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A Trust Level Evaluation Method for intelligent Numerical Control systems driven by Edge Intelligence 8

A Trust Level Evaluation Method for Intelligent Numerical Co...

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8th IEEE international conference on Computer and Communications, ICCC 2022

作者： Zhou, Zheng Yu, Dong Hu, Yi Zhang, Lipeng He, Wuwei Yu, Haoyu University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Chinese Acadamy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Acadamy of Sciences Shenyang China Shenyang Casnc Technology Co. Ltd Digital Workshop Department Shenyang China

ISBN: (纸本)9781665450515

With the increase in the number of networking devices in the industrial field, the total amount of data increases, resulting in huge broadband pressure and power consumption, making the traditional centralized processing outdated, and giving rise to the development of edge computing in the industrial field. To ensure the security of the overall network, the trust concept of the access terminal computer numerical control (CNC) system is introduced as an important basis for improving the manufacturing capability of CNC machine tools, so the inclusion of computing power and trust mechanism in the intelligent CNC system is a good solution to the lack of computing and analysis resources and the ability to reduce the security risks of the CNC system. In this paper, we mainly design a trust level evaluation method for the intelligent CNC system based on edge intelligence, which incorporates the trust evaluation in the edge computing environment, and effective plans and coordinates the cloud, edge, and final resources in the intelligent CNC system. We designed the trust degree model evaluation and constructed the security trust level of the CNC intelligent system with the collaboration between the cloud and the edge. Finally, the experimental scheme is validated and tested in a real environment. © 2022 IEEE.

关键词： intelligent systems

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Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction 3

Comparative Analysis of Machine Learning Techniques for Hear...

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3rd international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Susitha, N. Selvi, H. Mahesh Kumar, N.B. Nagarajan, V.R. Ali, Syed Sumera Kumar, L. Sathish TamilNadu Chennai India Saveetha College of Liberal Arts and Sciences TamilNadu Chennai India School of Computing Science and Hindusthan Institute of Technology Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Faculty of Arts Science Commerce and Management Tamil Nadu Coimbatore India Csmss Chh Shahu College of Engineering Maharashtra Aurangabad India Vit Bhopal University Scse Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798350306989

Due to its substantial effects on society, efforts to enhance heart disease diagnosis and treatment have increased. Through data mining and the archiving of medical records, improved patient management is now possible thanks to the fusion of technology and medical diagnoses. Understanding the interaction between risk factors in the histories of patients and their impact on their prospects for heart disease is of utmost importance. This study aims to accurately predict cardiac disease by analyzing various data elements from patients. The selection and feature extraction methods are the most efficient choices for prediction system for heart disease. The following variables, such as age, sex, occupation, smoking, obesity, diet, exercise, mental stress levels, type of chest pain, history of chest pain, pressure, ECG, and results, have all been identified as significant factors in diagnosing heart disease. Various machine learning methods, such as Multilayer Perceptron (MLP), Naive Bayes (NB), K-nearest Neighbor (K-NN) and Support Vector Machine (SVM) were utilized to analyze the heart disease dataset. These datasets consisted of one with all features and another with only selected features. The objective was to compare the performance of these methods and determine which ones yielded accurate predictions. The results revealed that random forest, using the chosen features, outperformed other artificial intelligence algorithms, including those using all input features, achieving a maximum accuracy rate of 90%. As a support framework for predicting cardiac disease in its early stages, the suggested approach shows promise. This study advances the prognosis of cardiac disease, enables prompt therapies, and enhances patient outcomes by fusing the strength of data-driven AI algorithms with thorough feature selection. © 2023 IEEE.

关键词： Forecasting

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Identification of Illicit Activities & Scream Detection using Computer Vision & Deep Learning 6

Identification of Illicit Activities & Scream Detection usin...

