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Anomaly Detection for Log using AutoM and Auto-Sklearn Classification Algorithm

Anomaly Detection for Log using AutoM and Auto-Sklearn Class...

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international conference on computing and networking Technology (ICCNT)

作者： G.M. Karthik Ankur Gupta Sh. Rajesh Gupta Amaresh Jha A. Sivasangari B. P Mishra School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of CSE RIMT University Mandi Godindgarh India Department of Management Sanskriti University Mathura Uttar Pradesh India School of Modern Media University of Petroleum and Energy Studies Dehradun School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Electronics JSS Academy of Technical Education Sector-62 Noida India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the realm of application development, logs represent a pivotal resource for the identification and comprehension of application performance, as well as the detection of anomalies, all of which are essential for ensuring the overall efficiency of an application. the proliferation of users has resulted in a substantial increase in the volume of logs available for analysis in the field of log analysis research. Typically, these logs exhibit an unstructured format, largely dictated by the developer’s design choices. Consequently, there is a need for log parsing techniques to in still structure into these logs, enabling the application of machine learning algorithms for the prediction of anomalies. In the present research, the primary objective is the prediction of anomalies within Hadoop distributed File System (HDFS) logs utilizing an AutoML (Automated Machine Learning) tool known as Auto-Sklearn. the research outcomes illustrate the effectiveness of the proposed model, which attains an impressive accuracy rate of $\mathbf{9 9 \%}$ when applied to the dataset. this study underscores the critical role of logs in application development, highlights the importance of log structure transformation, and demonstrates the substantial predictive capabilities of the Auto-Sklearn model in the context of HDFS log analysis.

关键词： Accuracy Machine learning algorithms File systems Predictive models Prediction algorithms Robustness Anomaly detection Tuning Random forests Monitoring

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A Layered Strategy for Reducing Offloading Latency in Fog computing

A Layered Strategy for Reducing Offloading Latency in Fog Co...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Ahmed Chebaane Muhammad Kamran Arshad Florian Burger Abdelmajid Khelil CS Department Institute for Data and Process Science Landshut University of Applied Sciences Landshut Germany CS Department University of Salerno Fisciano Italy

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

Moving applications from the local Internet of things devices to neighboring infrastructures has been the highlight of the current distributed computing era. Due to high latency and network bottlenecks, Cloud computing has become rather problematic for real-time applications. Accordingly, Fog computing has been presented as the missing piece to provide along with cloud computing for virtually all applications. Application virtualization through containers has become more and more popular in the context of Fog computing as it simplifies live migration and, in particular, offloading of time-critical applications or their selected tasks. However, the measurement study of the overall offloading latency in Fog computing has not been sufficiently investigated. this article proposes a measurement analysis of the offloading latency of time-critical application tasks from the end device, such as a vehicle, to surrounding Fog nodes based on Docker containers, Kubernetes, and checkpointing. Based on our layered offloading framework, several offloading scenarios have been studied. the experimental results show that by preparing the Fog nodes in advance through careful, easy arrangements, considerable time can be saved when offloading the application to the Fog node within the Fog cluster. We further consider Vehicular Fog computing (VFC) as a popular example of time-critical Fog computing and assess the suitability of various offloading strategies for VFC use cases.

关键词： Cloud computing Multi-access edge computing Atmospheric measurements Prefetching Containers Particle measurements Real-time systems Time factors Internet of things Edge computing

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Dynamic Modeling and Validation of Soft Robotic Snake Locomotion

Dynamic Modeling and Validation of Soft Robotic Snake Locomo...

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international conference on Control, Automation and Robotics (ICCAR)

作者： Dimuthu D. K. Arachchige Sanjaya Mallikarachchi Iyad Kanj Dulanjana M. Perera Yue Chen Hunter B. Gilbert Isuru S. Godage School of Computing Jarvis College of Computing & Digital Media DePaul University Chicago IL USA Dept. of Multidisciplinary Engineering Texas A&M University College Station TX USA Dept. of Biomedical Eng. Georgia Institute of Technology Atlanta GA USA Dept. of Mechanical & Industrial Eng. Louisiana S. University Louisiana USA Dept. of Eng. Tech. & Industrial Distribution and J. Mike Walker '66 Dept. of Mechanical Eng. Texas A&M University College Station TX USA

Soft robotic snakes made of compliant materials can continuously deform their bodies and, therefore, mimic the biological snakes9; flexible and agile locomotion gaits better than their rigid-bodied counterparts. Wi...

