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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Baviskar, Vishal Shekhar Radha, D. Sankari, S S Uma Department of Computer Science and Engineering Amrita School of Computing amrita vishwa vidyapeetham Bangalore India Indian Space Research Organisation Vikram Sarabhai Space Centre Bangalore India

ISBN: (纸本)9798350335095

Cryptocurrencies have become a major element in enterprises and financial market showing promising potential during the past ten years. Predictions that are accurate can help cryptocurrencies investors to make the best decisions and possibly enhance their earnings. For this work 4 cryptocurrencies namely Bitcoin, Binance Coin, Ethereum and Tether (USDT) are considered. The considered dataset is for around 5 years, which has a total of 9 columns, One Date Column ranging from year 2017 to 2022 and for each of the Coin there is the Closing price and the Volume. The Dataset is collected from Kaggle. Attempts are made to build the models in various ways, by dealing with certain features or by taking a subset of the dataset. All the models, according to the way the dataset was dealt were evaluated based on certain evaluation metrics like R-squared, mean square error and root mean square error. The models used are KNN, Decision Tree, Random Forest, XGBoost and CatBoost. Visualized and understood how the prices of those cryptocurrencies were impacted by other features of the dataset. The models that outperformed are Random Forest and CatBoost. Achieved RMSE was 365, 2.99, 29.99 and 0.0023 for Bitcoin, Binance, Ethereum and USDT respectively. © 2023 IEEE.

关键词： Bitcoin

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Comparison of Novel Machine Learning Algorithms for Predicting Chronic Renal Disease

Comparison of Novel Machine Learning Algorithms for Predicti...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Hima Bindu, G.B. Valluru, Dinesh Vachhani, Harshita Sunil Kumar, M. Ganesh, D. Neelima, P. The Apollo University School of Technology Department of of CSE Andhra Pradesh Chittoor India MLRITM Engineering College Department of IT Telangana Hyderabad India Pratibha College of Commerce and Computer Studies Chinchwad Dept.of Computer Science Maharshtra Pune India School of Computing Andhra Pradesh Tirupati India SPMVV School of Engineering & Technology Department of CSE Tiruapti India

ISBN: (纸本)9798350381887

Chronic Renal Disease (CRD) is a serious sickness that affects the entire world and is the primary cause of mortality for many other illnesses. Due to the lack of visible symptoms at the beginning of CRD, people usually neglect the illness. Early CRD diagnosis enables individuals to receive prompt therapy to slow the course of their illness. Because it quickly and accurately identifies potentials, model-based machine learning can help doctors accomplish this aim. In this paper, an ML strategy for CRD forecasting. The UCI machine learning repository provided the CRD dataset, which has a high number of uncertain data values. To address unknown values or unknown data, KNN estimation was employed, which picks multiple instances that contain the most comparable measures to manage the undetermined data value for every insufficient sample. Missing data is prevalent in real therapy situations, therefore individuals may skip particular measurements for a variety of reasons. The technique is generated using five machine learning algorithms (SVM, Logistic Regression, k-nearest neighbor, Random Forest, and Neural Network). Random forest outscored the other models of machine learning regarding accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Favorite+: Favorite Tuples Extraction via Regret Minimization 22

Favorite+: Favorite Tuples Extraction via Regret Minimizatio...

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31st ACM International Conference on Information and Knowledge Management, CIKM 2022

作者： Xie, Min Liu, Yang Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)9781450392365

When faced with a database containing millions of tuples, a user might be only interested in some of them. In this paper, we study how to help an end user to find the favorite tuples based on the recent advancements in regret minimization queries, which guarantees the tuples returned are not far from the user's favorite tuple in the database, without asking the user to scan the entire database. We consider three types of regret minimization queries: (1) End-to-end query: Given an output size k, we directly return a subset of at most k tuples from the database;(2) Interactive query: We identify the user's favorite tuple via user interaction, where a user might be presented with a few pairs of tuples, and the user is asked to indicate the one s/he favors more from each pair;and (3) Incremental query: Analogous to how we use search engines, if the user is not satisfied with the current tuples, we continually return more. We developed a demonstration system, called Favorite+, by supporting the above queries. We demonstrate that the system could help the users to find their favorite tuples in the database efficiently and effectively. © 2022 ACM.

关键词： Data Analytics

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Editorial: Special Section on Egocentric Perception

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第6期45卷 6602-6604页

作者： Furnari, Antonino Crandall, David Damen, Dima Grauman, Kristen Farinella, Giovanni Maria University of Catania Department of Mathematics and Computer Science Catania95124 Italy Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN47405 United States University of Bristol Department of Computer Science BristolBS8 1QU United Kingdom University of Texas at Austin Department of Computer Science AustinTX78712 United States Fair United States

The papers in this special issue focus on egocentric perception. It gathers recent advances in this field that brings together multiple communities including computer vision, machine learning, and multimedia.

关键词： Special Issues And Sections Collision Avoidance Graph Theory computer Vision Benchmark Testing Transfer Learning Trajectory Navigation

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A Systematic Overview on Simulation of Connected Vehicles Infrastructure in Smart Cities 1

A Systematic Overview on Simulation of Connected Vehicles In...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Gupta, Manik Dhaou, Imed Ben Garg, Kamal Deep Sharma, Bhisham Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

The development and use of connected vehicles are becoming increasingly important areas of study in the wireless networking and transportation research communities. These networks can provide various services such as collision warning. Unlike other types of devices, such as hand-held devices, the vehicles' nodes are not limited by their energy consumption and storage capacity. Therefore, the development of connected networks can be carried out with minimal resources. It is necessary to run a simulation in order to determine whether they are feasible. The availability of multiple devices and protocols has led to the creation of new utilization scenarios. Due to the complexity of the task, many simulation tools were developed during the past few years. This paper systematically reviews the current state of these tools and discusses their capabilities to evaluate the various scenarios related to the development of connected vehicles network. © 2023 IEEE.

