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Fast Verification of Network Configuration Updates

computers, Materials & Continua 2023年第1期74卷 293-311页

作者： Jiangyuan Yao Zheng Jiang Kaiwen Zou Shuhua Weng Yaxin Li Deshun Li Yahui Li Xingcan Cao Scool of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Software Engineering Jilin UniversityChangchun130012China School of Software Beijing Jiaotong UniversityBeijing100091China University of British Columbia VancouverV5K1K5Canada

With the expansion of network services,large-scale networks have progressively become *** network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very ***,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential *** changes occur,it is necessary to verify the after-changed ***,for the verification of large-scale network configuration changes,many current verifiers show poor *** order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control *** aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring *** paper presents a method to determine the network range affected by the configuration *** present a flow model and graph structure to facilitate the design of verification algorithms and speed up *** scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and *** supports rich network attributes,meanwhile,has high efficiency and correctness *** experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

关键词： Network verification configuration updates network control plane forwarding information base

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Recommendation System in Industrial data analysis using Machine Learning Techniques 3

Recommendation System in Industrial data analysis using Mach...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Joshi, Tripuresh Joshi, Bhavesh Syed, Karimulla Vijayarani, S. Kumar, Sandeep Naval, Naveen Tula's Institute Deptt. of Electronics & Comm. Engg. UK Dehradun India Graphic Era Hill University Scool of Management India University of Technology and Applied Sciences Shinas College of Engineering and Technology Engineering Department Oman Srm Institute of Science and Technology Department of Mba Chennai India Tula's Institute Deptt. of Computer Science & Engg. U.K. Dehradun India Graphic Era Deemed to Be University Deptt. of Humanities & Social Sci. Dehradun India

ISBN: (纸本)9798350364569

The interactions of users are seamlessly integrated into the product life cycle in the Industry structure of the future. Recommender systems function as a means of filtering information by providing users with recommendations according to their interests in items, music, friends, and topics. Numerous research endeavours have aimed to enhance recommendation accuracy through various techniques like matrix factorization, trust-based approaches, hybrids, and both machine and deep learning methods. However, few studies have explored the utilization of textual opinions for recommendations. Existing research primarily relies on numerical ratings, which may not accurately represent user behavior. This study delves into analyzing the sentiments expressed in textual opinions to gain a deeper understanding of user behavior. Furthermore, textual opinions are processed through the use of methods from Natural Language Processing such as stop-word removal, lemmatization, stemming, and Part-of-Speech (POS) tagging. The aim of a recommendation system is to anticipate user preferences and understand their thought patterns. By considering user preferences and demands, the system delivers tailored information. A deeper examination of the data is necessary to enhance recommendation quality. © 2024 IEEE.

关键词： Matrix factorization

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Transforming Business Intelligence using Data Analytics and Artificial Intelligence 2

Transforming Business Intelligence using Data Analytics and ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Jwala, V.Ch. Joshi, Bhavesh Mishra, Pavan Saha, Aahana Goyal, Shweta Ramesh, Bandla Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India Graphic Era Hill University Scool of Management Bhimtal Campus India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Kr Mangalam University Department of Psychology Gurgaon India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India St. Martin's Engineering College Department of Ai & Ml Dhullapally500100 India

ISBN: (纸本)9798350364699

A wide range of businesses values generation through decision-making is crucial use business intelligence solutions. Business Intelligence is the process of using existing data to extract, analyse, and forecast business-critical insights. This paper looks at how artificial intelligence and data analytics are revolutionising business intelligence and how this has drastically changed how businesses plan their plans and make decisions. Business need to use ML and business intelligence to obtain an competitive hand and pursue informed choices in the present information driven market. This study takes a gander at how strategies from machine learning can be applied to further develop significant business experiences. An apple dataset was exposed to three unique classifiers: Random Forest, Decision Tree, and SVM. This article shows how machine learning algorithms may revolutionise business intelligence and provide businesses with a competitive edge. Businesses may predict future trends, find hidden patterns, and automate decision-making processes by utilising machine learning algorithms. The importance of selecting the right classifier for predictive modelling is further shown by the fact that Random Forest outperformed all other classifiers evaluated by 92%. This study also emphasises how important it is for business organisations to use machine learning and data analytics in order to improve business insights and drive business success. © 2024 IEEE.