Soft robotic snakes made of compliant materials can continuously deform their bodies and, therefore, mimic the biological snakes' flexible and agile locomotion gaits better than their rigid-bodied counterparts. Without wheel support, to date, soft robotic snakes are limited to emulating planar locomotion gaits, which are derived via kinematic modeling and tested on robotic prototypes. Given that the snake locomotion results from the reaction forces due to the distributed contact between their skin and the ground, it is essential to investigate the locomotion gaits through efficient dynamic models capable of accommodating distributed contact forces. We present a complete spatial dynamic model that utilizes a floating-base kinematic model with distributed contact dynamics for a pneumatically powered soft robotic snake. We numerically evaluate the feasibility of the planar and spatial rolling gaits utilizing the proposed model and experimentally validate the corresponding locomotion gait trajectories on a soft robotic snake prototype. We qualitatively and quantitatively compare the numerical and experimental results which confirm the validity of the proposed dynamic model.

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Computational Intelligent Techniques and Analysis on Automated Retinal Layer Segmentation and Detection of Retinal Diseases

Computational Intelligent Techniques and Analysis on Automat...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： T. M. Sheeba S. Albert Antony Raj M. Anand Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

the number of critically sick patients is increasing, placing pressure on the healthcare system, which is witnessing twice as many new cases as a year ago. Artificial intelligence (AI) technologies are advantageous to the health business because they offer better and more accurate autonomous sickness detection. Artificial intelligence applications are revolutionizing image classification and pattern recognition, two fields of commerce. Optical coherence tomography, or OCT, is a noninvasive imaging method that provides high resolution without causing pain to the patient. It is used to study the retina’s anatomy. Non-invasive images of a cross-sectional volume of tissues may be obtained using OCT. Picture segmentation techniques and structure classification strategies that have been found to be the most successful among the presently available research are discussed in this review study. this study presents a short overview of all of the most recent ways for applying machine learning algorithms to the problem of predicting retinal abnormalities based on OCT images. Readers will be able to find the algorithm that delivers the best degree of accuracy for retinal classification of infected and healthy eyes while also taking the least amount of processing time for layer segmentation using the techniques mentioned in this research study. As a consequence, accuracy improves while the amount of time needed decreases.

关键词： Image segmentation Machine learning algorithms Pain Optical coherence tomography Imaging Medical services Retina

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Social Media Text to Formal Representation: A Knowledge Graph Approach

Social Media Text to Formal Representation: A Knowledge Grap...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： Yaswanth Phani Kommineni Srilekha Somanchi Boina Sai Pavan Kumar Yash Ladani Lekshmi S Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Social media is an online platform where users freely share their views on various topics. By sharing their views, they come to a mutual agreement or disagreement with other users’ views. Users are quite active on social media and the number of users is growing exponentially with time. this leads to an abundance of data available through social media. this data helps us in curating different research problems. One such research problem can be finding similarities between users by analyzing user data. Data on social media is available in many forms like textual posts, comments, likes, shares, etc. Users often tend to express their diversity of opinions widely through comments on social media. Many opinionated and active users on social media are found in the comments section under a post. Hence, comments play a major role in determining similarity in user judgment about a topic. Likewise, people liking a post is also an essential feature in finding similarities between users. People with similar opinions tend to like posts of a specific type. this paper talks about how comments, textual posts, and likes for comments can be combined to form something called a keyword or a tag. these tags that are extracted from user data are further used in building a tag network. the tag network is used to make communities of users with similar interests. User grouping can also be done based on these extracted tags and comments under the posts. the proposed methods in this paper to achieve the above-mentioned are TF-IDF (Term frequency-inverse document frequency) followed by the TextRank algorithm.

关键词： Social networking (online) Communication systems Buildings Clustering algorithms Knowledge graphs Feature extraction Data mining

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Improving the Performance of OPTICS on Short Text Clustering by IsoKernel and UMAP

Improving the Performance of OPTICS on Short Text Clustering...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Xuechen Zhang Peng Wu School of Cyber Science and Engineering Nanjing University of Science & Technology Nanjing China School of Intelligent Manufacturing Nanjing University of Science & Technology Nanjing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Clustering of short text streams has become significant due to the popularity of social media platforms. such as Twitter, Facebook, and Weibo. Clustering of short text can automatically detect new topics. Most existing approaches exploit dimensionality reduction and feature representation methods to enhance the clustering quality. However, it's a big challenge that how to improve the clustering performance under the aspects of large noise and high dimensionality of short text. OPTICS(Ordered Partitions Clustering Algorithm) algorithm is a density-based clustering algorithm recognized for its robust handling of noise. However, OPTICS encounters challenges when dealing with high-dimensional datasets. this paper proposes a novel short text clustering method that uses UMAP(U niform Manifold Approximation and Projection) and IsoKernel(Isolation Kernel) to improve the performance of OPTICS(Ordered Partitions Clustering Algorithm) in short text clustering tasks. the proposed method can overcome the problems of large noise and high dimensionality in short texts. the experimental results prove that proposed method is better than the baseline, which is evaluated on purity, AMI and NMI metrics.