关键词： Energy utilization

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SAHA: An SDN Enabled Fog Architecture for Healthcare Applications 24

SAHA: An SDN Enabled Fog Architecture for Healthcare Applica...

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25th International Conference on Distributed computing and Networking, ICDCN 2024

作者： Sarkar, Joy Lal Peng, Sheng-Lung Deb, Subhrajyoti Muthaiah, U. Mondal, Ayan Gopal, Gaddam Venu Techno College of Engineering Agartala India National Taipei University of Business Taiwan Icfai University Tripura India Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Iit Indore India Koneru Lakshmaiah Education Foundation India

ISBN: (纸本)9798400716737

In this paper, a software-defined Fog architecture is suggested to initiate an immediate course of treatment for a patient. Sensors on a patient outfitted with various sensors can read physiological data. This work uses two types of queries such as strict and flexible queries. Strict inquiries are used when an emergency is recognised due to a patient's criticality. Non-emergency queries are referred to as flexible inquiry. The strict queries are executed at a nearby edge node, and the data is sent to the fog node via a switch or SDN controller. Instead, a patient can move for treatment depending on the proximity of medical professionals, the quality of the facilities, and the travel time. The task of determining availability falls to a Local Medical Team (LMT). It is also the responsibility of a Global Medical Team (GMT) to keep track of the patient's health and provide the LMT with any information that is required. We can observe from the outcomes that the suggested algorithm outperforms the baseline algorithms. © 2024 ACM.

关键词： Travel time

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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A cascade classifier-based system for identifying And counting the different kinds of vehicles using Open CV

A cascade classifier-based system for identifying And counti...

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Amit Kumar Sunita Yadav Department of Computer Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Vehicle identification and counting are becoming more important tools for highway management. Anyhow, because cars come in a variety of sizes, identifying them remains a test that directly affects the accuracy of vehicle counts. The advancements in vehicle detection technology have significantly progressed alongside the development of autonomous driving and intelligent transportation research. This study focuses on video-based algorithms for vehicle detection and counting, utilizing OpenCV technology as the foundation for these techniques. The suggested solution use the background subtraction method to identify foreground elements in a subsequent movie. Using OpenCV, Numpy, and object detection techniques, from those moving objects, automobiles are spotted, categorised, and counted for traffic estimation. The results of an experiment reveal that the proposed counting techniques are about 84.60% percent accurate. The method is able to provide good vehicle counting accuracy with quick calculation when combined with quick motion detection and tracking techniques.

关键词： Road transportation Accuracy Tracking Vehicle detection Transportation Estimation Object detection Motion pictures Motion detection Automobiles

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FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Heeyong Yoon Kang-Wook Chon Min-Soo Kim Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea School of Computer Science and Engineering KOREATECH Cheonan Republic of Korea School of Computing KAIST Daejeon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy.

关键词： Cloud computing Adaptation models Federated learning Computational modeling Time series analysis Mathematical models Graph neural networks Servers Internet of Things Edge computing

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Shuffle-F-ZFNet: ShuffleNet Fuzzy Zeiler and Fergus network for data aggregation in WSN data communication

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Wireless Networks 2025年 1-24页

作者： Dhanabal, S. Goswami, Chandrashekhar Praveen, Rani Venkata Satya Vetriselvi, T. Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Thottiam Trichy India Faculty of Computing and Informatics Sir Padampat Singhania University Udaipur India Digital Engineering & Assurance Hitech City Hyderabad India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Wireless sensor networks (WSN) have acquired great importance from industries owing to their wide range of applications. In system security, intrusion detection systems (IDS) play a significant role. In WSN, data aggregation protocols are utilized to maximize the network’s life span and minimize every sensor node’s communication weight as well as energy consumption. However, WSN faces energy and resource conservation conflicts. Furthermore, assuming uniform initial energy levels among sensors in existing algorithms is impractical for real-world applications. This variation in initial energy levels has a significant effect on data aggregation within sensor networks. To mitigate these issues, an innovative approach named ShuffleNet Fuzzy Zeiler and Fergus network (Shuffle-F-ZFNet) for data aggregation in WSN is proposed. Primarily, the system model for dynamic cluster WSN is simulated, by considering energy, mobility, link lifetime (LLT) and trust model. Thereafter, energy prediction is conducted utilizing the Deep Neuro-Fuzzy Network (DNFN). Then, dynamic cluster (DC) computation is carried out utilizing the Adaptive Genetic Fuzzy System (AGFS) by considering objectives like distance, throughput, trust factors, LLT, delay, residual and predicted energy. Here, AGFS is trained using Fractional Flamingo Jellyfish Search Optimization (FFJSO), which combines Fractional Calculus (FC) with Flamingo Jellyfish Search Optimization (FJSO). FJSO integrates Flamingo Search Algorithm (FSA) and Jellyfish Search Optimization (JSO). Next, the routing process is executed utilizing FFJSO with the above-mentioned objectives as fitness parameters. At Base Station (BS), feature selection is performed utilizing Angular Separation Distance (ASD) by getting input log data. Then, intrusion attack detection is carried out utilizing a Convolutional Deep Q Learning Network (CDQ-LN), which is designed by integrating a Deep Q-learning Network (DQN) and Convolutional Neural Network (CNN), where lay

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