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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Swetha, K. Ishaq Shareef, C. Sreenivasulu, G. Baseer, K.K. Jahir Pasha, M. Ashoka Woman's Engineering College Dept. of Cse Kurnool India G. Pullaiah College of Engineering and Technology Dept. of Cse Kurnool India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering. College Dept. of Information Technology Tirupati India

ISBN: (纸本)9798350300093

The recent technological innovation, Blockchain system is used to store data in a way that makes it difficult or impossible to edit, hack, or steal the data. It is an important part of technology with a decentralized architecture, distributed database, and continuously expanding records. There are many other technologies that are used along in blockchain. After doing critical study on many documents and understanding the technologies used by various studies, its necessities, and the issues encountered by it while building their respective models. The purpose of this study is to examine various tools and technologies that can be used in blockchain. Around 200 research works have been collected and up to 40 research studies are filtered. Research studies are filtered based on various factors. Some research studies were excluded based on title, few were excluded based on abstract and titles. This study is categorized in terms of Blockchain using Ethereum, Electronic Health records, Smart Contracts and Health monitoring. © 2023 IEEE.

关键词： Smart contract

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Constructing Disease Comorbidity Networks Using Statistical Methods from Electronic Health Records

Constructing Disease Comorbidity Networks Using Statistical ...

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2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Bramesh, S.M. Anil Kumar, K.M. P. E. S. College of Engineering Dept. of Information Science & Engineering Mandya India Jss Science and Technology University Dept. of Computer Science & Engineering Mysuru India

ISBN: (纸本)9798350300826

Disease comorbidity is a prevalent and significant concern in modern medical practice. Patients in such circumstances will need specialized medical interventions. This study proposes a network-based computation model to extract the disease comorbidity patterns by utilizing the electronic health records of a 'Diabetes 130-US hospitals for years 1999-2008' dataset. The estimate of disease-disease relationships using statistical methods is a crucial step in this paradigm. A Disease Comorbidity Network (DCN) was constructed based on these relationships. This model can then be used to extract the corresponding illness comorbidity patterns for various races and genders, using the endocrine disease (diabetes) as an example to determine the associations between various diseases and their comorbidities. The outcomes of this research demonstrated that the strength of some interesting and or clinically relevant disease associations (Example: musculoskeletal disorder and endocrine disorder, nervous disorder and endocrine disorder) is stronger in male patients than in female patients. Also, observed that the endocrine-specific comorbidity patterns will vary based on the race and gender of a patient. Finally, extracted comorbidities patterns are validated with medical literature as well as with the state of art method. Notably, this study exploits the network-level relationships between diseases, which will improve our perception about disease associations at the level of the patient population. © 2023 IEEE.

关键词： Statistical methods

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Anomaly Data Enhancement and Detection for Charging Piles Considering Generative Adversarial Network 12

Anomaly Data Enhancement and Detection for Charging Piles Co...

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12th International Conference on Renewable Power Generation, RPG 2023

作者： Feng, Qi Zhou, Yun Li, Hengjie Feng, Donghan Dept of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Dept. of Electrical Engineering Shanghai Jiao Tong University Shanghai China

The accuracy of anomaly detection results is affected by factors such as low proportion of abnormal data samples and imbalanced distribution. To address the issue that the current abnormal data detection model for charging piles depends on the quality of abnormal data samples in the training set, this paper proposes a charging pile abnormal data augmentation method based on Generative Adversarial Network (GAN) and the corresponding abnormal data detection model. The proposed method first improves the traditional GAN by introducing the unsaturated activation function Leaky ReLU (LR) and proposes charging pile abnormal data augmentation method based on LR-GAN to enhance the abnormal data in the training set. Then, the LR-GAN augmented training set is used as input for the Random Forests (RF) model to establish a charging pile abnormal data detection model based on LR-GAN-RF to detect abnormal data in the charging pile charging dataset. Finally, different charging pile anomaly data sets are used to validate the feasibility and effectiveness of this method, which is verified to be highly feasible. © The Institution of engineering & technology 2023.

