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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Das, Puja Haldar, Amrita Singh, Moutushi Pise, Anil Audumbar Guha Roy, Deepsubhra Department of Computer Science HMM College for Women Dakshineswar Kolkata700035 India Department of Computer Science and Business Studies IEM Kolkata700091 India Department of Information Technology IEM Salt Lake Kolkata700091 India Department of Data Science and Machine Learning Computer Science University of the Witwatersrand Johannesburg South Africa Institute of Engineering and Management Kolkata700091 India

ISBN: (纸本)9789819944323

Foodborne disease is still a major public health concern in both advanced and emerging economies. In terms of adopting new technology, the health industry continues to lag behind all other sectors. India has traditionally relied on food farming to make money but which food item is good for whom we have no proper idea. Especially when it comes to healthcare sectors, the Internet of health things (IoHT) and blockchain can play an important role. It can enhance the quality of care for its users and reduce costs by allocating the correct food chain to the targeted users. We are currently employing blockchain in supply chain management to cluster the patients based on their health care type. For example, diabetic patients prefer a particular type of food. Segregating the food type within the supply chain can reduce a lot of effort and cost. Blockchain can provide a solution to the issues that arise in the healthcare-based supply chain system because of its decentralized and immutable nature. The service's execution has been depicted in pictures and accompanied by detailed descriptions. The ultimate result of this study was to demonstrate the immutability, availability, and transparency security of blockchain in the healthcare-based supply chain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Deep Multi-View Clustering via View-Specific Representation and Global Graph 7

Deep Multi-View Clustering via View-Specific Representation ...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Pu, Jingyu Cui, Chenhang Ren, Yazhou Wen, Zichen Wu, Tianyi Pu, Xiaorong He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering Bethlehem United States

ISBN: (纸本)9798350359145

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method. © 2023 IEEE.

关键词： Embeddings

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Improved determination of the weights in a clustering approach based on a weighted dissimilarity measure between fuzzy data

Improved determination of the weights in a clustering approa...

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2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022

作者： Eskandari, Elham Khastan, Alireza Tomasiello, Stefania Institute for Advanced Studies in Basic Sciences Department of Mathematics Zanjan Iran University of Tartu Institute of Computer Science Tartu Estonia

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

In the literature, a popular dissimilarity measure between each pair of fuzzy data is defined as a weighted sum of the squared Euclidean distances among the centers and the spreads. The flexible weights introduced by D'Urso and Giordani in [Computational Statistics & Data Analysis 50 (2006) 1496-1523] may be helpful in certain situations as shown in that paper. The flexible weights are obtained by the minimization algorithm, and a larger weight is given to the center distance than the spread *** the present study, the weights are proposed to be fixed and equal to 0.5, which can contribute to higher performances of clustering methods in some cases. For instance, where the distance among the spreads is longer than that among the centers, i.e., the centers do not play a relevant role while the spreads play the predominant role, it is clear that a clustering method that takes the advantage of equal weights would outperform the other. Indeed, when there is no external condition, choosing the weights fixed and equal to 0.5 would be the best *** deeply investigate our claim, we provide a wide simulation study to show whether and under what circumstances the flexible weights are (not) helpful. The results furnished by numerical experiments on both simulated and benchmark datasets demonstrate the merit of using the fixed and equal to 0.5 weights suggested in the present paper. © 2022 IEEE.

关键词： Cluster analysis

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The Laplacian Energy of Hesitancy Fuzzy Graphs in Decision-Making Problems

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computer Systems science & Engineering 2023年第3期44卷 2637-2653页

作者： N.Rajagopal Reddy Mohammad Zubair Khan S.Sharief Basha Abdulrahman Alahmadi Ahmed H.Alahmadi Chiranji Lal Chowdhary School of Advanced Sciences Vellore Institute of TechnologyVellore632014TamilnaduIndia Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia Department of Mathematics School of Advanced SciencesVellore Institute of TechnologyVellore632014TamilnaduIndia School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the *** for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM *** objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy ***:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in ***:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth *** aim to identify the best one from the different testing *** are alsofinding the best operation schemes in the *** this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.

关键词： Hesitancy fuzzy graphs(HFGs) laplacian energy satellite communication system reservoir operation schemes decision-making

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Advances and Innovation in Digital Technologies

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Recent Patents on Engineering 2022年第2期16卷 1-2页

作者： Tanwar, Poonam Madaan, Rosy Computer Science & Engineering Department Manav Rachna International Institute of Research & Studies Haryana Faridabad121004 India

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Classification Medical Data Analysis Medical Imaging

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Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law 17

Annotators-in-the-loop: Testing a Novel Annotation Procedure...

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17th Linguistic Annotation Workshop, LAW 2023, LAW 2023, co-located with ACL 2023

作者： Zanoli, Emma Barbini, Matilde Riva, Davide Picascia, Sergio Furiosi, Emanuela D’Ancona, Stefano Chesi, Cristiano IUSS Pavia School for Advanced Studies NETS Lab. Italy Università degli Studi di Milano Department of Computer Science Italy IUSS Pavia School for Advanced Studies Italy

ISBN: (纸本)9781959429838

The availability of annotated legal corpora is crucial for a number of tasks, such as legal search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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An AI-integrated Enhanced Anatomical Brain Tumor Segmentation in Substantia Nigra for Early Disease Identification using Deep Resnet50-CNN

An AI-integrated Enhanced Anatomical Brain Tumor Segmentatio...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： P. V. Ganeshkumar S. Prasanna Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Application Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification.

关键词： Neuroimaging Image segmentation Accuracy Magnetic resonance imaging Brain tumors Feature extraction Pattern recognition Particle swarm optimization Tumors Diseases

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Navigating the Blockchain Landscape with a Comprehensive Survey of Smart Contracts, Ethereum, and Consensus Mechanisms 4

Navigating the Blockchain Landscape with a Comprehensive Sur...

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Suresh Kumar, N. Dolly Brinthakumari, S. Partheeban, N. School of Computer Science and Engineering Ramanagara India Jagan Institute of Management Studies Department of Information Technology Delhi India New Horizon Institute of Technology and Management Department of Computer Engineering Thane India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350393354

In the dynamic landscape of blockchain technology, this research paper meticulously explores and contrasts two pivotal paradigmsSmart Contracts and Traditional Contractswithin the overarching framework of decentralized agreements. In the pursuit of this, the research diligently examines the subtle differentiations inherent in the various aspects of execution, intermediaries, trust, transparency, speed, costs, flexibility, security, and enforceability. While Smart Contracts, executed automatically through code on blockchain platforms such as Ethereum, offer unprecedented efficiency and transparency, Traditional Contracts, reliant on manual execution and potential intermediaries like lawyers, maintain a familiar yet potentially more cumbersome approach. The research extends its focus to Ethereum, a groundbreaking blockchain platform, spotlighting its instrumental role in upholding data security and integrity. The subsequent challenge under consideration is Ethereum's key aspects, such as decentralization, immutability, cryptography, smart contracts, consensus mechanisms, transparent ledger, tamper resistance, encryption, and permissioned access. A pivotal facet of this study centers on the comprehensive examination and comparison of consensus mechanisms employed in blockchain systems. This research assesses and compares key parameters of prominent blockchain consensus mechanisms, emphasizing factors like accuracy, time efficiency, scalability, and energy efficiency. The research culminates in a robust conclusion that synthesizes key findings, emphasizing Ethereum's role in ensuring data security and integrity, and the intricate landscape of consensus mechanisms. © 2024 IEEE.

关键词： Smart contract

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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