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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Patil, Seema Babar, Vinod Mahadev Madhusudhana Rao, C. Karambelkar, V.H. Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant Institute of Management Karad India Jaywant Co Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Department of Ophthalmology Karad India

ISBN: (纸本)9798350360707

Electronic health records (EHRs) and genomic data make up the majority of the personal medical and health data that medical organizations keep, distribute, and analyze thanks to the quick development and implementation of various health information technologies. Meanwhile, new technologies like wearables and smartphones allowed third-party companies to gather massive amounts of consumer health data and offer a variety of supplementary mHealth services. Due to its enormous potential value for secondary use and significant privacy disclosure concerns, health big data has already emerged as the most significant big data category. Lack of trust directly leads to security, which is achieved through the use of methods similar to those we have already outlined. Since the techniques employed are tailored to the industry they belong to and the needs of the moment, we have come to the conclusion that there is no such thing as a perfect security system. But since hackers use the same technologies, it takes them progressively less time to figure out the keys. Since security will never be flawless, all we can do is create strategies to attempt and increase difficulty. © 2024 IEEE.

关键词： Electronic health record

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Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for Protecting Healthcare SDN Environments Against DDoS Attacks

Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Kaur, Amandeep Krishna, C. Rama Patil, Nilesh Vishwasrao Department of Computer Science and Engineering National Institute of Technical Teachers Training and Research Chandigarh India Department of Computer Engineering Government Polytechnic Chhatrapati Sambhaji Nagar Aurangabad India

ISBN: (纸本)9789819780952

The healthcare sector, dealing with vast and sensitive data, faces an ever-expanding need for efficient data management and security. Automated healthcare devices generate crucial information, and Software-Defined Networking (SDN) has become integral to healthcare systems due to resource efficiency, robust monitoring, and centralized control. However, in this data-centric environment, SDNs are vulnerable to various cyber threats, including Distributed Denial of Service (DDoS) attacks and probing attempts. These attacks can harm network performance, potentially endangering lives, especially with the rise of portable healthcare integrating mobile services and programmable devices. This article proposes a Kafka Streams-based distributed approach to protect SDN-based healthcare systems from DDoS attacks, named Med-DDoS-SDN. The Med-DDoS-SDN comprises two modules: (i) Real-time Attack Traffic Classification Module (RACM) and (ii) Preprocessed Data Storage Module (PDSM). The RACM categorizes incoming network traffic into four real-time classes. PDSM collects 21 essential features with outcomes into the Hadoop Distributed File System (HDFS) to maintain the classification model with the range samples. Med-DDoS-SDN is formulated and verified using the CICDDoS2019 dataset. The average classification accuracy of the distributed Med-DDoS-SDN solution stands at 92.75%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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Points of interest in the city of Barcelos in Portugal through augmented reality

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 40-48页

作者： Pereira, Miguel Silva, João Carlos Pinheiro, Marisa Carvalho, Sandro Santos, Gilberto Polytechnic Institute of Cávado and Ave Barcelos Portugal 2Ai – Applied Artificial Intelligence Laboratory Barcelos Portugal INESC TEC - Institute for Systems and Computer Engineering Porto Portugal LIACC - Laboratory of Artificial Intelligence and Computer Science Porto Portugal

Barcelos is a historic city in Portugal with many tourist attractions, attracting more and more visitors who come to the city with the aim of exploring it. The main objective of this article is to boost tourism in the city of Barcelos, specifically highlighting tourist, historical and leisure spots, based on the development of a mobile application using augmented reality technologies and geolocation. This application intends to allow the users to know historical points of interest in Barcelos, as well as interact with a certain point. The results of this investigation were evaluated by testing the application by end users, with the aim of identifying whether the application meets their needs, in particular the promotion of tourist and historical points. © 2023

关键词： Augmented reality

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Modification of Medicines in the Healthcare Provision with Evolving Technologies 1

Modification of Medicines in the Healthcare Provision with E...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Patil, Ashish Parmar, Indrajeet Shendge, Sandhya Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Karad India Jaywant Institute of Management Karad India

