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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Kumar, K. SriRam Suriya, S. Karpagam Institute of Technology Department of Information Technology Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

Current developments in cloud services, artificial intelligence (AI), and the Internet of Things (IoT) have transformed the traditional healthcare system into smart healthcare. Improved medical services can be achieved by utilising important technologies like AI and IoT. The current study article offers a novel illness detection model for smart healthcare systems that is based on the convergence of AI and IoT. The primary goal of this article is to develop a disease detection model for heart disease by combining data from smart wearable devices with convergence methodologies in artificial intelligence. The model under discussion involves many stages, such as gathering data, pre-processing, categorization, and adjusting parameters. Wearable's and other Internet of Things (IoT) devices facilitate the easy collecting of data, which is then used by artificial intelligence (AI) algorithms to diagnose diseases. For illness diagnosis, the suggested approach makes use of the Swarm Intelligence-Powered Long Short-Term Memory (SILSTM) model. Healthcare data was used to validate the SILSTM model's performance. The research revealed that the SILSTM model that was provided could diagnose cardiac disease with a maximum accuracy of 96.16%. For this reason, smart healthcare systems can make use of the suggested SILSTM model as a suitable instrument for disease diagnosis. © 2023 IEEE.

关键词： Internet of things

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Exponential Synchronization for Mobile Robotic Formation: A Hybrid Dynamic Event-Triggered Intermittent Control Method

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Chang, Zhenyu Guo, Ying Zong, Guangdeng Qi, Wenhai Zhao, Xudong School of Control Science and Engineering Tiangong University Tianjin China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering Qufu Normal University Rizhao China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

To address the formation tracking issue of mobile robotic systems, this paper constructs a novel hybrid dynamic event-triggered intermittent control strategy, which can achieve the exponential synchronization of the systems. Considering the limited control resources, the intermittent control method is introduced into the distributed control strategy to save resources, and the existing intermittent control model is reconstructed to describe the system model better. A hybrid dynamic event-triggered mechanism is developed by combining the dynamic event-triggered method with the time sampling strategy, eliminating the Zeno phenomenon. The control time sequences in the developed intermittent control strategy are automatically selected by the hybrid dynamic event-triggered sequences, rather than artificially designed in advance, which reduces a certain degree of design complexity. The developed control strategy effectively saves control resources while alleviating the burden of network communication. Sufficient conditions for achieving exponential synchronization formation tracking are provided, and the exponential convergence of the formation error is demonstrated through the proposed lemma. Finally, a control task of multi-mobile robots formation is presented to verify the effectiveness of the theoretical analysis. IEEE

关键词： Synchronization

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A Safeguard Agent for Intelligent Health-care Environments

A Safeguard Agent for Intelligent Health-care Environments

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Alzahrani, Abdulkreem Al Baha University Faculty of Computer Science and Information Technology Department of Computer Engineering and Science Al Baha Saudi Arabia

ISBN: (纸本)9798350347050

Intelligent environments rely heavily on the Internet of Things, which can be targeted by malicious attacks. Therefore, the autonomous capabilities of agents in intelligent health-care environments, and the agents' characteristics (accuracy, reliability, efficiency and responsiveness), should be exploited to devise an autonomous intelligent agent that can safeguard the entire environment from malicious attacks. Hence, this paper contributes to achieving this aim by selecting the eight most valuable features out of 50 features from the adopted dataset using the Chi-squared test. Then, three well-known machine learning classifiers (i.e. naive Bayes, random forest and logistic regression) are compared in classifying malicious attacks from non-attacks in an intelligent health-care environment. The highest achieved classification accuracy was for the random forest classifier (99.92%). © 2023 IEEE.

关键词： Internet of things

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Autonomous Driving System Configuration Defect Detection Method Based on Fuzzing 15th

Autonomous Driving System Configuration Defect Detection Met...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Liu, Jinzhao Yu, Xiao Zhang, Li Zhang, Yanqiu Li, Yuanzhang Tan, Kun Tan, Yuan School of Computer Science and Technology Shandong University of Technology Zibo China Department of Media Engineering Communication University of Zhejiang Hangzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Shandong Institute of Information Technology Industry Development Jinan China

ISBN: (纸本)9783031893599

The configuration item values of each component within the autonomous driving system (ADS) are mutually constrained and complexly associated, which leads to problems such as configuration logic faults and memory overflow easily occurring during the configuration process, thus triggering the frequent occurrence of system configuration faults. This paper proposes a dynamic fuzzing method for configuration parameters based on static analysis of source code, through mapping and extracting configuration constraints within the system, realizing dynamic fuzzing for configuration parameters based on source code characterization, and completing the diagnosis of configuration defects and assistive repair functions. The experimental results show that the fuzzing method is effective in detecting and localizing configuration parameter defects under a variety of ADS anomalies, which not only dramatically improves the coverage rate of system configuration defect detection, but also achieves a configuration defect detection rate of 89.9%. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Static analysis

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Features Driven Brain Tumor Detection Using Machine Learning Models 1

Features Driven Brain Tumor Detection Using Machine Learning...

