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A Machine Learning Framework for Job Failure Prediction in Cloud using Hyper Parameter Tuned MLP

A Machine Learning Framework for Job Failure Prediction in C...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： K. Vani S. Sujatha Department of computer science Emerald Heights College For Women Ooty India Department of computer science Dr. G.R. Damodaran College of Science Coimbatore India

ISBN: (纸本)9781665493970

Failures are inevitable in cloud computing systems (CCS) because of their enormous size and complexity, which results in reliability and efficiency losses. It is possible to take actions to increase the reliability and effectiveness of CCS through failure mitigation, fault tolerance, and recovery. Failure prediction and risk identification approaches could forecast such failure occurrences using data gathered during CCS operation. In order to handle the present state-of-the-art evolving computing systems, standard runtime fault-tolerance (FT) solutions like data replication and periodic check-pointing are not very successful. This has made it essential to have a robust method with a thorough knowledge of component and system failures as well as the capability to accurately predict probable system failures in the future. In this research, we develop a paradigm for improving the reliability and efficiency of cloud environment by risk assessment. This study starts with analyzing the failure task behavior and their related operational information. A predictive model is built using cat boost, genetic algorithm and hyper parameter tuned multilayer perceptron for finding the feature importance, selecting the most relevant features and to predict the high risk cloud tasks respectively. The present method is evaluated on Google Custer data with necessary performance metrics and compared with other machine learning approaches.

关键词： Cloud computing Fault tolerance Fault tolerant systems Machine learning Predictive models Multilayer perceptrons Feature extraction

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Multi-criteria Evaluation of Students’ Performance Based on Hybrid AHP-Entropy Approach with TOPSIS, MOORA and WPM 13th

Multi-criteria Evaluation of Students’ Performance Based on...

引用

13th international conference on ICT Innovations, ICT Innovations 2021

作者： Petrov, Iliyan Department of "Information Processes and Decision Support Systems" Bulgarian Academy of Sciences Institute of Information and Communication Technologies akad. Georgi Bonchev srt. bl. 2 Sofia1113 Bulgaria

ISBN: (纸本)9783031042058

Worldwide pandemics and long periods of social containment require further improvement of Intelligent Education System (IES) with reliable communication networks and computing systems for providing accessible and effective teaching and learning. One of the main challenges in distance learning is to ensure adequate monitoring of the education process and evaluation of interim and final results. The paper presents a hybrid approach for multi-criteria evaluation of students’ performance in a flexible framework of three preference scenarios where the theoretical learning, practical skills, and final exams participate with different weights. The systematization of criteria in blocks and their weighting according to preferences allow obtaining more objective macroscopic results. The Multiple Assessment integration (MAI) of the evaluation values in TOPSIS, MOORA and WPM allows to explore their behavior with different data sets and contributes to consolidate the final results for obtaining a better holistic and personalized view of the education process for each individual student. © 2022, Springer Nature Switzerland AG.

关键词： Entropy

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Impact on Higher Education in Pandemic: Analysis K-Means Clustering Using Urban & Rural Areas 3

Impact on Higher Education in Pandemic: Analysis K-Means Clu...

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3rd international conference on advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Rauthan, Anubhuti Singh, Ajay Shanker Singh, Nimish Anandhan, K. Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9781665438117

The corona pandemic shook the world in 2019 to till from lockdowns to late night curfews, corona virus also impacted the education sector. The shift from the traditional offline education to the new digital online education was adopted by most universities and colleges but many students faced problems due to the difference in socio economic backgrounds and the availability of internet in rural areas. The Indian government is still working on complete digitalization of India and it plans to provide internet to every rural area by 2022 which shows that India still has a long way to go. India is a developing country and it faces many problems in the education sector. This research paper introduces and represented the word pandemic and gives brief information about management of a pandemic. Moreover a data analytics term clustering is defined with its two types, i.e. inclusive and exclusive clustering. Further the K-means algorithm is explained and we have discussed about the impact of pandemic on higher education and how it has affected the students living in urban areas and rural areas with lack of internet services with the help of a clustering technique called k-means clustering representing various densities of the population having access to the internet in urban areas and rural areas. Analyzing the graph we have briefly introduced the 3G and 4G mobile technologies and discussed 3G architecture and 4G architectureand reached a possible solution where areas in dire need of the internet facilities are brought into light and efforts being made by the concerned authorities are told. © 2021 IEEE.