关键词： Piles

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Efficient Passenger Counting in Public Transport Based on Machine Learning 38

Efficient Passenger Counting in Public Transport Based on Ma...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Wiboonsiriruk, Chonlakorn Phaisangittisagul, Ekachai Srisurangkul, Chadchai Kumazawa, Itsuo Kasetsart University Faculty of Engineering Dept. of Electrical Engineering Bangkok Thailand National Science and Technology Development Agency National Metal and Materials Technology Center Pathum Thani Thailand School of Engineering Tokyo Institute of Technology Dept. of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9798350302196

Public transportation is a crucial aspect of pas-senger transportation, with buses playing a vital role in the transportation service. Passenger counting is an essential tool for organizing and managing transportation services. However, manual counting is a tedious and time-consuming task, which is why computer vision algorithms are being utilized to make the process more efficient. In this study, different object detection algorithms combining with object tracking are investigated to compare passenger counting performance. The system employs the EfficientDet algorithm, which has demonstrated balanced performance in terms of speed and accuracy. Our results show that the EfficientDet can accurately count passengers in varying conditions with an accuracy of 94 %. © 2023 IEEE.

关键词： Object detection

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An earlier serial lactate determination analysis of cardiac arrest patients using a medical machine learning model

An earlier serial lactate determination analysis of cardiac ...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. Jiwani, Nasmin University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Lewis University Dept.of Computer Information System IL United States Sri Eshwar College of Engineering Dept. of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350335835

In general, cardiac arrest results in an inability to pump enough blood for the body's needs. Through this, the organs and tissues get enough oxygen and nutrients for their metabolic needs, and excess fluid accumulates. This further increases the severity of the patient's problems. Cardiac arrest is the leading cause of hospitalization in people over 65 and is therefore considered a significant public health problem. It is a pathology linked to an increase in average life expectancy, and its incidence is increasing every year due to the general ageing of the population. In this paper, an earlier serial lactate determination analysis of cardiac arrest patients using a medical machine learning algorithm. It can occur following an episode of cardiac arrest or inadequate treatment of chronic diseases, including diabetes and hypertension. Adopting lifestyle habits that prevent the onset of these conditions is an essential strategy for preventing heart failure. The proposed approach has achieved 91.58% accuracy, 87.96% precision, 84.62% recall, and 85.23% F1-score results compared to other methods. © 2023 IEEE.

关键词： Tissue

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AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios 8

AIOps-Driven Enhancement of Log Anomaly Detection in Unsuper...

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8th International Conference on Big Data, Knowledge and control Systems engineering, BdKCSE 2023

作者： Dave, Daksh Sawhney, Gauransh Khut, Dhruv Nawale, Sahil Aggrawal, Pushkar Bhavathankar, Prasenjit Bits Pilani Dept. of Electrical Electronics Pilani India Sardar Patel Institute of Technology Information Technology Mumbai India Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350313246

Artificial intelligence operations (AIOps) play a pivotal role in identifying, mitigating, and analyzing anomalous system behaviors and alerts. However, the research landscape in this field remains limited, leaving significant gaps unexplored. This study introduces a novel hybrid framework through an innovative algorithm that incorporates an unsupervised strategy. This strategy integrates Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) and uses a custom loss function to substantially enhance the effectiveness of log anomaly detection. The proposed approach encompasses the utilization of both simulated and real-world datasets, including logs from SockShop and Hadoop Distributed File System (HDFS). The experimental results are highly promising, demonstrating significant reductions in pseudo-positives. Moreover, this strategy offers notable advantages, such as the ability to process logs in their raw, unprocessed form, and the potential for further enhancements. The successful implementation of this approach showcases a remarkable reduction in anomalous logs, thus un-equivocally establishing the efficacy of the proposed methodology. Ultimately, this study makes a substantial contribution to the advancement of log anomaly detection within AIOps platforms, addressing the critical need for effective and efficient log analysis in modern and complex systems. © 2023 IEEE.

关键词： Anomaly detection

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Bird Repeller Using Opencv

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Procedia Computer Science 2025年 252卷 975-984页

作者： M. Upendra T. Anuradha L. Prakash Dept. of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person’s presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used; YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds.

