on a pay-per-use basis. It is difficult to adopt CC and edge computing for several reasons, including security concerns, client vulnerabilities, and partnership acknowledgment. An algorithm that automatically improves with experience is known as a machine learning algorithm. this paper aims to examine some of the ML algorithms applied to CC security threats. Among the algorithms, we reviewed unsupervised, supervised, semi-supervised, and reinforcement learning for addressing cloud security challenges. CC models can be secured by pursuing future research directions. © 2022 IEEE.

关键词： Reinforcement learning

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A Dynamic Task Mapping Scheme Based on machine learning 5

A Dynamic Task Mapping Scheme Based on Machine Learning

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5th asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2024

作者： An, Jianwei Wu, Qing Dong, Chunfang Zhang, Ziyi University of Science and Technology Beijing Department of Communication Engineering Beijing China

ISBN: (纸本)9798350374407

With the development of technologies such as artificial intelligence and big data, diverse application scenarios have increasingly high demands for model training. the single-machine training mode can no longer meet the requirements of large-scale training, and the use of distributed training in heterogeneous clusters composed of multiple computers or servers has gradually become the mainstream solution. Task mapping is a key component of the heterogeneous cluster task execution process, which has a significant impact on the performance of the cluster and the running efficiency of tasks. However, current task mapping schemes are difficult to adapt to the high heterogeneity of cluster resources and the diversity of large-scale tasks. therefore, how to effectively establish the mapping relationship between different types of tasks and heterogeneous computing resources has become an urgent problem to be solved. In view of this question, the main work and innovative achievements of this thesis are as follows: To address the task mapping problem in large-scale heterogeneous clusters for distributed training, a machine learning-based dynamic task mapping scheme is proposed. the core modules of this scheme are the phase detector and ANN performance predictor. Specifically, the IPC is introduced as a processing core performance indicator in the phase detector, which is used to detect phase changes during task execution, accurately selecting the remapping timing. By selecting appropriate static and dynamic features in the ANN performance predictor, we can effectively predict the behavioral differences of different types of tasks on heterogeneous resources, and design mapping schemes that maximize system performance based on these predictions. Finally, simulation experiments were conducted to compare the proposed scheme with the Completely Fair Scheduler(CFS) and Round Robin Scheduler(RRS). the results show that the completion time of distributed training tasks under the prop

关键词： Mapping

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Proceedings of 5th International conference on Contemporary computing and Informatics, IC3I 2022

Proceedings of 5th International Conference on Contemporary ...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

ISBN: (纸本)9798350398267

the proceedings contain 404 papers. the topics discussed include: sensitivity assessment of human body fluids through PCF-based plasmonic biosensor for biomedical applications;a review on multi-focus image fusion techniques in surveillance applications for image quality enhancement;aspect analysis and visualization of Indian unicorns;a machine learning approach to analyze cloud computing attacks;predicting ratings of trending YouTube videos using machine learning;a review of emotion detection systems;aspect analysis based on statistical description and visualization of data;a structured investigation of network security strategies and its applications;a critical analysis on neural networks and deep learning based techniques for the cloud computing system and its impact on industrial management;internet of things and machine learning based intelligent irrigation system for agriculture;a comprehensive description of artificial intelligence techniques in financial market;and blockchain-based trust management system in implementation in social Internet of things.

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Research on Pedestrian Recognition Based on Deep learning 5

Research on Pedestrian Recognition Based on Deep Learning

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5th International conference on machine learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Liqin, Liu Blancaflor, Eric Abisado, Mideth National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Mapua University School of Information Technology Manila Philippines

ISBN: (纸本)9798350359930

In the realm of computer vision, pedestrian recognition has gained popularity as a study topic in recent years, and has received widespread attention and research from scholars. How to extract more discriminative pedestrian information for pedestrian re recognition has always been a noteworthy issue. While deep learning models can extract features independently, they may face gradient vanishing as the number of network layers increases. Traditional pedestrian recognition techniques rely on the human construction of visual features, which have low recognition accuracy and are easily influenced by other external circumstances. An additional aspect influencing pedestrian recognition accuracy is partial occlusion of pedestrian images. this article proposes an enhanced neural network that combines random erasure and residual attention networks for pedestrian recognition to improve the accuracy of recognition. © 2023 IEEE.

关键词： Network layers

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5th International conference on Recent Innovations in computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th International conference on Recent Innovations in computing , ICRIC 2022

ISBN: (纸本)9789819906000

the proceedings contain 66 papers. the special focus in this conference is on Recent Innovations in computing. the topics include: the Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer Vision Applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised machine learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with machine learning Techniques;machine learning Techniques for Image Manipulation Detection: A Review and Analysis;improving Real-Time Intelligent Transportation Systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical Data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based Automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;Intelligent Communication for Internet of things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;Time Series-Based IDS for Detecting Botnet Attacks in IoT and Embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural machine Translation from English to Marathi Using Various Techniques.

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Using machine learning Techniques to Explore the Possibilities of Reducing the Spread of Corona Virus and its New Variants 5

Using Machine Learning Techniques to Explore the Possibiliti...

