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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Armin Moin Moharram Challenger Atta Badii Stephan Gü nnemann Department of Informatics Technical University of Munich Germany Department of Computer Science Univ. of Antwerp &#x0026 Flanders Make Belgium Department of Computer Science Univ. of Reading United Kingdom Dep. of Informatics Munich Data Science Institute Technical University of Munich Germany

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Software engineering of network-centric Artificial Intelligence (AI) and Internet of Things (IoT) enabled Cyber-Physical Systems (CPS) and services, involves complex design and validation challenges. In this paper, we propose a novel approach, based on the model-driven software engineering paradigm, in particular the domain-specific modeling methodology. We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge. This way, we may increase the service quality of ML, for example, its availability and performance, regardless of the network conditions, as well as maintaining the privacy, security and sustainability. We let practitioners assign ML tasks to heterogeneous edge devices, including highly resource-constrained embedded microcontrollers with main memories in the order of Kilobytes, and energy consumption in the order of milliwatts. This is known as Tiny ML. Furthermore, we show how software models with different levels of abstraction, namely platform-independent and platform-specific models can be used in the software development process. Finally, we validate the proposed approach using a case study addressing the predictive maintenance of a hydraulics system with various networked sensors and actuators.

关键词： Analytical models Computational modeling Software Sensor systems Sensors Internet of Things Security

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Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory

Toxic Comment Detection based on Improved Bi Directional Lon...

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Intelligent Systems and Computational networks (ICISCN), International Conference on

作者： Vidya Kamma Saef Wbaid Sudhakar K Nisha A T. Saravanan Department of Computer Science and Engineering Neil Gogte Institute of Technology Rangareddy Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural networks (CNN)-BiLSTMs.

关键词： Accuracy Sensitivity Semantics User-generated content Bidirectional control Feature extraction Vectors Convolutional neural networks Intelligent systems Long short term memory

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The Impact of the Energy Storage System Operation Strategy on Power Grid Reliability

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Transactions of the Korean institute of Electrical Engineers 2021年第12期70卷 1820-1826页

作者： Kim, Beopsoo Lee, Dayoung Rusetskii, Nikita Shusterzon, Konstantin Sidorov, Denis Kim, Insu Dept. of Electrical Engineering Inha University Korea Republic of Dept. of Electrical and Computer Engineering Inha University Korea Republic of School of Information Technology and Data Science Irkutsk National Research Technical University Russia Energy Systems Institute Siberian Branch Russian Academy of Sciences Russia

Based on the empirical data published by the Korea Electric Power Exchange and the Korea Meteorological Administration, we present a methodology that predicts the system marginal prices. For this purpose, this study uses the long-short term memory (LSTM) technique, which is commonly used in time-series prediction problems. As a result, we present a strategy for operating energy storage systems (ESSs) from the perspective of a prosumer rather than a system operator or conventional utilities. This study also examines the effects of the proposed prosumer actions on the system. The proposed operating strategy is established based on dynamic programming. In particular, this study takes the proposed operation strategy of ESSs the point of view of system reliability. © The Korean institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Reliability

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Transforming Skin Disease Diagnosis: Harnessing Compound-Scaled AI Network for Enhanced Image Analysis and Precision Medicine

Transforming Skin Disease Diagnosis: Harnessing Compound-Sca...

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Disruptive Technologies (ICDT), International Conference on

作者： S. P. Santhoshkumar Torthi Ravichandra Nandipati Mounika R Ranjani S Gajalakshmi Jim Mathew Philip Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Computer Science and Engineering Ellenki College of Engineering and Technology Sangareddy India Department of Artificial intelligence and data science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Millions worldwide suffer from skin conditions, making prompt and precise diagnosis essential to successful treatment. However, conventional diagnostic techniques are time-consuming, dependent on physician knowledge, and frequently unavailable in low-resource locations. The compound-scaled AI network, an advanced deep-learning model, addresses these challenges by providing precise and scalable image analysis. Utilizing high-resolution imaging and compound scaling, it can accurately detect, classify, and segment various skin disorders, including benign and malignant lesions. While questions remain about whether AI matches human specialists in diagnosing all pigmented skin lesions, studies have compared AI performance against experts in identifying clinically significant cases. This research highlights the compound-scaled AI network's potential in dermatology, particularly for telemedicine, early cancer detection, and clinical workflow integration. By enhancing diagnostic accessibility and accuracy, this AI model could transform dermatology, enabling more personalized and equitable treatment worldwide. Impressively, it achieved 99.6% training accuracy and 98.4% testing accuracy, demonstrating deep learning's effectiveness in automating skin disorder detection and classification.

关键词： Training Accuracy Image analysis Precision medicine Computational modeling Dermatology Transforms Skin Lesions Artificial intelligence

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobility and Behavior based Trustable Routing in Mobile Wire...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Praveen Kumar J. Umamageswaran A.V. Kalpana R. Dhanalakshmi Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Routing Throughput Behavioral sciences Delays

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Hemalatha Jayabharathi Ramasamy K. Selvi P. Anandan R. Meenakshi S. P. Maniraj Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Faculty of Management Studies S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management.