关键词： K-means clustering

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Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models

Accuracy, Fairness, and Interpretability of Machine Learning...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Eric Ingram Furkan Gursoy Ioannis A. Kakadiaris Dept. of Computer Science Computational Biomedicine Lab University of Houston Houston TX USA

Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions. These models take in large amounts of data about an individual and then predict whether an individual would commit a crime if released on parole. Although such models are not the only or primary factor in making the final parole decision, questions have been raised about their accuracy, fairness, and interpretability. In this paper, various machine learning-based criminal recidivism models are created based on a real-world parole decision dataset from the state of Georgia in the United States. The recidivism models are comparatively evaluated for their accuracy, fairness, and interpretability. It is found that there are noted differences and trade-offs between accuracy, fairness, and being inherently interpretable. Therefore, choosing the best model depends on the desired balance between accuracy, fairness, and interpretability, as no model is perfect or consistently the best across different criteria.

关键词： Support vector machines Measurement Computational modeling Neural networks Closed box Predictive models Boosting

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Selection of Trust Nodes for Efficient data Transmission in MANET

Selection of Trust Nodes for Efficient Data Transmission in ...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： K. Gunasekaran D. Regan Basavaraj G Kudamble M. A. Manivasagam Department of ECE Siddartha Institute of Science and Technology Puttur India

ISBN: (纸本)9781665493970

Reliable Routing through Trust Node Selection scheme is proposed to implement improved security measures and efficient routing in MANET. The friend list is created for each and every node and this task is performed for identifying the node ratings through the challenging process for its neighbor nodes. Challenge is a process carried out for determining the ratings obtained for nodes to prove their integrity and honesty. The node challenge process is carried between the nodes through the count of control messages that have been processed. From the consequences of node ratings if the nodes achieve a certain value then the nodes come under the friend node list else the node falls under the unfriend node list and isolates from the routing process. Finally, the data transmission is done through reliable and trusted routes by utilizing a key management model for encrypting the data. Recreation investigation is supported obtainable intended for demonstrating the effectiveness of the future outline.

关键词： Process control Routing Routing protocols Encoding data models Delays Reliability

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Keynote: Sensing human activities: challenges and opportunities

Keynote: Sensing human activities: challenges and opportunit...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Claudio Bettini Dept. of Computer Science EveryWare Lab University of Milan Italy

ISBN: (数字)9781665416474

ISBN: (纸本)9781665416481

The ability to acquire and understand context is a key requirement to build intelligent, adaptive, and personalised services and environments. In the last decades we have experienced the impact of spatio-temporal awareness enabled by the localisation technologies of mobile computing. Pervasive computing has a major role in exploiting additional sensing modalities obtaining large streams of sensor data that, processed with AI techniques, can take context awareness to a new level, enabling human-centered applications that could not be considered before. This talk reviews the advances in this topic and the lessons learned. In particular, it considers sensor-based human activity recognition as a task to derive high level context from low level context data and sensors’ signals. The talk identifies open challenges and new research opportunities. Among the challenges there is the scarcity of annotated data as required by deep learning methods, the need to explain the activity prediction process, and the privacy threats involved in releasing personal data. Among the opportunities, some of the limits of purely statistical approaches could be effectively improved by combining them with reasoning on common knowledge in terms of ontologies or knowledge graphs. data scarcity and privacy concerns may be also mitigated by a combination of techniques including active learning and distributed approaches like federated learning.

关键词： Pervasive computing Deep learning data privacy conferences Distributed databases Context awareness Ontologies

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Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance data Streams Classification

Hybrid Nature-Inspired Based Oversampling and Feature Select...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Monika Arya Bhupesh Kumar Dewangan Monika Verma M. Rohini Anand Motwani Sumit Kumar Sar Dept. Of Computer Science & Engineering Bhilai Institute of Technology Durg CG India Dept. of computer Science Engineering OP Jindal University Raigarh India Dept. of computer Science & Engineering Bhilai Institute of Technology Durg India Dept. of computer Science & Engineering Sri Krishna College of Engineering and Technology Coimbatore India School of Computing Science & Engineering VIT Bhopal University Sehore MP India

A big data stream is described using 5 $\mathrm{V}prime$s (Volume, Variety, Velocity, Variability, and Veracity). These characteristics impose various challenges. In many cases, data streams are unbalanced, making traditional data mining approaches impossible to employ. The standard data mining approaches are not suitable for imbalanced data streams for achieving analytical efficiency because they require periodic analyses, but big data requires real-time analytics. Additionally, the induction model must be re-run and rebuilt each time to add up the most recent data. Mining these unique streams, on the other hand, is one of the most intriguing research areas. Deep learning (DL) algorithms were developed to increase classification performance for issues requiring large data sets with varying types and characteristics. Feature selection (F.S.) is a critical stage in any classification application. F.S. entails eliminating superfluous and redundant characteristics, resulting in a prediction model that is more efficient, interpretable, and fast. While complete solutions are available for F.S., managing massive data streams that need instantaneous processing is challenging by its own nature. This work provides a hybrid metaheuristic strategy with FFSMOTE for oversampling imbalanced data stream and Honey Bee algorithm for feature selection. Hybridization aims to improve feature selection processes by combing the advantage of both the algorithms. Furthermore, the Ensemble Deep classifiers are used for data stream classification.