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6th international conference on intelligent computing and Control systems, ICICCS 2022

作者： Mathur, Rohan Chintala, Tejas Rajeswari, D. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9781665410359

Self-reliant technologies as well as fully automated technologies are the few technology behemoths that propel entire disciplines forward by various bounds. Significant advances in technology can be tagged to activities which are better handled and are more visible by means of automation. Machine learning is currently being used to make these computer algorithms usable to be smart enough to carry out tasks with right decision making, causing a massive reduce in human interference within redundant processes. Our proposal adheres to the following ideals: creating a solution that reduces manual work (physically and emotionally) for jobs that can be smoothly automated and processed and at the same time addressing the core statement at hand. At times like this, surveillance cameras serve an important role in ensuring people's safety, but they are only multimedia entities with no intelligence. Because of the increased volume of data generated by security footage, automated video streaming have become necessary for automatically recognising abnormal occurrences. Along with this, audio data of these surveillances are hardly put to use. The project's main goal is to promote safety by techniques incorporating deep learning to optimise the recording and evaluating crimes from sensory Closed-Circuit Television (CCTV), delegating the liability of recognizing illegal conduct to something like a workflow that can recognize trends and classify them for scanning. Along with this, it aims to detect screams from input audios in cases of failure that can correspond to crimes like rape. Scream Detection Model's accuracy comes up till 0.95 in differentiating three categories. © 2022 IEEE.

关键词： Crime

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Advanced Model for Improving IoT Security Using Blockchain Technology 4

Advanced Model for Improving IoT Security Using Blockchain T...

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4th international conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Chauhan, Chetan Ramaiya, Manoj Kumar Department of CSE SIRT SAGE University M.P. Indore India Institute of Advance Computing SAGE University M.P. Indore India

ISBN: (纸本)9781665401180

In order to process and transmit large amounts of data generated by a wide range of devices, the advancement of artificial intelligence (AI) is critical. Anonymous users abuse the decentralisation of the blockchain when it is used to manage IoT devices and their associated large data. One of the most common uses of the platform was for illicit transactions and criminal activity. Since it's built into the hash function, no large hard forks are necessary. There are no changes made to its hash code. intelligent design would allow any errors in the chain to be corrected uniformly and automatically, but this has not been implemented. Big data or any other data-driven business is discouraged from using a new blockchain pattern due to this fact. to propose a advanced blockchain model (SRB) in support of intelligent chain editing in this article as a solution. Hash and an ephemeral trapdoor prevent editorial power from being misused. © 2022 IEEE

关键词： Internet of things

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Distributed Architecture of an Intrusion Detection System in Industrial Control systems 14th

Distributed Architecture of an Intrusion Detection System in...

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14th international conference on Computational Collective Intelligence (ICCCI)

作者： Abid, Ahlem Jemili, Farah Korbaa, Ouajdi Univ Sousse MARS Res Lab LR17ES05 ISITCom H Sousse 4011 Tunisia

ISBN: (纸本)9783031162107;9783031162091

Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. To improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods including machine learning, Big data techniques and deployed in a cloud environment. We use the industrial dataset SWat for the experiment. Our system achieved good results in terms of Accuracy (99%) and response time by using Gradient-Boosted Trees (GBTs) classifier due to the performance of databricks and Apache Spark.

关键词： Intrusion detection Industry 4.0 Industrial control systems Artificial intelligence Machine learning Cloud computing

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Proceedings of ScalAH 2022: 13th Workshop on Latest advances in Scalable Algorithms for Large-Scale Heterogeneous systems, Held in conjunction with SC 2022: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of ScalAH 2022: 13th Workshop on Latest Advances...

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13th IEEE/ACM Workshop on Latest advances in Scalable Algorithms for Large-Scale Heterogeneous systems, ScalAH 2022

ISBN: (纸本)9781665475709

The proceedings contain 6 papers. The topics discussed include: implementing asynchronous Jacobi iteration on GPUs;GPU optimization of lattice Boltzmann method with local ensemble transform Kalman filter;scalable finite-element viscoelastic crustal deformation analysis accelerated with data-driven method;MARs: memory access rearrangements in open MPI;threshold pivoting for dense LU factorization;and mixed-precision algorithm for finding selected eigenvalues and eigenvectors of symmetric and Hermitian matrices.

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MSLS: An intelligent Face Expression Identification System using Modified Sparse Learning Scheme

MSLS: An Intelligent Face Expression Identification System u...

