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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahadevan, Padma Sridharan, A. Sakpal, Shreya Sudesh Gujar, Shantanu Sudhir Labhane, Suchitra Kharche, Akash Isbr Business School Department of Management Studies Karnataka Bengaluru560100 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Law Department of Physical Education & Yoga Tamil Nadu Chennai600077 India Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Cyber Security RochesterNY14623 United States Datta Meghe Institute of Management Studies Department of Mba Maharashtra Nagpur440020 India

ISBN: (纸本)9798331509675

A rapid digital transformation in enterprises has resulted in an explosion of data pertaining to human resources (HR), which has necessitated the use of advanced analytics in order to discover insights that can be put into action. Using contemporary machine learning techniques, this research investigates the use of artificial intelligence (AI) for HR data analytics. The goal of the study is to solve issues in data quality, feature selection, and predictive modelling. A number of resampling strategies are utilized during preprocessing in order to correct class imbalances, and data transformation is utilized in order to extract useful features. Through the utilization of Principal Component Analysis (PCA) for dimensionality reduction, the process of feature selection is streamlined in order to determine which predictors have the most impact. In order to anticipate important HR outcomes, such as employee attrition and performance categorization, feedforward neural networks (FNNs) are utilized for classification purposes. Datasets from the real world of human resources are used to validate the suggested framework, which demonstrates enhanced accuracy and efficiency in the process of gaining insights. The findings of this study emphasize the potential for AI-driven approaches to change HR analytics. © 2025 IEEE.

关键词： Dimensionality reduction

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Performance Analysis of Cloud Hypervisor Using Network Package Workloads in Virtualization

Performance Analysis of Cloud Hypervisor Using Network Packa...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： J. Mary Ramya Poovizhi R. Devi Dept of Computer Science Schools of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) (Deemed to be University) Chennai

AWS (Amazon Web Services), Microsoft Azure, Cloud Zero, Kubernetes, and Google App Engine are cloud computing service providers that can manage client workloads and applications through virtualization and containerization. computing resources are provided by large data centres that consume large amounts of Energy, contributing to global warming. Today's businesses and economies rely heavily on cloud data centres, the world's fastest-growing power users. The latest DCN has two main challenges: scalability and efficiency. A DCN's design directly impacts its scalability, but its power consumption is a significant factor in its cost. If this trend continues, many scientists predict that servers will consume more Energy over their lifetime than they cost. Large-scale infrastructure facilities such as clustering, grids, and clouds of thousands of heterogeneous computers present even more significant energy consumption problems. This paper addresses the energy consumption problem of the hypervisor shown in the cloud data centre. A single hypervisor does not exhibit the same performance on all platforms as other hypervisors in terms of power and energy consumption. Several vital conclusions presented in this paper will provide system designers and operators of data centres with valuable insights that will assist them in placing workloads and scheduling virtual machines in the most power-aware manner. Researchers in this paper provide insight into power-aware workload placement and VM scheduling for system designers and operators.

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Increasing Productivity in Software Development Through the Use of Docker Technology 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Information Technology Guru Ghashi Das University Bilaspur Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The Docker technology makes the process of developing software substantially more effective by resolving issues related to deployment and hosting. Through the use of virtualization, it is able to function independently of the underlying hardware infrastructure, and it relies on its own code to carry out the execution of programs. The encapsulation of an application's whole environment together with its source code is at the heart of Docker's methodology. This enables Docker to efficiently eliminate the problems that are associated with having a variety of working environments, but it may also result in the images having a greater file size. "Images" is the term that Docker uses to refer to these bundled environments. Docker was used in this investigation to carry out a variety of tests, which allowed identical code to be executed on a variety of platforms, so permitting extensive comparisons. Software developers made use of Docker's ability to analyze the outcomes, which enabled them to make decisions based on accurate information. This study simplifies the software development and deployment procedures for the development community by providing significant insights into the benefits and considerations of Docker across numerous platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Quantum Algorithms and the Power of Forgetting 14

Quantum Algorithms and the Power of Forgetting

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14th Innovations in Theoretical computer science Conference, ITCS 2023

作者： Childs, Andrew M. Coudron, Matthew Gilani, Amin Shiraz Joint Center for Quantum Information and Computer Science College ParkMD United States Department of Computer Science University of Maryland College ParkMD United States Institute for Advanced Computer Studies University of Maryland MD United States National Institute of Standards and Technology GaithersburgMD United States

ISBN: (纸本)9783959772631

The so-called welded tree problem provides an example of a black-box problem that can be solved exponentially faster by a quantum walk than by any classical algorithm [3]. Given the name of a special entrance vertex, a quantum walk can find another distinguished exit vertex using polynomially many queries, though without finding any particular path from entrance to exit. It has been an open problem for twenty years whether there is an efficient quantum algorithm for finding such a path, or if the path-finding problem is hard even for quantum computers. We show that a natural class of efficient quantum algorithms provably cannot find a path from entrance to exit. Specifically, we consider algorithms that, within each branch of their superposition, always store a set of vertex labels that form a connected subgraph including the entrance, and that only provide these vertex labels as inputs to the oracle. While this does not rule out the possibility of a quantum algorithm that efficiently finds a path, it is unclear how an algorithm could benefit by deviating from this behavior. Our no-go result suggests that, for some problems, quantum algorithms must necessarily forget the path they take to reach a solution in order to outperform classical computation. © Andrew M. Childs, Matthew Coudron, and Amin Shiraz Gilani;licensed under Creative Commons License CC-BY 4.0.

