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Consumer Communications and Networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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Design and Development of a Mobile-Based Data Acquisition System for Aerial Bridge Inspection

Design and Development of a Mobile-Based Data Acquisition Sy...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ryan P. Himongala Earl Ryan M. Aleluya Steve E. Clar Jeanette C. Pao Francis Jann A. Alagon Carl John O. Salaan Rabby Q. Lavilles Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Department of Information Technology College of Computer Studies Mindanao State University - Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Infrastructures such as bridges are ubiquitous in today's world, but over time, corrosion, continuous vibrations, heavy loads, natural disasters, and aging have actively weakened their structural integrity. Hence, it is crucial to periodically assess the bridge's condition to maintain its structural soundness. Nevertheless, conventional bridge inspection techniques involve inspectors to visually assess a bridge's condition and manually capture images of its defects. UAVs for bridge inspections typically improve the conventional approach. This method provides a visual system by mounting a camera on the UAV equipped with a software application dedicated to data acquisition. However, the application operates primarily on desktop computers or laptops while incompatible with smartphones and tablets, limiting its portability and making it inconvenient for inspectors. In this study, the researchers developed an application for mobile devices to further improve data acquisition. The application is designed to display a real-time video feed from the shelled UAV, capture visual data of bridge defects, and allow users to review the data after the inspection. The researchers tested and evaluated the application's performance regarding its functionality. Results show that the application has successfully improved the portability and efficiency of the inspection process, achieving its intended features and functionalities while adhering to the requirements.

关键词： Bridges Visualization Reviews Data acquisition Inspection Streaming media Visual systems

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Self-assembled microtubular electrodes for on-chip low-voltage electrophoretic manipulation of charged particles and macromolecules

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Microsystems & Nanoengineering 2022年第1期8卷 341-352页

作者： Apratim Khandelwal Nagendra Athreya Michael Q.Tu Lukas L.Janavicius Zhendong Yang Olgica Milenkovic Jean-Pierre Leburton Charles M.Schroeder Xiuling Li Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Chemical Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Electrical and Computer Engineering Microelectronics Research CenterUniversity of TexasAustinTX 78758USA Coordinated Science Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA

On-chip manipulation of charged particles using electrophoresis or electroosmosis is widely used for many applications,including optofluidic sensing,bioanalysis and macromolecular data *** hereby demonstrate a technique for the capture,localization,and release of charged particles and DNA molecules in an aqueous solution using tubular structures enabled by a strain-induced self-rolled-up nanomembrane(S-RuM)***-in 3D electrodes that are embedded in cylindrical S-RuM structures and biased by a constant DC voltage are used to provide a uniform electrical field inside the microtubular *** charged-particle manipulation is achieved at a bias voltage of<2-4 V,which is~3 orders of magnitude lower than the required potential in traditional DC electrophoretic ***,Poisson-Boltzmann multiphysics simulation validates the feasibility and advantage of our microtubular charge manipulation devices over planar and other 3D variations of microfluidic *** work lays the foundation for on-chip DNA manipulation for data storage applications.

关键词： electrodes cylindrical planar

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection 7

Securing IoT-Edge Networks: Federated Deep Learning for Botn...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nagasundaram, S. Sindhuja, R. Rajesh Kanna, B. Rajalakshmi, S. Shobana, G. Srivastava, Aviral Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai Pallavaram India Saveetha Engineering College Department of Medical Electronics India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India RMK College of Engineering and Technology Department of Electronics and Communication Engineering India College of Science and Humanities Department of Computer Applications SRM Institute of Science and Technology Kattankulathur India Amity University Department of CSE Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Governments throughout the world are encouraging the use of 'smart city' technologies to improve urban residents' day-to-day experiences. In order to improve services like healthcare, electricity distribution, water purification, traffic management, etc., smart cities implement internet-connected technologies. The proliferation of connected gadgets has led to an increase in botnet assaults based on the IoT. The term IoT is used to describe a system of computers that are linked together that may be used for a wide variety of tasks, from environmental monitoring to on-demand power switching and beyond. Many Internets of Things gadgets are inherently disparate, update at irregular intervals, and hide in plain sight on a private or company network. The safety and confidentiality issues surrounding the Internet of Things that need to be addressed in both academic and practical settings. This research study proposes a federated-based solution to botnet attack detection utilizing on-device decentralized traffic data and a deep learning (DL) model. The proposed federated method addresses privacy concerns by preventing data from leaving the network's edge on the device. Instead, the edge layer is used to do the DL calculation, which has the extra benefit of being closer to the source of the data. Many tests are run on newly made public test data sets for deep learning models. Additionally, the sets of data are presented for examination and understanding. The recommended DL model achieved better results than the ML models. Finally, this research shows that the suggested model can detect anomalies with a precision of up to 98%. © 2023 IEEE.

