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OntoHDClass: Ontology driven Approach for High Dimensional data Classification Integrating Semantic Measures and Recurrent Neural Network 1

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1st international conference on Advanced Network Technologies and intelligent computing (ANTIC)

作者： Ramanathan, N. Deepak, Gerard Santhanavijayan, A. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

ISBN: (数字)9783030960407

ISBN: (纸本)9783030960407;9783030960391

Clustering the high-dimensional data has remained a burdensome job. Existing clustering algorithms developed are significantly inept. Frequently, in high dimensional data many noises and disturbances are formed and data is masked in existing clusters. Subsequently, we have presented an Ontology driven Approach for High Dimensional data Classification integrating Semantic Measures and Recurrent Neural Network to solve the above problem in this current Paper. The first phase OntoHDClass identifies the problem domain and formulate domain ontologies, accomplishes subspace relevance analysis by computing concept similarities. The second phase, after detection of dense regions elimination of outliers takes place and inliers are extracted. The extracted inliers then yield elite group by Cross entropy and Pearson's correlation. The third phase involves classification of the space using Recurrent Neural Network. The Result are found to be high that the OntoHDClass system reaches an overall Precision, Accuracy, and False Negative Rate of 97.32%, 96.37%, and 0.05% respectively. Elimination of outliers advances the subspace clustering with more accuracy and the elite subspace that are produced is indicated by our result. OntoHDClass can learn the clusters in very subspaces with Lower fault rate besides higher accuracy.

关键词： Concept similarity Cross entropy High dimensional data Ontology Subspace clustering

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Machine Learning Assisted intelligent Reflecting Surface MIMO Communication-Gateway for 6G—A Review 4th

Machine Learning Assisted Intelligent Reflecting Surface MIM...

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4th international conference on computing, Communications, and Cyber-Security, IC4S 2022

作者： Srivastava, Praveen Khera, Shelej School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara144411 India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Kanpur208001 India

ISBN: (纸本)9789819914784

intelligent reflecting surfaces (IRSs) are gaining interest due of their potential coverage and spectral efficiency advantages. However, there are a number of issues that must be resolved in order to materialise these surfaces in practise. A collaborative design for the BS and IRS beamformers is necessary, and the IRS components must be easily modified accordingly. Channel information is crucial to the accuracy of beamformer design deep learning (DL) is a data-driven technique that is essential in overcoming these difficulties. Deep learning is resistant to data flaws and environmental changes because of its faster computation speeds and model-free structure. DL has been demonstrated to be effective at the physical layer for active/passive beamforming, channel estimation, and IRS signal recognition utilising supervised, unsupervised, and reinforcement learning architectures. The methods for creating deep learning-based IRS-assisted wireless systems are summarised in this article. It gives insight to the different deep learning techniques like reinforcement learning, supervised learning, and federated learning which could be used along with IRS. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Beamforming

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An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings 46

An Efficient Deep Transfer Learning Network for Characteriza...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Kulwa, Frank McEwan, Alistair A. Aboyeji, Sunday Timothy Khushaba, Rami Fang, Peng Li, Guanglin University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom SIAT-CAS Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Sydney University Australian Centre for Field Robotics ChippendaleNSW2008 Australia

ISBN: (纸本)9798350371499

Recent advances in stroke rehabilitation technology have been focused on developing intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients' limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP's motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP's motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function. © 2024 IEEE.

关键词： Brain computer interface

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Comparative Analysis of Recommendation systems based on Medical data Sets 4

Comparative Analysis of Recommendation Systems based on Medi...

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4th international conference on advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Barwal, Ravi Kumar Raheja, Neeraj Mullana Haryana Ambala India

ISBN: (纸本)9781665474368

In today's world, a number of most common diseases such as DM (Diabetes Mellitus) for which not assured medicine has searched yet, the capable method for these types of patients for the survival is to take care and proper diet. Fasting Blood Sugar is one of the vital parameters of diabetes mellitus. In the health care system, numerous data of the patients have analyzed that help to obtain insights and assist the estimate of the diseases. Hence, there is a need for an intelligent scheme for estimation of the health situation by analyzing the standard of living, health check reports, and communal activities. However, the recommendation system is becoming the main framework for health care applications. Moreover, fitness intelligent schemes have become a necessary tool in the decision-making approach in health areas. Conversely, people use an online system for understanding the physical condition. Therefore, health recommendation schemes have estimated better results in diagnosis, medical insurance, medical-based management techniques, and other medicine based on the health profile of the patient. This paper provides comparative analysis of various recommendation methods such as MLP, RNN, LSTM, SVM, LR, and NB. These recommendation methods are used for the recommended diseases with Medical data Sets. © 2022 IEEE.