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Meshref, Hossam Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif Saudi Arabia

ISBN: (纸本)9781665499736;9781665499729

the Corona pandemic has been around for a while, and its threat to the world is growing. We believe that climate parameters and health prevention measures could be related to the number of reported Corona daily cases. In the literature there were different views on the nature of these relations using several datasets recorded from various parts of the world. In our research, data collected from zones with concentrated Corona cases: China, Europe and the United States were analyzed to understand the relation with climate as well as data at the global level to understand the relation with health prevention measures. Feature importance analysis revealed that temperature is the most important contributing attribute to the Corona cases' prediction models, followed by relative humidity. As well, the percentage of mask use and percentage of fully vaccinated individuals were found to have a great influence on the number of new Corona daily cases. the designed machine learning ensemble techniques had a maximum predication accuracy of 89.08%, and the produced possible interpretations for the designed models agreed with the performed feature importance analyses. We believe that the analysis approach followed in this research as well as the achieved findings could be very useful to other researchers who are interested in conducting more research investigation in the same research area on the new Corona variants. We also believe that policy makers could consider the findings of our research as they effectively plan their future health precautions measures to avoid further spread of the virus.

关键词： corona virus climate change health prevention measures ensemble machine learning model interpretation

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Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques 5

Detecting Chronic Kidney Disease(CKD) at the Initial Stage: ...

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Tusar, Md. Taufiqul Haque Khan Islam, Md. Touhidul Raju, Foyjul Islam City Univ Dhaka Dept Comp Sci & Engn Dhaka 1216 Bangladesh

ISBN: (纸本)9781665499736;9781665499729

Chronic Kidney Disease (CKD) has infected almost 800 million people around the world. Around 1.7 million people die each year because of it. Detecting CKD in the initial stage is essential for saving millions of lives. Many researchers have applied distinct machine learning (ML) methods to detect CKD at an early stage, but detailed studies are still missing. We present a structured and thorough method for dealing with the complexities of medical data with optimal performance. Besides, this study will assist researchers in producing clear ideas on the medical data preparation pipeline. In this paper, we applied KNN Imputation to impute missing values, Local Outlier Factor to remove outliers, SMOTE to handle data imbalance, K-stratified K-fold Cross-validation to validate the ML models, and a novel hybrid feature selection method to remove redundant features. Applied algorithms in this study are Support Vector machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbour, Gradient Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Finally, the Random Forest can detect CKD with 100% accuracy without any data leakage.

关键词： Early Diagnosis machine learning Pre-processing Healthcare Informatics

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Predicting Banking Customer Churn based on Artificial Neural Network 5

Predicting Banking Customer Churn based on Artificial Neural...

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5th International conference on computing and Informatics (IEEE ICCI)

作者： Zaky, Amany Ouf, Shimaa Roushdy, Mohamed Helwan Univ Fac Commerce & Business Adm Business Informat Syst Dept Cairo Egypt Future Univ Egypt Fac Comp & Informat Technol New Cairo Egypt

ISBN: (纸本)9781665499736;9781665499729

Customer churn has become one of the major issues in the banking industry. Because it is difficult to gain new clients, the major focus of customer relationship management is on existing clients. Customer Churn is defined as when customers switch to another provider due to their low prices and better offers. there are many research papers that found solutions to solve the customer churn problem with the help of the techniques of machine learning. In this research paper, we have suggested a framework that introduces a solution to the problem of customer churn in the banking industry. We used the techniques of deep learning namely the artificial neural network to analyze bank customer data and predict the customer churn. the experiment was conducted on a dataset called churn modeling and the results reveal that we were able to attain an accuracy of 87 % for bank customer data by using the ANN algorithm. the proposed framework presented a costeffective option for maintaining bank customers, which increases bank profits by retaining customers.

关键词： customer churn customer retention banking sector deep learning artificial neural network

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FPLGen: A Personalized Dialogue System Based on Feature Prompt learning 5th

FPLGen: A Personalized Dialogue System Based on Feature Prom...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Chu, Yuxing Huang, Ke Li, Yichen Zhu, Hao Li, Peiran Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Univ Sci & Technol Swinburne Coll Jinan 250000 Peoples R China Shandong Enguang Energy Technol Co Ltd Jinan 250001 Peoples R China Shandong Inspur Database Technol Co Ltd Jinan 250001 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Humans with cognitive ability are able to identify pertinent characteristics in other people's discussions easily and respond to conversations containing characteristics that already belong to a certain person. However, utilizing personality information for personalized response generation remains a non-trivial task. the system must consider both the user's conversation history and personality description, posing challenges for coherent model training. In this work, we meticulously design a feature-prompted learning-based dialog generator. this innovative tool primarily harnesses personality description data and dialog history to generate responses. FPLGen uses a clustering mechanism to organize personality description texts into distinct, sparsely populated categories. these categories are merged with historical contextual information and transformed via conditional variational autoencoders. the system incorporates our unique information enhancement and feature-prompted learning strategies, enabling comprehensive dialog synthesis. To validate our model's efficacy, we conducted experiments on the Chinese persona chat dataset. the results, compared to baseline models, provide irrefutable evidence that our FPLGen model excels at producing richer, more engaging personalized responses.

关键词： Personalized Dialogue System Companion Robot Pre-trained model Response Generation Prompt learning

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A Deep learning Based Approach to Recognize Real Time Emotions 5

A Deep Learning Based Approach to Recognize Real Time Emotio...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Vatsa, Abhimanyu Gupta, Sumita Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Noida India

ISBN: (纸本)9798350398267

Facial expressions is the most primary way by which a human recognizes another person's emotion. With a rapid development in the field of Artificial Intelligence and machine learning, research have started to develop and improve a sophisticated technology by which a machine or a computer can recognize emotions and mimic a human's thought process for the same. the proposed methodology for the same is to make use of deep learning. Deep learning is very closely related to machine learning. It allows multiple layers of neural networks to be stacked upon one another enabling our algorithm to 'think' like a human. Many mathematical models are also used to provide the best results and improve on the currently known technology. this paper discusses most of the known approaches for emotion recognition and tests out geometry based using machine learning and deep learning-based implementation providing a deep insight into both methodologies © 2022 IEEE.

关键词： Emotion Recognition

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