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IEEE International Conference on Edge Computing (EDGE)

作者： Yuyang Huang Faishal Zharfan Hendrawan Hendrawan Haryadi S. Gunawi Junchen Jiang Department of Computer Science University of Chicago Chicago USA School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application’s *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application’s accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application’s sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics.

关键词： Accuracy Sensitivity Visual analytics Estimation Bandwidth Edge AI Channel allocation

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Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Amrutha Sriya Ganjam Kishor Kumar Reddy C D Radhika Karthik Ganjam Monika Singh.T Keerthi Sai Akuthota Department of Computer Science Stanley College of engineering and technology for womenabids Hyderabad India Department School of informatics MSc Data Science University of Edinburgh Edinburgh Scotland UK Deptartment. of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches.

关键词： Training Pathology Accuracy Image recognition Error analysis Computed tomography Lungs Data models Convolutional neural networks Diseases

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images

A Systematic Literature Review of XAI-based Approaches on Br...

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Advanced Research in Computing (ICARC), International Conference on

作者： Savin Madapatha Pumudu Fernando Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection.

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems. © 2024 IEEE.

关键词： Anomaly detection

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Distributed Gibbs Sampling and LDA Modelling for Large Scale Big Data Management on PySpark 7

Distributed Gibbs Sampling and LDA Modelling for Large Scale...

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7th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Christos Karras, Aristeidis Tsolis, Dimitrios Giotopoulos, Konstantinos C. Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of History and Archaeology Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Big data management methods are paramount in the modern era as applications tend to create massive amounts of data that comes from various sources. Therefore, there is an urge to create adaptive, speedy and robust frameworks that can effectively handle massive datasets. Distributed environments such as Apache Spark are of note, as they can handle such data by creating clusters where a portion of the data is stored locally and then the results are returned with the use of Resilient Distributed Datasets (RDDs). In this paper a method for distributed marginal Gibbs sampling for widely used latent Dirichlet allocation (LDA) model is implemented on PySpark along with a Metropolis Hastings Random Walker. The Distributed LDA (DLDA) algorithm distributes a given dataset into P partitions and performs local LDA on each partition, for each document independently. Every nth iteration, local LDA models, that were trained on distinct partitions, are combined to assure the model ability to converge. Experimental results are promising as the proposed system demonstrates comparable performance in the final model quality to the sequential LDA, and achieves significant speedup time-optimizations when utilized with massive datasets. © 2022 IEEE.

关键词： Information management

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Dual-Tree Genetic Programming With Adaptive Mutation for Dynamic Workflow Scheduling in Cloud Computing

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Yifan Chen, Gang Ma, Hui Hartmann, Sven Zhang, Mengjie School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany

Dynamic workflow scheduling (DWS) is a challenging and important optimization problem in cloud computing, aiming to execute multiple heterogeneous workflows on dynamically leased virtual machine resources to satisfy user-defined Quality of Service requirements. For the popular deadline-constrained DWS in cloud problem, a virtual machine selection rule (VMSR) and a task selection rule (TSR) need to be designed simultaneously to minimize the rental fee and deadline violation penalty. For this purpose, Dual-Tree Genetic Programming (DTGP) has been previously developed to automatically evolve effective VMSRs and TSRs. However, existing DTGP approaches assume that VMSR and TSR, as well as terminals used by VMSRs and TSRs are equally important and evolve both VMSRs and TSRs in a black box manner, i.e., without using any knowledge about different impacts of trees and terminals. Several recent studies clearly indicate that different trees or terminals have varied performance impacts, making it critical to develop adaptive mutation mechanisms for effective DTGP. Driven by this motivation, this paper proposes two new levels of adaptive mutation mechanisms, contributing to the development of a new DTGP algorithm, which features the use of three new probability vectors for adaptive tree selection of VMSR and TSR at the first level and adaptive terminal selection at the second level while mutating any existing dual-tree individuals. Extensive experimental results demonstrate that the proposed two adaptive mechanisms can improve the effectiveness of DTGP compared to four baseline algorithms. IEEE

关键词： Heuristic algorithms

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Machine Learning based Classification Algorithm for Classification of Dengue (Dengue Fever -DF, Dengue Harmonic Fever -DHF & Serve Dengue -SD) 1

Machine Learning based Classification Algorithm for Classifi...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kapoor, Rajeev Ahuja, Sachin Kadyan, Virender Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781607685395

Dengue is becoming a burden for society worldwide and become challenge for the world. The main objective of this research paper is to classify dengue at an early stage. The author has adopted a methodology that is divided into five phases namely Data Collection, Transmission of Raw Data in Structural data format, Data Preprocessing, Model building with Train & Test Datasets and Prediction. Further, the proposed prediction model has been trained with the help of 250 data samples from primary sources namely clinical labs and 1000 data sample in the form of synthetic datasets adopted from 20 research papers. The proposed prediction Model has been tested and validated with the help of comparative study of different machine learning algorithms namely Decision Tree, Logistical Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes etc. The proposed model has achieved 97.39% accuracy, 0.98 Precision, 0.97 Recall, 0.97 F1 Score, 0.02 MS and 0.14 RMSE. © The Electrochemical Society

关键词： Dengue Prediction Model Machine Learning Prediction Model Vector Borne Disease

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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A hybrid approach of simultaneous segmentation and classification for medical image analysis

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Multimedia Tools and Applications 2024年

作者： Yang, Chao-Lung Harjoseputro, Yulius Chen, Yung-Yao Department Industrial Management National Taiwan University of Science and Technology Taipei106 Taiwan Department Electronics and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Department Informatics Universitas Atma Jaya Yogyakarta Yogyakarta55281 Indonesia

Medical image analysis is a crucial step required for accurate disease diagnosis, treatment planning, and condition monitoring. In recent years, the field has undergone a groundbreaking transformation due to the advancement in artificial intelligence (AI) and deep learning (DL). These cutting-edge developments have particularly revolutionized automated segmentation and classification tasks, making them more efficient and reliable. The simultaneous performance of segmentation and classification enables the AI model to identify and isolate regions of interest, thereby enhancing the accuracy of the classification process. Several research reports showed that this simultaneous optimization posed a significant challenge, particularly in medical image analysis, of capturing intricate details and complex structures and having mitigating issues, such as vanishing gradient, distracting information, and multi-scale context. Therefore, this study proposed a hybrid hierarchical approach, SSC (Simultaneous Segmentation and Classification), integrating image segmentation and classification tasks within a redesigned network architecture. The main contribution of the study was the combination of the UNet architecture, classification block (Cblock), residual block, attention mechanism, and Atrous Spatial Pyramid Pooling (ASPP) block together to achieve the improvement of segmentation and classification. A thorough comparative analysis and performance evaluation on two benchmark datasets, namely COVID-19 Radiography (CoR) and Breast Ultrasound Images (BUSI), were also conducted with 5-fold cross-validation. The experimental results showed that the proposed SSC model outperformed the baseline models. For the CoR dataset, the segmentation accuracy, Dice, IoU, classification accuracy, Recall, and Precision increased by 1.16%, 1.68%, 3.11%, 0.61%, 1.11%, and 1.20%, respectively. Meanwhile, for the BUSI dataset, 3.98%, 1.09%, 8.21%, 0.20%, 4.72%, and 1.55% increments were observed in the

关键词： Image segmentation

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