关键词： Long short-term memory

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Adaptation of Seven-Layered IoT Architecture for Energy Efficiency Management in Smart House 10

Adaptation of Seven-Layered IoT Architecture for Energy Effi...

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10th international Scientific conference on Computer Science, COMSCI 2022

作者： Kutseva, Milka Technical University of Sofia Plovdiv Branch Department of Computer Systems and Technologies 25 Tzanko Dustabanov Plovdiv4000 Bulgaria

ISBN: (纸本)9781665497770

This article provides an overview of the different types of Internet of Things (IoT) architectures, with more emphasis on new seven-layered model architecture for future big data-driven smart homes applied when building a system for achieving energy efficiency. One of the next evolutionary stages in Internet-based computations is the Internet of Things (IoT), which is already making a beneficial difference in a wide range of application sectors, including smart cities, sustainable living, healthcare, production, etc. We can transform the conventional grid into a more effective and intelligent energy grid with the help of IoT. The standards for various IoT technologies, including sensing, communication, and computing technologies, as well as how they relate to smart energy grids, will be the main focus of this article. Additionally, this article gives a thorough outline of the prior research on its IoT applications for Smart Grid systems. We conclude by highlighting a brand-new layered architecture that is suggested for the management and processing of massive data in future Smart Homes. The suggested Representational State Transfer (REST)-based architecture for future smart homes' efficient data interchange and processing activities has seven layers, including physical, fog computing, network, cloud computing, service, session, and application. © 2022 IEEE.

关键词： Fog computing

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Infrared and Visible Image Fusion based on Deep Gradient Constraint driven Network 23

Infrared and Visible Image Fusion based on Deep Gradient Con...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Ze Xu, Guoxia Center of China Academy of Launch Vehicle Technology Beijing100076 China Nanjing University of Posts and Telecommunications Jiangsu Nanjing210000 China

ISBN: (纸本)9781665494571

Infrared and visible image fusion technology refers to the fusion of thermal radiation information and texture details contained in infrared image and visible image respectively, so as to obtain high-quality fused images, which has important applications in intelligent monitoring, target detection and tracking, etc. The two images are based on different imaging principles. How to fuse the advantages of their respective images and ensure that the images are not lost is the key of fusion technology. Different from the traditional algorithms that only focus on the global information of superimposed images, this paper proposes a fusion algorithm that pays more attention to the local gradient information of images to extract more detailed gradient information. Experiments show that our algorithm is more effective and advanced. © 2021 IEEE.

关键词： Target tracking Smart cities Fuses Imaging Object detection data mining Convolutional neural networks

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Large Language Model Based Document Query Solution Using Vector databases

Large Language Model Based Document Query Solution Using Vec...

引用

advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Rochan A P Sowmya D. Anand Joseph Daniel Department of Computer Science & Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The world is moving towards a digital landscape where industries are starting to face great challenges in managing and extracting meaningful insights from vast volumes of unstructured data contained in documents such as PDFs, EPUBs and many more. Leveraging the advancements in Large Language Models, this paper presents a helpful approach to address the challenge using a document query solution using LLMs and the efficiency of vector databases. The proposed system presents a way for users to interact with documents and converse with it, allowing users to draw insights and information from the document in a new light and in a new way, facilitating intuitive interaction and information extraction for employees of companies across various industries. The system makes use of LLMs capabilities to interpret and generate responses similar to humans, while vector databases provide efficient data storage and retrieval. This integration enables users to extract insights and information from documents in novel ways, transforming data-driven decision-making processes. By delivering a more intuitive and appealing approach to interact with documents, the proposed approach has the potential to significantly enhance the productivity and effectiveness of data-driven decision-making processes, ultimately boosting innovation and growth across industries.

