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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Milind Shah Priyanka Padhiyar Kajal Parmar Jemisha Patel Sonia Panesar Krupa Raja Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa India Department of Computer Science & Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa India Department of Computer Engineering Vadodara Institute of Engineering Vadodara Gujarat India Department of Computer Science & Engineering Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India Engineer Decimal Point Analytics Vadodara Gujarat India

Medical science is significantly impacted by the applications of Machine Learning (ML). ML is a subset of Artificial Intelligence (AI) technology that improves physicians' efficiency and precision. Recently, machine learning techniques have facilitated a tremendous expansion in the field of medicine. The field of medical science has been revolutionized by machine learning, which provides new methods for analyzing large-scale patient data and providing accurate predictions for diagnosis, treatment, and disease progression. The risk of developing diseases such as cancer, diabetes, and cardiovascular disease has been predicted using machine learning algorithms. These algorithms analyze large sets of patient data, including genetic information, medical history, and lifestyle factors, to recognize patterns and predict disease risk. In addition, machine learning has been used to develop individualized treatment programs for patients. By analyzing patient data, machine learning algorithms can determine the most effective treatments for each patient, thereby reducing the risk of adverse effects and enhancing patient outcomes. Moreover, machine learning techniques have been used to develop image analysis tools for medical imaging, allowing doctors to accurately diagnose diseases such as cancer and identify the location and severity of injuries. This review discusses the current challenges of medical science using machine learning techniques and how medical science problems can be solved using machine learning algorithms.

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Access Optimization of Teaching Resource Management Database Based on Semantic Association Mining

Access Optimization of Teaching Resource Management Database...

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2021 International Conference on Education, Information Management and Service Science, EIMSS 2021

作者： Wang, Lei Chen, Rongli Feng, Huiya Ma, Yue Engineering Research Institute Guangzhou City University of Technology Guangzhou China Dongguan Polytechnic Department of Computer Engineering Dongguan China Academic Service Office Guangzhou City University of Technology Guangzhou China

ISBN: (纸本)9781665427074

In order to improve the service quality of teaching resource management database, it is necessary to construct an optimized access scheduling model of personalized intelligent teaching resource management database, and propose an access scheduling algorithm of personalized intelligent teaching resource management database based on semantic association mining. Using fuzzy fusion degree feature parameter set matching and adaptive filtering method, the block combination control model of personalized data in teaching resource management database is obtained. Constructing a semantic combination autocorrelation distribution detection model of personalized data of teaching resource management database, adopting rough set feature matching and joint cross-correlation feature detection methods to carry out personalized data fusion, cluster analysis and feature reorganization of teaching resource management database, carrying out personalized access and information clustering of teaching resource management database in the reorganized feature space, combining resource combination allocation and joint mining methods. A fuzzy clustering model for adaptive access and scheduling of teaching resource management database is established. According to the results of fuzzy dynamic detection and fusion clustering, the feature matching in the process of access and scheduling of personalized intelligent teaching resource management database is realized. The adaptive access and scheduling model of teaching resource management database is established by the combination of cluster block clustering and piecewise fusion clustering. Tests show that this method has high accuracy and good matching for adaptive access and scheduling of teaching resource management database. © 2021 IEEE.

关键词： Scheduling

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Pose Graph Optimization over Planar Unit Dual Quaternions: Improved Accuracy with Provably Convergent Riemannian Optimization

Pose Graph Optimization over Planar Unit Dual Quaternions: I...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： William D. Warke J. Humberto Ramos Prashant Ganesh Kevin M. Brink Matthew T. Hale Department of Mechanical and Aerospace Engineering University of Florida FL USA Principal Research Engineer EpiSci Science Incorporated Air Force Research Lab Munitions Directorate at Eglin Air Force Base Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this paper we develop a novel algorithm derived from a realistic, correlated model of relative pose uncertainty, and we quantify the resulting improvement in the accuracy of the solutions we obtain relative to state-of-the-art PGO algorithms. Our approach utilizes Riemannian optimization on the planar unit dual quaternion (PUDQ) manifold, and we prove that it converges to first-order stationary points of a Lie-theoretic maximum likelihood objective. Then we show experimentally that, compared to state-of-the-art PGO algorithms, this algorithm produces estimation errors that are lower by 10% to 25% across several orders of magnitude of correlated noise levels and graph sizes.

