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data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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Kathakali Mudras: An AI-Enhanced Predictive Approach

Kathakali Mudras: An AI-Enhanced Predictive Approach

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Jaison Paul Mulerikkal K Janani P.S Sreelakshmi Divya James K.S Lakshmi T.D Sajanraj A Binu Department of Information Technology Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering Department of Computer Science and Business Systems Department of Artificial Intelligence and Data Science Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8’s real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras.

关键词： Support vector machines Hands Accuracy Predictive models Prediction algorithms Real-time systems Object recognition Cultural differences Few shot learning Machine intelligence

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Characteristics Exploration of Hospitals in Shanghai through Spatial Relationship Using Taxi data 7

Characteristics Exploration of Hospitals in Shanghai through...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Zhou, Yixuan Zhang, Lei Xu, Qin Liu, Yixiao Fu, Wen Tongji University Department of Traffic Information and Control Engineering Shanghai China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China City University of Macau Faculty of Data Science China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

With the increasing volume of spatiotemporal traffic data, many scholars used it to explore the characteristics of urban facilities. In this paper, a method of classifying the main tertiary hospitals in Shanghai using taxi data of online car-hailing was proposed. Rasterization of the researched area and extraction of OD (Origin-Destination) records were done, and an optimum buffer distance threshold was determined. By comparing the previous studies, we designed the clustering approach (based on K-Means algorithm) and set new inputs based on trips at different distances from a researched hospital. A reasonable result was achieved: 4 types of hospitals are obtained and a discussion was conducted. This research is valuable for identifying the function and orientation of hospitals, and can help government better understand the spatial relationship of hospitals. © 2022 IEEE.

关键词： Hospitals

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification

A Novel Breast Cancer Detection and Classification Using Enh...

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IoT Based Control Networks and Intelligent systems (ICICNIS), International Conference on

作者： P. Mathiyalagan G. Kiruthiga A. Rasmi M. Vadivel K.V. Mahalakshmi T.S. Poornappriya Department of CSE Sri Ramakrishna Engineering College Coimbatore Dept of Artificial intelligence and Data science Karpagam College of Engineering Coimbatore Information Science and Engineering RR Institute of Technology Bangalore Department of Information Technology Excel Engineering College Namakkal TamilNadu CSE B V Raju Institute of Technology Telangana Data and Analytics Aspire Systems Pvt Ltd Bengaluru Karnataka

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography.

关键词： Image segmentation Sensitivity Mortality Predictive models Inspection Breast cancer Reliability Lesions Intelligent systems Medical diagnostic imaging

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Real-Time data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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Serious Games in Digital Gaming: A Comprehensive Review of Applications, Game Engines and Advancements

arXiv

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arXiv 2023年

作者： Gazis, Alexandros Katsiri, Eleftheria Democritus University of Thrace School of Engineering Department of Electrical and Computer Engineering Xanthi67100 Greece Institute for the Management of Information Systems Athena Research & Innovation Center in Information Communication & Knowledge Technologies Marousi15125 Greece

- Serious games are defined as applied games that focus on the gamification of an experience (e.g., learning and training activities) and are not strictly for entertainment purposes. In recent years, serious games have become increasingly popular due to their ability to simultaneously educate and entertain users. In this review, we provide a comprehensive overview of the different types of digital games and expand on the serious games genre while focusing on its various applications. Furthermore, we present the most widely used game engines used in the game development industry and extend the Unity game machine advantages. Lastly, we conclude our research with a detailed comparison of the two most popular choices (Unreal and Unity engines) and their respective advantages and disadvantages while providing future suggestions for serious digital game development. © 2023, CC BY.

关键词： Serious games

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Integrating Attribute information into Relational data: A Latent Structure Approach

Integrating Attribute Information into Relational Data: A La...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Kenta Mikawa Manabu Kobayashi Tomoyuki Sasaki Akiko Manada Department of Information Systems Tokyo City University Kanagawa Japan Center for Data Science Waseda University Tokyo Japan Department of Computer Science Tokyo City University Tokyo Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Various analytical methods for diverse data have been proposed with the evolution of information technology. This study focuses on relational data, comprising information on relationships between objects. Although several studies have focused on the relationships between objects, only a few have utilized attribute information. The conventional study proposed analyzing relational data with attribute information using a latent structure model. However, the model assumes that both attribute information and relational data are generated by a single latent variable. Furthermore the model does not effectively handle missing values in relational data. Therefore, in this study, we modify the conventional latent structure analysis model for relational data with attribute information, which assumes that latent variables have various types of structures, and derived a method for estimating its parameters. In addition, we apply the proposed model to cases when the relational data contain missing values. The effectiveness of our proposed method is demonstrated through simulation experiments on synthetic dataset.

关键词： Analytical models data models information technology Synthetic data

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Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

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Computers, Materials & Continua 2021年第7期68卷 921-939页

作者： Bader Rasheed Adil Khan S.M.Ahsan Kazmi Rasheed Hussain Md.Jalil Piran Doug Young Suh Institute of Data Science and Articial Intelligence Innopolis UniversityInnopolis420500Russia Institute of Information Security and Cyberphysical Systems Innopolis UniversityInnopolis420500Russia Department of Computer Science and Engineering Sejong UniversitySeoulKorea Department of Electronics Engineering Kyung Hee UniversityYonginKorea

Detecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing *** researches have investigated the role of machine learning(ML)models to detect malicious *** using ML algorithms,rst,the features of URLs are extracted,and then different ML models are *** limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the ***,deep learning(DL)models are used to solve these issues since they are able to perform featureless ***,DL models give better accuracy and generalization to newly designed URLs;however,the results of our study show that these models,such as any other DL models,can be susceptible to adversarial *** this paper,we examine the robustness of these models and demonstrate the importance of considering this susceptibility before applying such detection systems in real-world *** propose and demonstrate a black-box attack based on scoring functions with greedy search for the minimum number of perturbations leading to a *** attack is examined against different types of convolutional neural networks(CNN)-based URL classiers and it causes a tangible decrease in the accuracy with more than 56%reduction in the accuracy of the best classier(among the selected classiers for this work).Moreover,adversarial training shows promising results in reducing the inuence of the attack on the robustness of the model to less than 7%on average.

关键词： Malicious URLs detection deep learning adversarial attack web security

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Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Novel Technique for Improving Pedestrian Detection Accuracy and Processing Efficiency 7

Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Nov...

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7th International Conference on Image information Processing, ICIIP 2023

作者： Bhavani, N.P.G. Sudha, R.V. Priya, K.Shanmuga Arun Raj, S.R. Yadav, Vinay Kumar Balaram, Allam Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Electronics And Communication Engineering Tamilnadu India Knowledge Institute of Technology Department of Computer Science And Business Systems Selliampalayam Tamil Nadu Kakapalayam India KSR Institute For Engineering And Technology Department of Information Technology TamilNadu Tiruchengode637215 India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： Computer vision

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