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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shanmugasundaram, C. Umamaheswari, C. Vijayalakshmi, A. Varghese, Prabha Elizabeth Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Puducherry India St. Thomas College of Arts and Science Department of Computer Science with Data Science Tamil Nadu India Sri Manakula Vinayagar Engineering College Department of Biomedical Engineering Puducherry India

ISBN: (纸本)9798331510022

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry. © 2024 IEEE.

关键词： Agricultural economics

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Sequencing Efforts and Epidemiological Trends: Analyzing SARS-CoV-2 Dynamics Across European Nations

Sequencing Efforts and Epidemiological Trends: Analyzing SAR...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Rancati, Simone Pala, Daniele Marini, Simone Salemi, Marco Bellazzi, Riccardo Nicora, Giovanna University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy University of Florida Department of Epidemiology Gainsville United States University of Florida Department of Pathology Gainsville United States

ISBN: (纸本)9798350386226

The COVID-19 pandemic has profoundly impacted global health, leading to millions of deaths and overwhelming healthcare systems worldwide. This study investigates the relationship between SARS-CoV-2 sequencing rates and critical epidemiological parameters, such as cases, deaths, and ICU admissions, across 25 European countries from January 2020 to November 2023. By analyzing these relationships, we aim to determine whether sequencing efforts were reactive - in response to epidemiological pressures - or proactive, guided by public health strategies. The analysis used publicly available data from GISAID, OxCGRT, and ECDC, and included weekly aggregation, correlation analysis, and the application of TimeGPT for predictive modeling. Results show that sequencing rates were significantly correlated with ICU admissions, hospitalizations, case numbers, and deaths, though with variability between countries and over different pandemic phases. TimeGPT analysis revealed that sequencing rates were often the most informative feature for predicting future COVID-19 cases in many countries. These findings highlight the potential of sequencing rates to serve as early indicators for severe pandemic outcomes and underscore the importance of context-specific approaches for managing future health crises. © 2024 IEEE.

关键词： COVID-19

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A Vision-Based Recognition and Navigation Method for Autonomous Underwater Robot Grasping

A Vision-Based Recognition and Navigation Method for Autonom...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Luo, Yudong Xie, Feiyu Wang, Tong Zhao, Na Shen, Yantao Dalian Maritime University Department of Computer Science and Technology Liaoning Dalian116026 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

The development of underwater robotic systems with autonomous grasping capabilities is challenging due to the complexity of the operation environment, limited sensing performance, and computation load. This paper proposes a fast recognition and navigation method for underwater robot grasping. In detail, we theoretically demonstrate the feasibility of underwater robots that can quickly identify and localize targets at a long distance through a monocular camera, thus navigating the robot to approach the target in real-time and then generate accurate grasping points at short-range through stereo-vision cameras to help the robot complete autonomous grasping. Experiment results show that our constructed system can recognize and determine the target's position within 0.03 seconds, then detect and generate an accurate grasping point within 1 second at a distance of 50 cm from the target with the error of 23 cm. Our research proves that underwater robots equipped with low-cost devices such as commercial stereo-vision cameras and edge computing devices can identify targets and generate accurate grasping points in real-time, promoting research in the field of autonomous underwater grasping. © 2024 IEEE.

关键词： Stereo vision

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Underwater Autonomous Grasping Strategy with Constrained Visual Information

Underwater Autonomous Grasping Strategy with Constrained Vis...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wang, Tong Luo, Yudong Xie, Feiyu Zhao, Na Shen, Yantao Dalian Maritime University Department of Computer Science and Technology Liaoning Dalian116026 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

When the robot arm grasps across media, continuously grasping and placing the objects inevitably causes water flow fluctuations, thus interfering with subsequent visual quality. This results in the inability to correctly feedback object information, ultimately leading to grasping failure. To tackle this problem, we propose a generalized grasping model based on constrained visual information (VC-Grasp). Specifically, we leverage RGB-D information combined with the physical logic of action primitives to achieve effective movements. During the operation, we thoroughly consider the relative position changes between objects, thereby attaining true zero-collision. Eventually, we predict the optimal action sequences for the upcoming period to complete the task. Additionally, we propose a novel sim2real framework, which significantly enhances the generalization capability of our model. Extensive experiments in physical environments demonstrate that our system outperforms other methods in terms of grasping success rate and time efficiency under constrained visual information conditions. © 2024 IEEE.

关键词： Robotic arms

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Assessing Distributed Energy Resource Hosting Capacity Under Different Grid-Tie Inverter Control Strategies 8

Assessing Distributed Energy Resource Hosting Capacity Under...