关键词： Metaheuristics Big data Predictive models Feature extraction Prediction algorithms Real-time systems Classification algorithms

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ChefAI Text to Instructional Visualization Using Amazon Web Services 5th

ChefAI Text to Instructional Visualization Using Amazon Web ...

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5th international conference on Smart computing and Informatics, SCI 2021

作者： Gupta, Sangeeta Athyaab, Saif Ali Harsh Raj, J. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad500075 India Chaitanya Bharathi Institute of Technology Hyderabad500075 India

ISBN: (纸本)9789811696688

Food industry is one of the most widespread and rapidly growing one with scope for video content generation and analysis via social media. There is an immense rise in the popularity of videos based on food items preparation, as people watching these videos feel attached and involved in the process. Watching someone cook in person as a third person viewer is not as intuitive as watching a pair of hands cooking from the viewer’s perspective. Therefore, in this work, we capitalize on Tasty Video datasets, a collection of 2511 recipes, in order to train a model on temporal dynamics for video-captioning and video-generation tasks. It is proposed to have a system to take in the recipe text and pass it through a transformer-based network to produce the cooking video. It is intended to qualitatively demonstrate the capability to generate plausible videos conditioning on the text as input. Moreover, quantitative experiments will be performed to validate the models using various metrics and human validation. Amazon Web services are the cloud environment to be used for data ingestion, preprocessing, computation, and deployment of the constructed model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Web services

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Analysis of the Performance, Scalability, Availability, and Security of Cloud computing in Different Cloud Environments

Analysis of the Performance, Scalability, Availability, and ...

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international conference on Futuristic technologies (INCOFT)

作者： Basetty Mallikarjuna Manish Kumar Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering (AIML) Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The practise of distributing data, programmes, and other resources over several computers connected via a network is referred to as ‘‘cloud computing Cloud computing allows us to perform all of these tasks by allowing us to use remote servers for information transfer, storing, and retrieval. Because of the huge number of Virtual Machines that cloud computing makes open, we are currently ready to do computations in equal. Performance, scalability, accessibility, and security issues with the cloud could turn out to be the most serious issues from here on out. We will examine the difficulties of scalability, accessibility, and security with regards to cloud computing here, as well as the means we have taken to work on the security and reliability of our foundation while working here. Recent breakthroughs and advancements in information technology have led to a rise in popularity and admiration for cloud computing. Access to services is made feasible via cloud computing at any time. Cloud computing utilises numerous remote network sites and servers that are connected to the internet for the purpose of storing, managing, and processing data instead of utilising a server that is a part of a local area network (LAN) or a single computer. In contrast, traditional computing uses a single server and relies on a single computer. Because of the many advantages it provides, such as massive processing power at a low cost, great performance at a scalable level, stability, accessibility, and availability to customers, cloud computing is currently a highly sought-after service. The performance of cloud data, issues with cloud data security, and difficulties with cloud data environments are all examined in this research study. This poll provides some insightful information that can be used in the analysis of current cloud issues and problems. For upcoming cloud computing enhancements and expansion, testing and analyses of the performance and scalability of software services delivered

关键词： Computers Cloud computing Privacy Scalability Programming Software Virtual machining

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V2X and Vision Fusion Based Undulating Road Recognition and Intelligent Vehicle Control

V2X and Vision Fusion Based Undulating Road Recognition and ...

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international conference on Big data computing and Communications (BIGCOM)

作者： Xingling Shi Liusheng Huang Yingjian Chen School of computer science and technology University of Science and Technology of China Hefei China Intelligent driving R & D department Guochuang (Hefei) Intelligent Automotive Technology Co. Ltd. Hefei China

With the widespread application of intelligent driving technologies, more and more practical problems need to be solved urgently. Focusing on the integration of key intelligent driving technologies: V2X(Vehicle to Everything) and vision perception, this paper proposes a system-level solution to coping with undulating road conditions for intelligent vehicle control. By introducing taillight recognition, vehicle detection performance for puddles on rainy nights is enhanced. At the same time, this solution improves the accuracy, environmental adaptability, and robustness for road roughness detection. On extremely undulating road surfaces, it can avoid or mitigate damages caused by large pits or bumps to the vehicle, improving driving comfortability and vehicle control safety. The fusion algorithm used in this paper, based on the application of V2X equipment, fully excavates the ability of visual perception, can deal with extreme working conditions, greatly improving the driving safety. This will make contribution to the practical application of intelligent driving technologies.

关键词： Roads Vehicle detection Focusing Excavation Surface roughness Robustness Safety

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