关键词： Computer Vision Cascade Classifier Open CV Distress Call image detection Raspberry pi pi camera YOLO Bird Repellent

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A Comprehensive Study of Solar Energy Harvesting System in Wireless Sensor Networks 9

A Comprehensive Study of Solar Energy Harvesting System in W...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer engineering, UPCON 2022

作者： Sah, Dipak Kumar Mazumdar, Nabajyoti Pal, Pankaj Amgoth, Tarachand Dept. of Computer Science & Engineering Dhanbad826004 India Indian Institute of Information Technology Dept. of Information Technology Allahabad211015 India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Wireless Sensor Networks (WSNs) incorporate sensor nodes with minimal power consumption. Sensor devices are in high demand in many areas, including smart cities, environmental monitoring, the Internet of Things (IoT), health monitoring, and the like. As nodes are frequently located in remote places, and an ordinary node battery life is too short, energy depletion is a significant problem for the sensor network. But it's not practical to change or regularly maintain the sensor node's battery. This could cause the network to disconnect. Consequently, a recharging sensor node battery has been identified using energy harvesting (EH). It has several environmental forms, including solar, wind, mechanical, etc. The solar system provides unlimited energy resources to nodes. This paper examines a comprehensive case study of solar harvesting systems and their most recent applications. In solar harvesting nodes, the following primary components are utilised: solar panels, energy storage classes, a DC-DC converter, maximum power point tracking (MPPT), an energy predictor, and a sensing module. Furthermore, we have discussed some recent applications and future work of sensor networks, for example, green street lights, agriculture 4.0, outdoor environment-based monitoring, IIoT, hybrid storage class and new communication technologies. © 2022 IEEE.

关键词： Sensor nodes

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TyCo: A Novel Approach to Collaborative Filtering Recommendation Based on User Typicality 3

TyCo: A Novel Approach to Collaborative Filtering Recommenda...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Kiran, Ajmeera Rao, Sudesh Waghmare, Pravin B. Somasekar, J. Dhabliya, Dharmesh Gupta, Ankur Mlr Institute of Technology Dept. of Cse Hyderabad Telangana Dundigal India Nmamit Dept. of Aiml Karnataka Karkala India Wardha India Faculty of Engineering and Technology Dept. of Cse Karnataka Bengaluru India Vishwakarma Institute of Information Technology Dept. of It Maharashtra Pune India Vaish College of Engineering Dept. of Cse Haryana Rohtak India

ISBN: (纸本)9798350330724

Collaborative filtering (CF), which uses user-based and item-based approaches, is a popular methodology for recommender systems. Item-based CF focuses on making suggestions based on strong correlations, whereas user-based CF focuses on discovering comparable user preferences and recommending things to consumers. Currently, the majority of CF approaches employ corated items to compare users. This study suggests a typicality-based technique called TyCo that locates users' 'neighbors' by using user typicality in user groups. A user is represented by TyCo by a user typicality vector, which reflects their preference for various goods. It chooses 'neighbors' by comparing individuals based on how typical they are, which can get around the drawbacks of conventional collaborative filtering techniques. This is the first piece of work to use typicality to collaborative filtering, and it runs experiments to assess and show off its benefits. The cost of preprocessing steps, which include creating user prototypes and assessing user typicality in user groups, varies based on the clustering approach selected. Simulation result concludes that in case of Movie lens, MAE is 0.732, Precision is 0.820, and recall value is 0.738 when method is TyCo. On other hand MAE is 0.854, Precision is 0.702, recall value is 0.623 when method is UPCC and MAE is 0.863, Precision is 0.716, recall value is 0.612 when method is case of IPCC. Simulation result concludes that in case of Book-Crossing, MAE is 0.828, Precision is 0.782, and recall value is 0.712 when method is TyCo. On other hand MAE is 0.943, Precision is 0.671, recall value is 0.589 when method is UPCC and MAE is 0.958, Precision is 0.683, recall value is 0.586 when method is case of IPCC. © 2023 IEEE.

关键词： Recommender systems

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Quadrature control-Bounded ADCs 66

Quadrature Control-Bounded ADCs

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Malmberg, Hampus Feyling, Fredrik De La Rosa, Jose M Eth Zürich Dept. of Information Technology & Electrical Engineering Zürich Switzerland Norwegian University of Science and Technology Dept. of Electronic Systems Trondheim Norway Seville Spain

ISBN: (纸本)9798350302103

In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated by considering band-pass analog-to-digital conversion. We show how a low-pass control-bounded analog-to-digital converter can be translated into a band-pass version where the guaranteed stability, converter bandwidth, and signal-to-noise ratio are preserved while the center frequency for conversion can be positioned freely. The proposed converter is validated with behavioral simulations on several filter orders, center frequencies, and oversampling ratios. Finally, robustness against component variations is demonstrated by Monte Carlo simulations. © 2023 IEEE.

关键词： Analog-to-digital converters control-bounded quadrature and band-pass sigma-delta modulation

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