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Quantum spin systems: Toroidal classification and geometric duality

Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3517-3536页

作者： Lijo, Jithy Janardhanan Subramanian, Saleema Department of Computer Science Christ University Karnataka Bengaluru India Department of Statistics and Data Science Christ University Karnataka Bengaluru India Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Karnataka Bengaluru India

Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Biwolf optimization Feature extraction LP Norm Regularized deep CNN Pre-processing Yolo V5

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A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies 2

A Machine Learning Approach for Revving Up Revenue of Indian...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Tungar, Srushti Kailas Umme Salma, M. Department Of Statistics And Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350381665

This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. © 2024 IEEE.

关键词： Machine learning

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Construction of Optimal Mixed-Level Uniform Designs

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Journal of Systems science & Complexity 2024年第2期37卷 841-862页

作者： CHATTERJEE Kashinath LIU Min-Qian QIN Hong YANG Liuqing Department of Population Health Sciences Division of Biostatistics and Data ScienceAugusta UniversityGA 30912USA NITFID LPMC&KLMDASRSchool of Statistics and Data ScienceNankai UniversityTianjin 300071China School of Statistics and Mathematics Zhongnan University of Economics and LawWuhan 430073China

The theory of uniform design has received increasing interest because of its wide application in the field of computer *** generalized discrete discrepancy is proposed to evaluate the uniformity of the mixed-level factorial *** this paper,the authors give a lower bound of the generalized discrete discrepancy and provide some construction methods of optimal mixed-level uniform designs which can achieve this lower *** methods are all deterministic construction methods which can avoid the complexity of stochastic *** saturated mixed-level uniform designs and supersaturated mixed-level uniform designs can be obtained with these ***,the resulting designs are also χ^(2)-optimal and minimum moment aberration designs.

关键词： Generalized discrete discrepancy Hadamard matrix mixed-level design orthogonal array supersaturated design

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LBP-GLZM Based Hybrid Model for Classification of Breast Cancer 2

LBP-GLZM Based Hybrid Model for Classification of Breast Can...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Mahapatra, Debangana Umme Salma, M. Department of Statistics and Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350383287

Classifying mammogram images is difficult because of their complex backgrounds and the differences in resolutions across the images. One of the toughest parts is telling the difference between harmless (benign) and harmful (malignant) tissue. This is hard because the differences between them are incredibly subtle. As a consequence, the distinctive features embedded within tissue patches become not just relevant but critical for the accurate and automatic classification of these images. Traditionally, efforts to automate this classification process have encountered limitations when relying on a singular feature or a restricted set of characteristics. The subtle variations in texture within these images often render such approaches insufficient in achieving high-quality categorization results. Recognizing this, the present investigation undertakes a more comprehensive approach by incorporating distinct feature extraction techniques - specifically, the utilization of Local Binary Pattern (LBP) and Gray Level Zone Matrix (GLZM). These techniques are adept at capturing and delineating the nuanced texture features inherent in mammogram images. By extracting and analyzing these textural nuances, the aim is to construct a hybrid model capable of classifying mammograms into three distinct categories: malignant, benign, and without the necessity for further examination or follow-up. This proposed hybrid model holds significant promise in the field of mammography classification by leveraging the strengths and complementary attributes of multiple feature extraction methods. The integration of LBP and GLZM aims not only to enhance the accuracy of classification but also to improve the robustness of the system in identifying subtle yet crucial differences in tissue textures. Ultimately, the goal is to create a hybrid feature extraction framework that augments the diagnostic capabilities of mammography, providing more precise and reliable categorization of breast tissue for effect

关键词： Local binary pattern

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Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

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computer Modeling in Engineering & sciences 2024年第3期138卷 2471-2495页

作者： Wimonmas Bamrungsetthapong Pramote Charongrattanasakul Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull *** control chart developed supports the examination of the mean lifespan variation for a particular product in the process of *** control limit levels are used:the warning control limit,inner control limit,and outer control ***,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control *** control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control ***,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

关键词： Modified DS np chart generalizedmultiple dependent state sampling time truncated life test Weibull distribution average run length average sample size

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Implementation of Movie Recommendation System Using Hybrid Filtering Methods and Sentiment Analysis of Movie Reviews 2

Implementation of Movie Recommendation System Using Hybrid F...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Naskar, Ishika Joseph, Niju P. Department of Statistics and Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350383287

In present era of digitization of entertainment, immense volume of movies are produced, which results in the necessity of sophisticated recommendation systems. In the streaming platform these systems empower users to discover new and relevant movies, benefiting both viewers and the entertainment industry. This research paper offers a comprehensive method for incorporating movie review sentiment analysis into a hybrid recommendation system. The study focuses on 4890 movies using a broad dataset containing the detailed descriptions of the movies along with the reviews. To employ the demographic filtering, the popularity score of the movies were calculated, then to apply the collaborative filtering, the textual movie descriptions were vectorized using the countvectorizer method. To predict the sentiment of the movie reviews, the high accuracy model "ControX/Sen1"was used. This hybrid recommendation system ranked the movies based on the user's preferences by employing cosine similarity, the sorted list was further filtered with the positive sentiment reviews. By including sentiment analysis, this research advances sophisticated movie recommendation systems by providing a comprehensive method for addressing user preferences and emotional resonance in film selections. © 2024 IEEE.

关键词： Recommender systems

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Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT:A Survey

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Chinese Journal of Electronics 2024年第4期33卷 859-874页

作者： Jiwei HUANG Fangzheng LIU Jianbing ZHANG Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Department of Computer Science and Technology China University of Petroleum

With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

关键词： Internet of things Mobile edge computing Quality of service Quality of service evaluation Quality of service optimization

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2024年第17期84卷 1-21页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Sequential good lattice point sets for computer experiments

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science China Mathematics 2024年第9期67卷 2153-2170页

作者： Xue-Ru Zhang Yong-Dao Zhou Min-Qian Liu Dennis K.J.Lin NITFID LPMC&KLMDASRSchool of Statistics and Data ScienceNankai UniversityTianjin 300071China Department of Statistics Purdue UniversityWest LafayetteIN 47907USA

Sequential Latin hypercube designs(SLHDs) have recently received great attention for computer experiments, with much of the research restricted to invariant spaces. The related systematic construction methods are inflexible, and algorithmic methods are ineffective for large designs. For designs in contracting spaces, systematic construction methods have not been investigated yet. This paper proposes a new method for constructing SLHDs via good lattice point sets in various experimental spaces. These designs are called sequential good lattice point(SGLP) sets. Moreover, we provide efficient approaches for identifying the(nearly)optimal SGLP sets under a given criterion. Combining the linear level permutation technique, we obtain a class of asymptotically optimal SLHDs in invariant spaces, where the L1-distance in each stage is either optimal or asymptotically optimal. Numerical results demonstrate that the SGLP set has a better space-filling property than the existing SLHDs in invariant spaces. It is also shown that SGLP sets have less computational complexity and more adaptability.

关键词： contracting space maximin distance nested Latin hypercube design sequential design space-fillingdesign

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