关键词： Industries databases Decision making User interfaces Transformers Vectors data models

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Enhancement of Industrial Automation using Internet of Things

Enhancement of Industrial Automation using Internet of Thing...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Adlin Sheeba C.A Subasini Kata Purna Avinash S Maheswaran Vijayendran S St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The convergence of Industrial Automation, Cloud computing, and the Industrial IoT Devices has sparked a transformative paradigm shift across diverse industries. This synergy harnesses the power of interconnected smart devices, cloud-based data processing, and automated systems to redefine operational landscapes. In this abstract, we explore the intricate integration of these technologies and their collective impact on industrial realms. IoT sensors embedded within machinery facilitate real-time data collection on performance metrics, environmental factors, and machine health. These data streams, propelled through cloud-based infrastructure, undergo robust processing and analysis, enabling predictive maintenance, process optimization, and informed decision-making. The marriage of cloud computing and IoT not only allows for remote monitoring and control but also facilitates agile responsiveness to dynamic operational needs. By leveraging machine learning algorithms, anomalies are identified, allowing preemptive actions to mitigate potential downtime and enhance productivity. Moreover, the scalability and accessibility of cloud systems offer unprecedented flexibility in adapting to evolving industry requirements. However, such integration necessitates robust security measures to safeguard sensitive industrial data transmitted and stored within cloud environments. Encryption protocols, stringent access controls, and resilient authentication mechanisms become paramount to ensure data integrity and confidentiality. This abstract encapsulates the potential of this amalgamation, illuminating its transformative capabilities in optimizing processes, reducing downtime, and fostering cost-efficient, data-driven decision-making across industrial sectors.

关键词： Industries Cloud computing Technological innovation Automation Decision making systems architecture Hardware

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computing the Relative Value of Spatio-Temporal data in data Marketplaces 22

Computing the Relative Value of Spatio-Temporal Data in Data...

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

作者： Andres Azcoitia, Santiago Paraschiv, Marius Laoutaris, Nikolaos IMDEA Networks Inst Leganes Spain Univ Carlos III Madrid Leganes Spain

ISBN: (纸本)9781450395298

Spatio-temporal information is used for driving a plethora of intelligent transportation, smart-city and crowd-sensing applications. data is now a valuable production factor and data marketplaces have appeared to help individuals and enterprises bring it to market and the ever-growing demand. Such marketplaces are able to combine data from different sources to meet the requirements of different applications. In this paper we study the problem of estimating the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that simplistic but popular approaches for estimating the relative value of data, such as splitting it equally among the data sources, more complex ones based on volume or the "leave-one-out" heuristic, are inaccurate. Instead, more complex notions of value from economics and game-theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. This does not seem to be a coincidental observation related to a particular use case but rather a general trend across different use cases with different objective functions.

关键词： Shapley Value Value of Information Spatio-Temporal data data Marketplaces

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Towards Improving Mathematics Learning Using the Deep Learning Models

Towards Improving Mathematics Learning Using the Deep Learni...

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advances in data-driven Analytics And intelligent systems (ADACIS), IEEE international conference on

作者： Imane Lmati Hind Khoulimi IR2M Laboratory Faculty of Sciences and Techniques Hassan First University Settat Morocco Applied Mathematics and Computing Laboratory Higher Normal School Hassan II University of Casablanca

Nowadays, the majority of intelligent tutoring systems (ITS) have significantly advanced in terms of capabilities and technologies. These systems have evolved to provide personalized and adaptive learning experiences for students. In mathematics, ITS can provide hints or messages to facilitate problem solving, but they do not provide feedback on educational concepts useful for resolving (such as theorems, definitions, etc.). Our methodology enables the generation of feedback in human learning environments. It makes it possible to predict the didactic concepts relevant to solving a given question Q(i) in an exercise based on existing or previously demonstrated data (Q(i-1), Q(i-2),…) using the Math-Bridge ontology. To extract knowledge from mathematical text, we use vectorization with SBERT models to calculate the semantic similarity between ontology concepts and exercises. We tested our approach on a set of exercises from mathematics teaching platforms and compared the number of concepts generated by our approach with those of experts. The results highlight the encouraging potential of our exercise solution method.

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