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1st International Conference on Informatics, ICI 2022

作者： Saxena, Bhawna Arora, Anuja Gupta, Mayank Mittal, Prakhar Singh, Japteg Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Brain tumor detection seeks to separate tumorous brain images and healthy images, which is considered to be the first step in diagnosis in order to maximize the likelihood of successful tumorous region detection accuracy. Magnetic Resonance Imaging (MRI) is a viable input in brain tumor anatomy, which helps it in making an effective diagnosis system. To handle this research issue, a feature driven brain tumor detection model is proposed which takes BraTS 2015 MRI images dataset as input and provides segmented result label as output. The dataset contains T1, T2, T1Gd, and FLAIR modalities for each patient. The input data is preprocessed, wherein it is first converted from *** format to NumPy arrays, and then divided into 4 subsets namely HGG-1, HGG-2, HGG-3, and LGG-1. The images are then cropped to discard the unnecessary background and to narrow the object region for all the four modalities. Feature extraction and Grey Level Co-occurrence Matrix (GLCM) generation is then done based on the cropped image. Fourteen Haralick textual features are computed from the GLCM Matrix and standardization of the independent features using feature scaling presents data in a fixed range. Finally, supervised machine learning classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Random Forest are used to train the model. Out of all, Random Forest algorithm reached the highest performance accuracy. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Automated Learning Engines and their Potential to Enhance information Retrieval Accuracy 3

Automated Learning Engines and their Potential to Enhance In...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sakhare, Nitin N. Thangarasu, N. Rajarajeswari, S. Dedgaonkar, Suruchi G. Rengarajan, A. Pareek, Ravi Kant Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

Latest advances in artificial intelligence (AI) have given upward push to automated mastering engines which can be capable of leveraging big quantities of records to become aware of and extract applicable information with top notch accuracy. This has enabled more green records retrieval strategies, which has capability to revolutionize how human beings use and get admission to facts. computerized studying engines are capable of acting complex duties consisting of natural language processing, sentiment analysis, and object recognition, all of which require extensive computation strength. because of this possibilities for more suitable statistics retrieval systems are getting an increasing number of available to *** automatic mastering engine is particularly beneficial in situations wherein there's an abundance of statistics. The engine is able to filter and narrow down the facts to identify and extract simplest the most beneficial data. This allows to keep away from clutter and false positives from the hunt consequences, accordingly main to greater accurate and reliable results. moreover, their capacity to examine from the records they process way that they can be tailor-made to particular use instances, thereby making searches greater green and powerful. furthermore, automated mastering engines can perform at a miles quicker fee than traditional seek techniques. through automating the technique of statistics collection, evaluation, and usage, time is stored and the efficiency of the whole information retrieval system is substantially improved. this may have a ways-reaching implications. © 2023 IEEE.

关键词： information positives quicker system traditional

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Hybrid Cloud Security by Revocable KUNodes-Storage with Identity-Based Encryption

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computer Systems Science & engineering 2022年第12期43卷 985-996页

作者： S.Saravanakumar S.Chitra Department of Information Technology Paavai Engineering CollegeNamakkal637018India Department of Computer Science and Engineering Er.Perumal Manimekalai College of EngineeringHosur63117India

Cloud storage is a service involving cloud service providers providingstorage space to customers. Cloud storage services have numerous advantages,including convenience, high computation, and capacity, thereby attracting usersto outsource data in the cloud. However, users outsource data directly via cloudstage services that are unsafe when outsourcing data is sensitive for users. Therefore, cipher text-policy attribute-based encryption is a promising cryptographicsolution in a cloud environment, and can be drawn up for access control by dataowners (DO) to define access policy. Unfortunately, an outsourced architectureapplied with attribute-based encryption introduces numerous challenges, including revocation. This issue is a threat to the data security of DO. Furthermore,highly secure and flexible cipher text-based attribute access control with role hierarchy user grouping in cloud storage is implemented by extending the KUNodes(revocation) storage identity-based encryption. Result is evaluated using Cloudsim, and our algorithm outperforms in terms of computational cost by consuming32 MB for 150-MB files.

关键词： Cloud computing storage identification based revocation attribute based access control encryption decryption

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AI-Enabled Learning for Imbalanced Class Data using Spark Streaming Concepts 21

AI-Enabled Learning for Imbalanced Class Data using Spark St...

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21st OITS International Conference on information technology, OCIT 2023

作者： Tiwari, Sadhana Zohair, Mohammad Chandra, Ritesh Agarwal, Sonali Agarwal, Parikshit Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Jamia Millia Islamia Department of Computer Engineering New Delhi India SIET Department of Computer Application Prayagraj India

ISBN: (纸本)9798350358230

Chronic Diseases are a very significant problem around the world. Manhandling medical cases often leads to significant repercussions in the treatment process and needs to be detected and dealt with early on. Unusual clinic actions are often related to medical errors, enabling detection and alerting. Huge leaps in machine learning, data science, and big data analytics techniques have led to innovative ways to deal with these issues. As a result, mortality rates can be greatly reduced in all sorts of medical cases. In this work, a new ensemble method is proposed for multiclass classification applied on the US-Chronic Disease Indicator (US-CDI) dataset and validated using UCI-Diabetes data by applying K-means clustering to it to detect chronic disease. The proposed hybrid model, KD-Boost Ensemble, is based on the concept of ensemble voting mechanisms, which combine the predictions of the K-neighbors classifier, Decision Tree classifier, and Gradient Boosting classifier to obtain improved accuracy over the dataset when compared to the individual conventional classifiers. This chronic disease classification achieved 96% accuracy using the proposed model. The devised model is validated through the diabetes dataset in a real-time environment and attains 98% accuracy. © 2023 IEEE.

关键词： Decision trees

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