ISBN: (纸本)9798350360707

The combination of artificial intelligence (AI) with precision medicine has the potential to transform health care. In recent years, the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly acquired traction as a new study specialization in a variety of collegiate and industrial areas, most notably in the healthcare industry. Recent advances in healthcare delivery have provided many patients with enhanced tailored healthcare, which has improved their quality of life. The next stage in healthcare will be to seamlessly integrate these new technologies, such as IoT-assisted wearable sensor devices, AI, and Blockchain. Surprisingly, IoT and AI-enabled technology are rapidly transitioning healthcare from a traditional hub-based system to a more individualized healthcare management system (HMS). They also look at the existing limits of customized medicine. Furthermore, researchers argue that because components of customized medicine are founded in biological facts, personalized medical practices in specific contexts are likely to be unavoidable, particularly if relevant tests and deployment tactics grow more efficient and cost-effective. © 2024 IEEE.

关键词： Personalized medicine

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Robust and Automatic Shock Detection and Processing in High-speed Flow Simulations

Robust and Automatic Shock Detection and Processing in High-...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Woodruff, Aiden Sahni, Onkar Rensselaer Polytechnic Institute Department of Computer Science TroyNY12180 United States Rensselaer Polytechnic Institute Department of Mechanical Aerospace and Nuclear Engineering TroyNY12180 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this paper, we present an automated workflow for shock detection and processing in high-speed flow simulations. Here we present an efficient approach to detect and process shocks including those computed on relatively coarse unstructured meshes. We develop and apply a robust set of steps to target problems with shock systems containing interactions between different shock components/segments. In particular, these steps include de-fragmentation to f ill-up holes or missing shock elements/cells, sorting/labeling to determine contiguous set of shock elements/cells, removal of noise or small sets, and identification of each distinct shock segment/component. The resulting information could be used for shock fitting, e.g., for creating highly anisotropic orthogonal/layered elements aligned with shocks, and/or quantitative analysis of complex shocks. A combination of manufactured data and numerical simulation data is used to demonstrate the performance of the current procedure. Overall, the novel procedure developed in this work is found to be efficient and robust. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Numerical Simulation Crew Exploration Vehicle Hypersonic International Flight Research Experimentation Viscosity Mach Number Projectiles Shock Capturing Method Aerodynamic Heating Supersonic Flight Reentry Vehicles

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Particle Swarm Optimization versus Simulated Annealing for 5G and 6G Wireless Networks: A Comparative Study 27

Particle Swarm Optimization versus Simulated Annealing for 5...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Agrawal, Atul Baniya, Pashupati Pandey, Shraiyash Bhushan, Bharat Rakesh, Nitin ITS Engineering College Department of Computer Science and Engineering Greater Noida India IEC College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Polytechnic Institute Purdue University Department of Computer Science and Engineering West LafayetteIN United States Sharda University Department of Computer Science and Engineering Greater Noida India Pune India

ISBN: (纸本)9798350392319

The evolution from 5G to 6G represents a significant leap in wireless communication technology, moving beyond enhanced mobile broadband, low latency, and increased connectivity offered by 5G. The evolution of 5G is expected to create a whole new way to connect and communicate around the world. In this paper, we compare the performance of Particle Swarm Optimization PSO and Simulated Annealing SA in optimizing network density and network interference in 5G and6G wireless networks, focusing on the technological advancements and challenges associated with the transition. PSO is a more effective optimization algorithm than SA across multiple dimensions of network performance, particularly in the challenging context of 6G networks, where the demands for optimization are greater. These results underscore PSO's potential to significantly enhance network performance in the complex, dense environments of next-generation wireless systems. It is crucial to focus on sustainable and energy-efficient solutions to support the anticipated massive increase in connected devices and data traffic, ensuring that the technological advances are matched with responsible development practices. © 2024 IEEE.

关键词： 5G mobile communication systems

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in engineering & sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses 22

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yin, Biao Josselyn, Nicholas Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Snyder, James Zhang, Ziming Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350345346

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing 3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation - promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/ © 2023 IEEE.

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