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2022 IEEE international conference on advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Amudha, V. Ramkumar, G. Nagalakshmi, T.J. Nalini, N. Jagadeesh, P. Shyamalabharathi, P. Saveetha School of Engineering SIMATS Department of ECE Chennai India

ISBN: (纸本)9781665495295

Now-A-days, image processing needs and related fields are growing in drastic manner as well as the technology enhance itself periodically. In this Face expression identification is a crucial need and attain more importance. For example, interpersonal communication, computerized education, video and pictures search, intelligent surroundings and safety detection systems might all benefit from the ability to recognize natural feelings in real human faces. Face-recognition software has always been assessed on experimentally controlled data, in which it is not reflective of the real-world environment. Facial expressions may be recognized using a novel interactive technique. Face emotions identification system is a long-Term organization registration challenge for adaptive face expression detection. The following are the major advantages of this procedure: In the first place, a relevant constructs growth model describes the movement patterns of the unique face characteristics of different manifestations in terms of the disciplines;in the second place, a sparsity multi-group object recognition technique builds a pertinent spatial face expression map for each interpretation, in which it is able to explain the massive changes in the facial features of the entire population and repress the partiality due to massive inter-subject facial variability's;in the third place, a feature extraction approach uses both temporal and spatial image exposure information to help identification of a sparsely represented object. © 2022 IEEE.

关键词： Face recognition

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Decoding Urban Industrial Complexity: Enhancing Knowledge-driven Insights via IndustryScopeGPT 24

Decoding Urban Industrial Complexity: Enhancing Knowledge-Dr...

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32nd ACM international conference on Multimedia, MM 2024

作者： Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen Tongji University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong Guangdong Guodi Institute of Resources and Environment Guangzhou China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Research Centre on Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9798400706868

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://***/Tongji-KGLLM/IndustryScope. © 2024 ACM.

关键词： Semantics

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Integrated IoT and Block chain for secured access and managing education data 3

Integrated IoT and Block chain for secured access and managi...

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3rd international conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Ahmad, Nazia George, Remya P Jahan, Rifat Hussain, Sajithunisa Imam Abdulrahman Bin Faisal University Applied College Qatif Computer Department Dammam Saudi Arabia

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The emergence of IoTtechnology has brought a revolutionary change in the world, transforming every sector smarter like smart education, smart industry, smart health through their data driven techniques. Smart education is one of the most impacted areas of this *** education represents a systemwhere traditional processes of the education system are efficiently and effectively carried out by smart modules consisting of inter connected intelligent or smart devices. It includes smart classrooms to smart campuses which use Internet of Things (IoT) Technology. IoT devices can collect real time sensor data to process and analyze. However, due to its centralized structure which is considered as a single point of failure, the privacy and security issue exists. Attackers have access to educational data that they are not authorized to see, edit, or even *** of the architectural drawbacks of IoT can be solved by a technology called Block chain which has the capabilities of data encryption, immutability, auditability, transparency and operational *** this paper we propose a combination of block chain and IoT technologies to store, access and manage education data in a secured way. © 2022 IEEE.

关键词： Internet of things

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W-Trace: Robust and Effective Watermarking for GPS Trajectories 22

W-Trace: Robust and Effective Watermarking for GPS Trajector...

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30th ACM SIGSPATIAL international conference on advances in Geographic Information systems (ACM SIGSPATIAL GIS)

作者： Dadwal, Rajjat Funke, Thorben Nuesken, Michael Demidova, Elena Leibniz Univ Hannover L3S Res Ctr Hannover Germany Bonn Aachen Int Ctr Informat Technol Bonn Germany Univ Bonn Data Sci & Intelligent Syst Grp DSIS Bonn Germany

ISBN: (纸本)9781450395298

With the rise of data-driven methods for traffic forecasting, accident prediction, and profiling driving behavior, personal GPS trajectory data has become an essential asset for businesses and emerging data markets. However, as personal data, GPS trajectories require protection. Especially by data breaches, verification of GPS data ownership is a challenging problem. Watermarking facilitates data ownership verification by encoding provenance information into the data. GPS trajectory watermarking is particularly challenging due to the spatio-temporal data properties and easiness of data modification;as a result, existing methods embed only minimal provenance information and lack robustness. In this paper, we propose W-Trace - a novel GPS trajectory watermarking method based on Fourier transformation. We demonstrate the effectiveness and robustness of W-Trace on two real-world GPS trajectory datasets.

关键词： GPS trajectory Watermarking data provenance data protection

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