关键词： Integrated optics Social networking (online) Computational modeling Noise Signal processing algorithms Clustering algorithms Approximation algorithms Partitioning algorithms Optical noise Optical signal processing

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Evaluating and Contrasting the Performance of Web Development Technologies

Evaluating and Contrasting the Performance of Web Developmen...

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international conference on Communication and Electronics Systems (ICCES)

作者： C. Swedheetha Shruthika C Rubi Preethi Vedha Varshini T B J Sabreen Maryam P. Manikandan Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

the next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites; PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. this research study includes the performance evaluation of web development technologies.

关键词： Performance evaluation Hands Concurrent computing Technological innovation Buildings Libraries Service-oriented architecture Security Optimization Next generation networking

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Enhancing Load Forecasting by Clustering in distributed Microgrids Based Energy Internet Framework

Enhancing Load Forecasting by Clustering in Distributed Micr...

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Big Data, Cloud computing, Data Science & Engineering (BCD)

作者： Anjana Vijayan Jung-Min Yang School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

the intermittent nature of distributed renewable en-ergy sources and varying patterns of end-user loads in microgrids necessitate the manufacturers to accommodate unforeseen and expected fluctuations in energy consumption and production. Lack of accurate load forecasting may result in ineffective harnessing and storage of renewable energy and complicates energy trading and dynamic pricing. Existing literature on load forecasting of microgrids is limited to single microgrids, and the possibility of inter-microgrid communication is not addressed sufficiently. this study explores the enhancement of load fore-casting in an Energy Internet (EI) framework among multiple interconnected microgrids. A novel approach is proposed which integrates k-means clustering with Support Vector Regression (SVR) to forecast the load in the EI. We also investigate the influence of the communication network of the EI in improving short-term load forecasting (STLF).

关键词： Support vector machines Renewable energy sources Accuracy Load forecasting Energy Internet Microgrids Production Wind power generation Vectors Load modeling

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Detection of Online Hate in Social Media Platforms for Twitter Data: A Prefatory Step 9th

Detection of Online Hate in Social Media Platforms for Twitt...

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9th international conference on Frontiers in Intelligent computing: theory and Applications, FICTA 2021

作者： Ramana, D. Venkata Swetha Reddy, T. Hanumantha Rao Bahadur Y Mahabaleswarappa Engineering College Karnataka Ballari India

ISBN: (纸本)9789811666155

Online social media (OSM) plays a major role in today’s life by providing people with a platform to share individual contents such as photos, ideas, opinions, and videos. However, at the same time, there are many negative carry-overs on social media users, which lead to uncontrolled and offensive communication called online hate. It has become a major concern for all online social media (OSM) users as it creates nothing but a negative influence on the users. Hence, developing a better generalized online hate classifier and network analyzer across domains is the challenge. this article highlights a novel 8-stage methodology that is proposed to develop an efficient generalized online hate classifier and network analyzer both which can help in overcoming the challenges. As a preliminary step toward 8-stage process proposed, the dataset related to women is collected from Twitter and analyzed to find out the frequent words used, number of retweets increased are negative tweets. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social networking (online)

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Cherry: A distributed Task-Aware Shuffle Service for Serverless Analytics

Cherry: A Distributed Task-Aware Shuffle Service for Serverl...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Nikitas, Nikolaos Konstantinou, Ioannis Kalogeraki, Vana Koziris, Nectarios CSLAB NTUA Athens Greece Univ Thessaly Volos Greece Athens Univ Econ & Business Athens Greece NTUA Athens Greece

ISBN: (纸本)9781665439022

While there has been a lot of effort in recent years in optimising Big Data systems like Apache Spark and Hadoop, the all-to-all transfer of data between a MapReduce computation step, i.e., the shuffle data mechanism between cluster nodes remains always a serious bottleneck. In this work, we present Cherry, an open-source distributed task-aware Caching sHuffle sErvice for seRveRless analYtics. Our thorough experiments on a cloud testbed using realistic and synthetic workloads showcase that Cherry can achieve an almost 23% to 39% reduction in completion of the reduce stage with small shuffle block sizes, a 10% reduction in execution time on real workloads, while it can efficiently handle Spark execution failures with a constant task time re-computation overhead compared to existing approaches.

关键词： Big Data Analytics Frameworks distributed Systems Cloud computing Serverless Architecture

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