关键词： Adversarial machine learning

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Pure Exploration in Bandits with Linear Constraints

arXiv

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arXiv 2023年

作者： Carlsson, Emil Basu, Debabrota Johansson, Fredrik D. Dubhashi, Devdatt Department of Computer Science and Engineering Chalmers University of Technology Sweden Équipe Scool Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL France

We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when the arms are subject to linear constraints. Unlike the standard best-arm identification problem which is well studied, the optimal policy in this case may not be deterministic and could mix between several arms. This changes the geometry of the problem which we characterize via an information-theoretic lower bound. We introduce two asymptotically optimal algorithms for this setting, one based on the Track-and-Stop method and the other based on a game-theoretic approach. Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone. Finally, we provide empirical results that validate our bounds and visualize how constraints change the hardness of the problem. © 2023, CC BY.

关键词： Game theory

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Enhancing Autonomous Driving with Reinforcement Learning and LIDAR-Based Object Detection

Enhancing Autonomous Driving with Reinforcement Learning and...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Dr.B.Lakshmi Krishna Chaithanya Kasthala Nagendar Yamsani G Vasukidevi Mannepuli Srujana A Athiraja Department of Scool of Computing SASTRA Deemed University Tirumalaisamudram Thanjavur Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vignan Bharathi Institute of Technology Hyderabad Telangana India Department of School of Computer Science and Artificial Engineering SR University Warangal Telangana India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of CSE Malla Eeddy Engineering College Maisammaguda Dhulapally Medchal Malkajgiri India Department of CSE (AI&ML) Malla Reddy College of Engineering Hyderabad India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Transportation could be revolutionized by autonomous driving, but sustaining reliability in dynamic, real-world conditions is still an important challenge. In order to overcome this, the research combines Reinforcement Learning (RL) with LIDAR-based object recognition, in particular using a Deep Q-Network (DQN) to improve the formulation of driving strategies. Accurate 3D environmental mapping is made possible using LIDAR, which enhances the system's ability to identify barriers and make judgments in real time. In complicated situations, this integrated method improves obstacle detection accuracy by 30% and reduces collision rates by around 20% when compared to conventional systems that rely on camera and radar technologies. The RL-LIDAR system's real-time applicability is further demonstrated by its 35% increase in decision-making efficiency and 20% reduction in processing times. These developments point to a significant increase in the dependability and security of autonomous cars in difficult situations. In order to further improve system performance, future research will concentrate on expanding this strategy to real-world applications and integrating new sensor technologies.

关键词： Accuracy Laser radar Three-dimensional displays Decision making Transportation Reinforcement learning Real-time systems Object recognition Vehicle dynamics Autonomous vehicles

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Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned DenseNet169 Deep Learning Model

Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned ...

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IEEE International Conference on Multi Topic

作者： Ali Orangzeb Panhwar Shahzad Memon Lachhman Das Dhomeja Mukesh Prasad Asghar Ali Chandio Dr. A. H. S. Bukhari Postgraduate Centre of Information and Communication Technology University of Sindh Jamshoro Pakistan SZABIST University Gharo Department of Computer Science and Digital Technologies Scool of Architecture Compuing and Engineering University of East London United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Department of Artificial Intelligence Science and Technology (QUEST) Nawabshah Quaid-e-Awam University of Engineering Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality issues, which can result in errors, misalignments, and potential harm to patients. However, recent advancements in artificial intelligence (AI) and deep learning have revolutionized medical imaging, with state-of-the-art methods now capable of handling 2D and 3D images. This study focuses on deep-learning approaches for the classification and detection of bone fractures in radiograph images and aims to analyze and compare various deep-learning algorithms and techniques used in fracture detection. It also highlights current cutting-edge approaches in this field, providing insights and guidance for future research and practical applications. In this paper, the application of Fine-tuned DenseNet169 for the automated classification of bone fractures in X-ray images is explored. By using deep learning approaches, our method seeks to enhance the accuracy and efficiency of fracture detection. We trained and evaluated the DenseNet169 model on the MURA Stanford dataset and achieved 83% accuracy in distinguishing fractured and non-fractured elbow bones. The model’s performance highlights the potential of DenseNet169 to assist radiologists in clinical settings, promoting better patient outcomes through prompt and reliable fracture diagnosis.

