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Con-Fog: Consensus-Driven Fog Node Selection in FU-Serve Platform for IoT Applications

IEEE Internet of Things Journal 2025年第13期12卷 24686-24697页

作者： Imandi, Raju Roy, Arijit Sethi, Kamalakanta Pavan, Kumar Guizani, Mohsen Indian Institute of Information Technology Computer Science and Engineering Group Sri City India Indian Institute of Technology Department of Computer Science and Engineering Patna India Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform. © 2014 IEEE.

关键词： Fog computing

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ISO 24617-8 Applied: Insights from Multilingual Discourse Relations Annotation in English, Polish, and Portuguese 20

ISO 24617-8 Applied: Insights from Multilingual Discourse Re...

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20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation, ISA 2024

作者： Tomaszewska, Aleksandra Silvano, Purificação Leal, António Amorim, Evelin Institute of Computer Science Polish Academy of Sciences Poland University of Porto Centre for Linguistics of the University of Porto Portugal Institute for Systems and Computer Engineering Technology and Science Portugal

ISBN: (纸本)9782493814326

The main objective of this study is to contribute to multilingual discourse research by employing ISO-24617 Part 8 (Semantic Relations in Discourse, Core Annotation Schema – DR-core) for annotating discourse relations. Centering around a parallel discourse relations corpus that includes English, Polish, and European Portuguese, we initiate one of the few ISO-based comparative analyses through a multilingual corpus that aligns discourse relations across these languages. In this paper, we discuss the project’s contributions, including the annotated corpus, research findings, and statistics related to the use of discourse relations. The paper further discusses the challenges encountered in complying with the ISO standard, such as defining the scope of arguments and annotating specific relation types like Expansion. Our findings highlight the necessity for clearer definitions of certain discourse relations and more precise guidelines for argument spans, especially concerning the inclusion of connectives. Additionally, the study underscores the importance of ongoing collaborative efforts to broaden the inclusion of languages and more comprehensive datasets, with the objective of widening the reach of ISO-guided multilingual discourse research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： ISO Standards

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Layer-Wise External Attention by Well-Localized Attention Map for Efficient Deep Anomaly Detection

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SN computer science 2024年第5期5卷 592页

作者： Nakanishi, Keiichi Shiroma, Ryo Hayakawa, Tokihisa Katafuchi, Ryoya Tokunaga, Terumasa Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka-ken Iizuka-shi 820-8502 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka-ken Iizuka-shi 820-8502 Japan

The external attention mechanism offers a promising approach to enhance image anomaly detection (Hayakawa et al., in: IMPROVE, pp. 100-–110, 2023). Nevertheless, the effectiveness of this method is contingent upon the judicious selection of an intermediate layer with external attention. In this study, we performed a comprehensive series of experiments to clarify the mechanisms through which external attention improves detection performance. We assessed the performance of the LEA-Net (Hayakawa et al., in: IMPROVE, pp. 100–110, 2023), which implements layer-wise external attention, using MVTec AD and Plant Village datasets. The detection performances of the LEA-Net were compared with that of the baseline model under different anomaly maps generated by three unsupervised approaches. In addition, we investigated the relationship between the detection performance of LEA-Net and the selection of an attention point, which means an intermediate layer where external attention is applied. The findings reveal that the synergy between the dataset and the generated anomaly map influenced the effectiveness of the LEA-Net. For poorly localized anomaly maps, the selection of the attention point becomes a pivotal factor in determining detection efficiency. At shallow attention points, a well-localized attention map successfully notably improves the detection performance. For deeper attention points, the overall intensity of the attention map is essential;this intensity can be substantially amplified by layer-wise external attention, even for a low-intensity anomaly map. Overall, the results suggest that for layer-wise external attention, the positional attributes of anomalies hold greater significance than the overall intensity or visual appearance of the anomaly map. © The Author(s) 2024.

关键词： Anomaly detection Deep learning MVTec AD Plant science Self-attention Visual attention mechanism Visual inspection AI

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Enhancing Data Security and Privacy through Blockchain and Machine Learning

Enhancing Data Security and Privacy through Blockchain and M...

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2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Machhale, Gurunath Purushothama, B.R. Modi, Chirag N. National Institute of Technology Computer Science and Engineering Goa Cuncolim India National Institute of Technology Computer Science and Engineering Karnataka Surathkal India

ISBN: (纸本)9798350362879

Blockchain and Machine Learning (BML) are two of the most rapidly advancing technologies that are revolutionizing various industries worldwide. Blockchain is a widely known decentralized, immutable technology that provides a transparent, safe method of exchanging and storing data. In contrast, Machine Learning presents organizations with predictive analysis and automated decision-making abilities, enabling them to derive valuable insights from data. This study looks at how blockchain technology and machine learning are combining to try to improve data security and privacy. We have proposed a novel framework with the required components which integrates blockchain and machine learning. We have compared our proposed framework with ACO and SVM implemented frameworks with accuracy metric. Our system gave higher accuracy than former. Later we have provided discussion which encompasses the advantages, obstacles, and future implications of this integration, with a particular emphasis on its applications within the healthcare and financial industries. © 2024 IEEE.

关键词： Blockchain

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Hobby Based Learning for Automata Theory and Computability 2

Hobby Based Learning for Automata Theory and Computability

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pallavi, N. Darshna, A. Naik Manjula, L. Keerthi, P. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306415

In the traditional form of instruction, the teacher and pupil converse face to face. The discussion is started by the instructor, who generally discusses the material from the required textbooks. Many students might not find the concepts intriguing, which would reduce their ability to concentrate and pay attention in class. This makes learning challenging. A novel teaching strategy that allows pupils to learn a subject depending on their interests has been implemented in the classroom. With this approach, students apply their own hobbies to the subject they wish to learn. It focuses on interest-driven learn. This method focuses on interest driven learning where students apply their Personal interest to the subject they want to learn. A prime example of interest-driven learning is hobby-based learning. Modelling enthusiasm and making the topics relevant to their hobby would make the classroom student centric learning and interest driven learning where student would learn the topic self-directed, enjoy learning process, gain deeper insight on the topic and also develop their hobbies. Hence hobby-based learning was introduced to the classroom for the subject Automata Theory and Computability. A comparative study was made on analysis and data obtained from classrooms with hobby based learning and conventional learning handled by the same instructor on the subject Automata Theory and Computability where hobby-based learning gave significantly best feedback than conventional learning. © 2024 IEEE.

关键词： Hobby based learning interest driven learning student centric learning

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An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

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Multimedia Tools and Applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

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Mathematical Biosciences and engineering 2024年第3期21卷 4742-4761页

作者： Prethija, G. Katiravan, Jeevaa School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Information Technology Velammal Engineering College Chennai600066 India

Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively. © 2024the Author(s).

关键词： Drops

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks 9

A Trust-based Framework for the Detection and Avoidance of B...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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