关键词： Quantum computers

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Reconstructing the Medical Image by Autoencoder with Stochastic Processing in Neural Network 3

Reconstructing the Medical Image by Autoencoder with Stochas...

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3rd International Conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Senthil Kumar, V. Jayalakshmi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9780738146270

The working on image to perform different operation is known as image processing. Digital Image is a collection of a defined number of pixels. Each feature has a particular pixel value at a specific position. Image restoration can be considered as an image enrichment where the final image is printed with more important features that create more appealing for the spectator but it is not essential to create genuine data from the point of a scientific vision. Autoencoder is one of the techniques in artificial neural network to analyze and predict a huge volume of data with different many features. This technique has three stages namely the first one is encoding, second one is compression and third one is decoding. Denoising autoencoder is an enhanced technique for image processing to reconstruct the damaged image to original image. Denoising autoencoder techniques are applying on the medical image to get the high quality image in digital image processing. In this paper, stochastic gradient descent algorithm is applied to predict the damaged pixel and replaced by new pixel values. The result is better than previous result in medical images. © 2021 IEEE.

关键词： Restoration

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A Framework for Tile Processing on Edge Servers for Roadside Traffic Surveillance 23

A Framework for Tile Processing on Edge Servers for Roadside...

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Proceedings of the 7th International Conference on Algorithms, computing and Systems

作者： Saumya Jaipuria Arani Bhattacharya Ansuman Banerjee Advanced Computing and Microelectronics Unit Indian Statistical Institute India Department of Computer Science Indraprastha Institute of Information Technology India

ISBN: (纸本)9798400709098

Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice.

关键词： computer Vision Edge computing Integer Linear Programming

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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Attentive Hawkes Process Application for Sequential Recommendation 28th

Attentive Hawkes Process Application for Sequential Recomme...

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28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Yu, Shuodian Ma, Li Gao, Xiaofeng Guo, Jianxiong Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China

ISBN: (纸本)9783031306716

Sequential recommendation systems which aim to predict the users’ future clicking items are essential in both research and industry services. Based on the contextual characteristics of users, a large number of existing methods exploit abundant information from user behavior sequences and gain users’ interests through expressive sequential models like recurrent neural networks and self-attention mechanism to predict the next item. However, the temporal generative process of behavior sequences, which is crucial to the complex relationships and distributions of users’ dynamic preferences, is rarely considered. In this paper, we propose HawRec (Hawkes process based sequential recommendation), which is a new representation learning approach to model the interacted sequences of users from a temporal point process perspective. In particular, we leverage temporal self-attention mechanism to make an analogy to the self-exciting mechanism of Hawkes process. We bring expressive superiority of neural network into the Hawkes process with non-parametric conditional intensity kernel, resulting in more accurate recommendation and inheriting robustness from the temporal point process. Furthermore, extensive experiments conducted on four public benchmarks demonstrate that our HawRec outperforms various baselines of state-of-the-art sequential recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Assessing the Impact of Physical Configuration and Lead Time on WRF Forecasting of an Extreme Wind Event

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Journal of Meteorological Research 2025年第1期39卷 154-171页

作者： Rocío OTERO Matías SUAREZ Edgardo PIEROBON Leandro MATURANO Ignacio MONTAMAT Juan Ezequiel SANCHEZ Lucia SANDALIO Andrés RODRIGUEZ Denis POFFO Hydrometeorology Laboratory Department of ExactPhysical and Natural SciencesNational University of CordobaCórdobaX5016GCAArgentina Institute for Advanced Studies in Engineering and Technology National Scientific and Technical Research CouncilCordobaX5016GCAArgentina Radar and Remote Sensing Laboratory Department of MathematicsAstronomyPhysics and Computer ScienceNational University of CordobaCordobaX5016HUAArgentina

The central region of Argentina is known to be a source of some most extreme weather events in the world,which are partially associated with the passage of cold fronts accompanied often by extreme wind *** may cause severe property damage and even loss of human ***,there is a lack of studies that evaluate the performance of the numerical weather prediction(NWP)models such as weather research and forecasting(WRF)model in anticipating this type of weather in the *** study compares the performance of the operational WRF in Argentina using four combinations of various planetary boundary layer(PBL)and microphysics parameterization schemes under varied lead times in predicting an extreme wind event(gusts>30 m s^(-1))in Central *** results demonstrate that the WRF model is capable of providing an acceptable prediction of wind speed during an extreme *** is found that no single combination outperforms the others,although there is a slight tendency for Combination A,which utilizes the Mellor-Yamada-Janjic(MYJ)parameterization for the PBL and the Eta similarity parameterization for the surface layer,to more accurately capture the extreme wind *** with wind gust observations at five weather stations,the wind gust parameterization predicted the intensity and occurrence time of the peak wind,with an acceptable bias(time of peak<±1 h).Analysis of grid configurations(resolutions of 4 vs 9/3/1 km)revealed that higher resolution does not imply an improvement in the wind gust forecast for this particular *** regard to lead time,a shorter lead time does not necessarily result in more accurate ***,it is beneficial to conduct multiple sensitivity runs in order to obtain and understand the dispersion of forecasted wind speeds.

关键词： extreme wind wind event weather research and forecasting(WRF)model wind gust forecast cold front lead time

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