关键词： Deep learning Accuracy Three-dimensional displays X-rays Bones Data models Convolutional neural networks Diagnostic radiography Artificial intelligence Load modeling

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Transforming Business Intelligence using Data Analytics and Artificial Intelligence

Transforming Business Intelligence using Data Analytics and ...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： V. Ch. Jwala Bhavesh Joshi Pavan Mishra Aahana Saha Shweta Goyal Bandla Ramesh Department of Information Technology SRKR Engineering College Bhimavaram Andhra Pradesh Scool of Management Graphic Era Hill University Bhimtal Campus India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab Department of Psychology KR Mangalam University Gurgaon India Deptt. of Electrical Engineering Graphic Era Deemed to be University Dehradun India Department of AI & ML St. Martin's Engineering College Dhullapally India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

关键词： Support vector machines Hands Machine learning algorithms Decision making Predictive models Market research Business intelligence Predictive analytics Organizational aspects Random forests

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Recommendation System in Industrial data analysis using Machine Learning Techniques

Recommendation System in Industrial data analysis using Mach...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Tripuresh Joshi Bhavesh Joshi Karimulla Syed S. Vijayarani Sandeep Kumar Naveen Naval Deptt. of Electronics & Comm. Engg. Tula’s Institute Dehradun UK India Scool of Management Graphic Era Hill University India Engineering Department Shinas College of Engineering and Technology University of Technology and Applied Sciences Sultanate of Oman Department of MBA SRM Institute of Science and Technology Chennai India Deptt. of Computer Science & Engg. Tula’s Institute Dehradun U.K. India Deptt. of Humanities & Social Sci. Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

关键词： Measurement Industries Deep learning Accuracy Reviews Filtering Tagging Feature extraction Natural language processing Recommender systems

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Mathematical modelling of dendritic complexity mechanism in Alzheimer’s disease

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AIP Conference Proceedings 2023年第1期2872卷

作者： Rashmi Kumari Shivani Goel Subhranil Das Scool of Computer Science Engineering and Technology Bennett Univesity Greater Noida Uttar Pradesh India-201310 Department of EEE BIT Mesra Ranchi Jharkhand India-835215

Alzheimer’s disease (AD) is a neurological disease that wreaks havoc on memory and cognition. Extracellular plaques composed of amyloid peptides and intracellular neurofibrillary tangles of hyperphosphorylated tau proteins are two neuropatho-logical markers of Alzheimer’s disease. Currently, there is no medicine that can cure, stop, or even delay the disease’s progression. In this paper, a mathematical model of AD is developed that incorporates pyramidal neurons, hyperphosphorylated tau proteins, microglia, and peripheral macrophages to calculate dendrites’ complexity. A set of partial differential equations represents the model by incorporating Aβ cascade, tau, and oxidative stress hypothesis. Real-time simulations have been performed to validate the different types of medications that have failed or are presently undergoing clinical trials.

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Justicia: A stochastic SAT approach to formally verify fairness

arXiv

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arXiv 2020年

作者： Ghosh, Bishwamittra Basu, Debabrota Meel, Kuldeep S. School of Computing National University of Singapore Singapore Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden Scool Inria Lille-Nord Europe France

As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure. © 2020, CC BY-NC-SA.

关键词： Stochastic systems

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