关键词： Cloud computing Hospitals Process control User interfaces Control systems HTTP Real-time systems

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Deep Learning and MRI Improve Carotid Arterial Tree Reconstruction

Deep Learning and MRI Improve Carotid Arterial Tree Reconstr...

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data science and Network Security (ICDSNS), International Conference on

作者： Narasimha Murthy M S Gunti Spandan Inakollu Aswani Shankar Nayak Bhukya Ramachandra A C Department of Information Science & Engineering BMS Institute of Technology & Management Bengaluru India Department of Computer Science GST GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of CSE (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This study suggests using magnetic resonance imaging (MRI) scan data to segment and rebuild the carotid artery tree using a deep learning-based method. In the proposed method, the UNET architecture is used to divide an image into parts, and then a 3D level set method is used to make the parts even better. The method was compared to seven different approaches to segmentation and reconstruction, and it outperformed all but one of them. It had a 94% accuracy rate and took 12 hours to process. Deep learning's capacity to reliably segment and categorise pictures, the UNET architecture's ability to maintain spatial information through skip connections, and the level set approach's ability to improve segmentation further all contribute to the proposed method's effectiveness. The proposed approach has the potential to be a useful tool for clinicians in evaluating the status of the carotid artery and finding any irregularities. Stroke is one of the most devastating symptoms of cardiovascular disease, and recognising and treating carotid artery stenosis in its early stages can help avoid or reduce the risk of having a stroke. The success of the proposed method shows that deep learning techniques have the potential to improve the accuracy of carotid arterial tree reconstruction from MRI data. Better identification and treatment of carotid artery stenosis may increase the overall effectiveness of the proposed technique, ultimately leading to better patient outcomes.

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Deep learning approaches for multi-modal sensor data analysis and abnormality detection

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Measurement: Sensors 2024年 33卷

作者： Jadhav, Santosh Pandurang Srinivas, Angalkuditi Dipak Raghunath, Patil Ramkumar Prabhu, M. Suryawanshi, Jaya Haldorai, Anandakumar Department of Information Technology MVPS ‘KBT College of Engineering Maharashtra Nashik India Department of Computer Science Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Engineering Amrutvahini College of Engineering Maharashtra Sangamner India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Center for Future Networks and Digital Twin Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

The information gathered within the structure health monitored (SHM) device would display a range of irregularities mainly a result of sensing defeat, noise disturbance, and different causes. This will greatly impair the structure's security evaluation. This research presents a multipurpose deeper neural network-based data-driven abnormality diagnostic system called SHM. The multipurpose deeper neural networks fuse single-dimensional as well as two-dimensional properties regarding the sensory signals to increase the detecting efficiency. Two separate Convolutional Neural Network, streams within the system are used for obtaining time-frequency characteristics from information collected by sensors (also referred to as two-dimensional-CNN medium) as well as unprocessed one-dimensional characteristics (also referred to as one-dimensional-CNN medium). Following the 2D as well as 1D streams' individual clustering and filtering processes using the sensing information, the two categories of recovered properties have been distorted through single-dimensional matrices that combined within the fusion level. The ideal framework shows the efficacy as well as potential of the suggested approach having a precision percentage of 95.10%. Considering an accurate AI-assisted electronic instrument for evaluating security in structured health management networks, the suggested approach has an exciting period ahead of it. © 2024 The Authors

关键词： Anomaly detection

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QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Yang Wu, Yuan Xing, Huanlai Feng, Li Wang, Tian Jia, Weijia Southwest Jiaotong University School of Computer and Artificial Intelligent Chengdu611756 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform

关键词： Augmented reality

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Towards a Benchmark EO Semantic Segmentation dataset for Uncertainty Quantification

Towards a Benchmark EO Semantic Segmentation Dataset for Unc...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Dawood Wasif Yuanyuan Wang Muhammad Shahzad Rudolph Triebel Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Institute of Robotics and Mechatronics German Aerospace Center Weßling Germany

In order to achieve the objective of accurate and reliable use of deep neural networks for Earth Observation in large-scale scene understanding and interpretation, a large and diverse dataset with proper quantification of uncertainty is required. In this work, we exemplify the lack of a benchmark dataset and present the progress of a novel benchmark dataset for uncertainty quantification of deep learning models in the classic problem of building segmentation from overhead imagery. We present a synthetic dataset where synthetic UAV images were rendered from 3D mesh models of Berlin, Germany. The building masks were extracted from precise LoD-2 building models of the same area. We compare and contrast the performances of baseline methods for semantic segmentation and various uncertainty quantification techniques on this dataset. The experiments show that U-Net is the most accurate model with mIoU of 0.812. Moreover, the Bayesian model is found to be the most reliable uncertainty quantification method on our dataset, with the least ECE.

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