关键词： Internet of things

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Sustainable Energy Evolution Using IoT-Enhanced Carbon Capture and Storage for Oil Recovery 6

Sustainable Energy Evolution Using IoT-Enhanced Carbon Captu...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Valli, K. Vijaya Naga Swathi, G. Lumina Julie, R. Saravanan, S. Sujatha, S. Malarvizhi, E. Srkr Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissions and optimize hydrocarbon recovery. This paper examines IoT-enabled CCS for EOR and carbon-neutral oil and gas operations. Real-time data capture and analysis using IoT technologies optimize CCS operations. Installing IoT sensors and devices across the EOR and CCS infrastructure lets operators monitor CO2 injection rates, reservoir pressure, temperature, and fluid composition. The CCS process is precisely controlled using data, improving carbon capture efficiency and lowering operating costs. Remote monitoring and management of carbon storage sites using IoT in CCS for EOR improves environmental sustainability. The collected CO2 is securely and efficiently contained, reducing leakage and environmental effects. Preventing CCS infrastructure problems with IoT-driven predictive maintenance ensures long-term operational dependability. Securely storing collected CO2 offsets oil and gas emissions and contributes to carbon neutrality. EOR process optimization with IoT technologies may boost hydrocarbon recovery, making the switch to greener energy sources easier. IoT-enabled CCS for EOR has the potential to produce carbon-neutral oil and gas operations. The system improves CCS efficiency and sustainability using real-time data, remote monitoring, and predictive maintenance. Integration of IoT with CCS reduces carbon emissions and promotes the industry's worldwide sustainability objectives. © 2024 IEEE.

关键词： Carbon capture and utilization

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Advancements in sensors and actuators technologies for smart cities: a comprehensive review

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Smart Construction and Sustainable Cities 2023年第1期1卷 1-19页

作者： Arshi, Oroos Mondal, Surajit Department of Cyber Security and Forensics School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electrical and Electronics Engineering School of Advanced Engineering University of Petroleum and Energy Studies Dehradun India

The creation of smart cities has benefited greatly from the quick advancement of sensor and actuator technology. The basis of data-driven urban environments is supported by these technologies, which seamlessly connect with the Internet of Things (IoT). This in-depth review paper explores the crucial part that sensors and actuators play in the development of smart cities, covering important topics such as technological kinds, data security, regulatory frameworks, and future possibilities. The review begins by explaining the importance of sensors and actuators in the Internet of Things (IoT) connections that serve as the framework for smart cities. Additionally, it sheds light on the wide range of sensors designed for different IoT applications as well as the variables affecting their service life, highlighting how crucial precision and durability are. Actuators are examined in detail to clarify how it might be used to create smarter cities. Actuators are the dynamic counterparts of sensors. This review discusses data security in big data exchange among actuators, legal foundations for smart city development, and key elements for creating a smart city. It highlights the benefits of advanced actuator technology and sensor integration, and emerging trends like AI-driven urban management and blockchain-enhanced data security. The paper serves as a guide for researchers, policymakers, and urban planners. The graphical abstract below illustrates the multifaceted advancements in sensor and actuator technologies, showcasing their pivotal role in shaping smarter, more sustainable cities.

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Deep Learning-Based Assessment of Teacher Effectiveness in classrooms: Integrating ARIMA and LSTM Models 2

Deep Learning-Based Assessment of Teacher Effectiveness in c...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Shukla, Tushar Dhar Bharadwaja, A.V. Bhattacharya, Chaitali Chary Ch, Narasimha Bhushan, P. Vinay Thulasimani, T. University Institute Physical Education & Sports Chandigarh University Punjab Mohali India Autonomous College Department of ECE Visakhapatnam India Tecnia Institute of Advanced Studies CDL New Delhi India School of Engineering & Technology Dept of CSE Telangana Hyderabad India Malla Reddy University School of Engineering Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Mathematics Erode India

ISBN: (纸本)9798331534950

Teachers are no different from everyone else when it comes to the value of helpful criticism. Because the system teach so often, teachers have plenty of chances to perfect these skills. However, improving one's abilities via unstructured exercise alone isn't enough;targeted practice, usually with the help of a coach, may be necessary for mastery. The ideal coach would provide instructors with realistic yet demanding training goals, provide them with helpful feedback, and help them find ways to advance their careers. The proposed approach incorporates preprocessing, feature selection, and training of the model. Data cleaning, conversion, and formatting are all part of data preparation, which is preparing a dataset for analysis. Data gain and principal component analysis make up feature selection. The ARIMA-LSTM model was consistently used throughout the training. With a 90.86% average accuracy, this innovative method surpasses ARIMA and LSTM. © 2024 IEEE.

关键词： Contrastive Learning

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400713941

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025 Copyright held by the owner/author(s).

关键词： creativity Hackathons human-centered AI large language models quantitative methods

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Optimal Design of Distributed Network Based on Power Quality to Minimize the Flicker of Wind Turbines

Optimal Design of Distributed Network Based on Power Quality...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Abolfazl Ghaffari Alireza Askarzadeh Roohollah Fadaeinedjad Gerry Moschopoulos Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran Department of Electrical and Computer Engineering Western University London Ontario Canada

The use of wind energy as an energy resource in distribution networks is increasing. A disadvantage to using wind energy, however, is the emission of flicker in an electrical distribution network (EDN), which may adversely affect power quality during system operation. An effective way to improve power quality is to include the allocation of wind turbines (WTs) in the planning phase. The location and size of WTs can significantly improve the network power quality and electrical performance in an EDN. This paper considers modeling for flicker emission by using a mixed integer non-linear programming (MINLP) model to determine the optimal location and size of WTs. The optimal design of the network associated with WTs generation profile patterns and the variation of load demand during the planning horizon 24-h is also determined. The proposed model is implemented in GAMS and tested on a standard IEEE 33-bus system.

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