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8th Workshop on Communication Networks and Power Systems, WCNPS 2023

作者： Santos, Brenda L. M. Barros, Luciano S. Barbosa, Daniel Moreira, Fernando A. Federal University of Bahia Graduate Program in Electrical Engineering Salvador Brazil Federal University of Paraíba Computer Systems Department João Pessoa Brazil Federal University of Bahia Department of Electrical and Computer Engineering Salvador Brazil

ISBN: (纸本)9798350343571

Despite the potential benefits that the integration of distributed energy resources (DERs) can bring to the system, it may cause problems related to power quality constraints, such $as$ reverse power flow in substation and overvoltages. An effective approach to address these problems involves the adoption of reactive and active power control in grid-tie inverters associated with DER. Therefore, this paper assesses the impacts of grid-tie inverter control modes, including both Volt-Var and Volt-Watt strategies, on the DER hosting capacity. In order to improve the overall system operation, modifications in the Volt-Var and Volt-Watt curves were proposed. It is noteworthy that these control strategies can have adverse effects on certain distribution system performance indicators, such $as$ voltage deviation and power losses;for this reason, these indicators are also evaluated in this study. A stochastic approach was adopted to deal with the uncertainties associated with DERs and loads. Finally, from tests conducted in the IEEE 33-bus test system, it was concluded that the proper adjustment of the Volt-Var and Volt-Watt control curves significantly influences DER hosting capacity, $as$ well $as$ voltage deviation and power losses. © 2023 IEEE.

关键词： Reactive power

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Supervisor Control of Power System for Stability Problems and Improvements Using computer Control Technology

Supervisor Control of Power System for Stability Problems an...

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2023 International Conference on Emerging Power Technologies, ICEPT 2023

作者： Yaqoob, M. Sardar, M. Usman Shahid, M. Usama Shahid, Tanzeela Mutloob, Tayyaba Ahmad Shah, S. Imran Huzaif, Muhammad Khwaja Fareed University of Engg.& It Dept. of Electrical & Biomedical Engineering Rahim Yar Khan Pakistan Lahore College for Women University Department of Electrical Engineering Lahore Pakistan Uspcase Nust Department of Electrical Power Engineering Islamabad Pakistan Ned University of Engineering & Technology Department of Computer & Information System Engineering Karachi Pakistan

ISBN: (纸本)9798350301014

Currently, there is a growing interest in the topic of power quality across the entire electric power system. The power system is a complex network of modules with a broad distribution area, designed to meet the demand for electric power. The power system is susceptible to a range of disturbances arising from either dynamic consumer load behavior or faults. The power system necessitates a precise and advanced stability and protection mechanism. Ensuring the safe and reliable operation of the power system is a significant challenge, with voltage stability and fault protection being key areas of concern. The present research study proposes a technique to augment the stability of a grid-connected system that comprises of several generating sources through the utilization of FACTS devices, specifically static synchronous compensator (STATCOM). The technology has been designed to enable the monitoring of supervisory control of devices, while the distribution system offers decision-making tools to enhance system performance. A fault was generated between the time interval of 1-1.04 sec duration, and the simulation waveform was subsequently observed. The present study entails a simulation-based comparison between a power system without a STATCOM devise and one equipped with the STATCOM. The analysis results of the transient voltage stability of HV AC transmission lines utilizing STATCOM. © 2023 IEEE.

关键词： Static synchronous compensators

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Optimal Redundant Transmission-Based Defense Against Reverse Attacks with a Game Theory Perspective 3

Optimal Redundant Transmission-Based Defense Against Reverse...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Guo, Ziyi Zhou, Jing Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada City University of Hong Kong Department of Biomedical Engineering Hong Kong Hong Kong

ISBN: (纸本)9798331540319

This paper studies a redundant transmission-based defense strategy against reverse attacks on remote state estimation. Firstly, a game-theoretic framework is developed, allowing both the adversary and defender to select multiple channels for attacking and transmitting data. Secondly, within the proposed framework, a zero-sum game, a general-sum game, and a Bayesian game are formulated depending on the defender's knowledge of the attacker's cost. Finally, the Nash and Bayesian Nash equilibria of the three games are analyzed and computed to identify the optimal strategies for both the attacker and defender. Numerical simulations are conducted to demonstrate the proposed approach. © 2024 IEEE.

关键词： Nash equilibrium

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

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Big Data Bot with a Special Reference to Bioinformatics

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computers, Materials & Continua 2023年第5期75卷 4155-4173页

作者： Ahmad M.Al-Omari Shefa M.Tawalbeh Yazan H.Akkam Mohammad Al-Tawalbeh Shima’a Younis Abdullah A.Mustafa Jonathan Arnold Biomedical Systems and Informatics Engineering Department Yarmouk UniversityIrbid21163Jordan Department of Medicinal Chemistry and Pharmacognosy Yarmouk UniversityIrbid21163Jordan Department of Electrical Computer and Software EngineeringUniversity of Ontario Institute of TechnologyOshawaL1H7K4Canada Department of Mechanical Engineering University of MosulMosul41001Iraq Genetics Department University of GeorgiaAthens30602GAUSA

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential *** scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough *** data should go through several processes,including mining,data processing,analysis,and *** study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human *** software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line *** software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel ***,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data *** a result,data collection from the web will become faster and less expensive,with no need for manual data *** software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

关键词： Bioinformatics big data data extraction bot drug design

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