关键词： Accuracy Uncertainty Three-dimensional displays Quaternions Approximation algorithms Robot sensing systems Numerical models Noise measurement Optimization Rotation measurement

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An end-to-end interpolated Automatic speech recognition system with punctuated transcripts for the Hindi language 6

An end-to-end interpolated Automatic speech recognition syst...

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6th International Conference on Computing, Communication and Security, ICCCS 2021

作者： Joshi, Saurav Kumar, Vishal Machine Learning Engineer Convin.ai India Research Group Dehradun India Bipin Tripathi Kumaon Institute of Technology Dept. of Computer Science and Eng. Dwarahat India

ISBN: (纸本)9781665440394

The Automatic Speech Recognition System (ASR) produces transcripts that often are misinterpreted and confuses the reader due to lack of context and punctuations. The presence of punctuation in the text improves readability and helps in better cognitive understanding. A wide variety of work has been done on English but Hindi which is the third-largest spoken language in the world, after English and Mandarin, still remains in the shadows. This paper aims to extend the technology to a wider section and introduces an end-to-end system, interpolating an automatic speech recognition system and natural language processing to produce high-quality punctuated transcriptions for the Hindi language. An ASR is implemented using the Kaldi toolkit leveraging the hybrid deep neural networks and the punctuation restoration is done with Bidirectional RNNs with an attention network. © 2021 IEEE.

关键词： Speech recognition

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Optimizing Cloud IoT Applications with Machine Learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy Machine learning algorithms Scalability Internet of Things Security Anomaly detection

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A DHR executor selection algorithm based on historical credibility and dissimilarity clustering

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Science China(Information Sciences) 2023年第11期66卷 288-301页

作者： Sisi SHAO Yimu JI Weili ZHANG Shangdong LIU Fei JIANG Zhigang CAO Fei WU Fukang ZENG Jun ZUO Longfei ZHOU School of Computer Science Nanjing University of Posts and Telecommunications Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center School of Foreign Languages Information Engineering University The 28th Research Institute of China Electronics Technology Group Corporation

The security of the dynamic heterogeneous redundancy(DHR) architecture relies on the heterogeneity of its executors, which also defines the vulnerability of the mimic system. In order to select executors with reliable and significant dissimilarity as service executors, we propose a DHR executor selection algorithm based on historical credibility and dissimilarity clustering(HCDC), which adds two metrics of executor historical credibility and dissimilarity. First, to maximize the difference between heterogeneous executor pools,clustering is performed based on the dissimilarity of the executor. Second, the executor with the highest historical credibility is selected from the heterogeneous executor pool as the candidate pool. The historical credibility is dynamically updated by the negative feedback control based on the results of the multi-mode adjudicator. Finally, the dynamic scheduling algorithm selects the executors from the candidate pool to form the set of service executors. The simulation results demonstrate that, in comparison to existing methods,the algorithm reduces the attack success rate and average failure rate while increasing system reliability.

关键词： endogenous security mimic defense DHR executor selection historical credibility

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data

A Comprehensive Approach to Identify Stroke based on Real-ti...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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Nuclear correlation functions using first-principle calculations of lattice quantum chromodynamics

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Physical Review D 2024年第11期110卷 114505-114505页

作者： Debsubhra Chakraborty Piyush Srivastava Arpith Kumar Nilmani Mathur Department of Theoretical Physics Tata Institute of Fundamental Research Homi Bhabha Road Mumbai 400005 India School of Technology and Computer Science Tata Institute of Fundamental Research Homi Bhabha Road Mumbai 400005 India Department of Physical Sciences IISER Mohali Academic Block 1 AB1-1F9 Sector 81 Mohali India

Exploring nuclear physics through the fundamental constituents of the strong force—quarks and gluons—is a formidable challenge. While numerical calculations using lattice quantum chromodynamics offer the most promising approach for this pursuit, practical implementation is arduous, especially due to the uncontrollable growth of quark-combinatorics, the so-called Wick-contraction problem of nuclei. We present here two novel methods providing a state-of-the art solution to this problem. In the first, we exploit randomized algorithms inspired from computational number theory to detect and eliminate redundancies that arise in Wick contraction computations. Our second method explores facilities for automation of tensor computations—in terms of efficient utilization of specialized hardware, algorithmic optimizations, as well as ease of programming and the potential for automatic code generation—that are offered by new programming models inspired by applications in machine learning (e.g., tensorflow). We demonstrate the efficacy of our methods by computing two-point correlation functions for Deuteron, Helium-3, Helium-4, and Lithium-7, achieving at least an order of magnitude improvement over existing algorithms with efficient implementation on GPU-accelerators. Additionally, we discover an intriguing characteristic shared by all the nuclei we study: specific spin-color combinations dominate the correlation functions, hinting at a potential connection to an as-yet-unidentified symmetry in nuclei. Moreover finding them beforehand can reduce the computing time further and substantially. Our results, with the efficiency that we achieved, suggest the possibility of extending the applicability of our methods for calculating properties of light nuclei, potentially up to A∼12 and beyond.

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Implementation of PSO-Optimized PI Control Algorithm for SMES Based DVR in Power Quality Mitigation

Implementation of PSO-Optimized PI Control Algorithm for SME...

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Delhi Section Conference (DELCON), IEEE

作者： Jeba Singh O D. Godwin Immanuel Sheila Mahapatra Rajesh Sharma R Akey Sungheetha G. S. Pradeep Ghantasala Centre for Academic Research Alliance University Bengaluru India School of Electrical and Electronics Sathyabama Institute of Science of Technology Chennai India Department of Electrical and Electronics Engineering Alliance University Bengaluru India Department of Computer Sciences and Engineering Alliance University Bengaluru India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Nowadays, Power Quality (PQ) problems should be more concentrated in distribution systems of power sector. To maintain the power quality in the distribution system the occurrence of PQ disturbances must be identified and compensated. Disturbances like voltage sag and swell are the major issues that cause failure in electrical devices. In this paper, a Superconducting Magnetic Energy Storage (SMES) integrated Dynamic Voltage Restorer (DVR)is implemented to protect the grid voltage from sag, swell and transients. A better solution to PQ disturbances and mitigation related issues are provided through DVR topology. Voltage sag is one of the dominant problems which cause the PQ issues. In this proposed work, sag, swell and outages are mitigated thus the real power is recovered so that the power wastage is prevented. Moreover, this SMES technology-based DVR method compensates the grid from balanced and unbalanced sag, mitigation of outages, interruption, single phase and two-phase swell conditions. The principle, design and working of the SMES based DVR is implemented using MATLAB / SIMULINK. The testing of the established model and performance results are presented.

关键词： Voltage fluctuations Software packages Prevention and mitigation Power quality Reliability engineering Topology Power system reliability Superconducting magnetic energy storage Transient analysis Testing

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A Q-learning-based Multipath Scheduler for Data Transmission Optimization in Heterogeneous Wireless Networks

A Q-learning-based Multipath Scheduler for Data Transmission...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Thanh Trung Nguyen Minh Hai Vu Phi Le Nguyen Phan Thuan Do Kien Nguyen School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam Institute for Advanced Academic Research Chiba University Japan Graduate School of Engineering Chiba University Japan

In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.

关键词： Transport protocols Schedules 5G mobile communication Wireless networks TCPIP Mobile handsets Heterogeneous networks

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