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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Miao, Shengyi Zhong, Daming Miao, Runqing Sun, Fuchun Wen, Zhenkun Huang, Haiming Zhang, Xiaodong Wang, Na College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Modern Post Beijing University of Posts and Telecommunications Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing100083 China

ISBN: (纸本)9789819906161

Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： End effectors

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Predicting the Air Pollution Level and Creating an Awareness Platform

Predicting the Air Pollution Level and Creating an Awareness...

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Advancements in Computing (ICAC), International Conference on

作者： M.N Nuha G.K.N.P Wishvajith Amitha Caldera N.T Weerasinghe G.S Weeratunga Shashika Lokuliyana Department of Computer Science and Software Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka Department of Computer Systems & Network Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution.

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New Bounds on the Accuracy of Majority Voting for Multi-Class Classification

arXiv

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arXiv 2023年

作者： Aeeneh, Sina Zlatanov, Nikola Yu, Jiangshan The Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia The Department of Computer Science and Engineering Innopolis University Respublika Tatarstan Innopolis420500 Russia The Department of Software Systems and Cybersecurity Monash University ClaytonVIC3800 Australia

Majority voting is a simple mathematical function that returns the value that appears most often in a set. As a popular decision fusion technique, the majority voting function (MVF) finds applications in resolving conflicts, where a number of independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowd-sourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multi-class classification problem has remained unknown. In this paper, we derive a new upper bound on the accuracy of the MVF for the multi-class classification problem. More specifically, we show that under certain conditions, the error rate of the MVF exponentially decays toward zero as the number of independent voters increases. Conversely, the error rate of the MVF exponentially grows with the number of independent voters if these conditions are not met. We first explore the problem for independent and identically distributed voters where we assume that every voter follows the same conditional probability distribution of voting for different classes, given the true classification of the data point. Next, we extend our results for the case where the voters are independent but non-identically distributed. Using the derived results, we then provide a discussion on the accuracy of the truth discovery algorithms. We show that in the best-case scenarios, truth discovery algorithms operate as an amplified MVF and thereby achieve a small error rate only when the MVF achieves a small error rate, and vice versa, achieve a large error rate when the MVF also achieves a large error rate. In the worst-case scenario, the truth discovery algorithms may achieve a higher error rate than the MVF. Finally, we confirm our theoretical results using numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Data fusion

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Stroke Probability Prediction and Data Visualization Based on Multiple Machine Learning Models 5

Stroke Probability Prediction and Data Visualization Based o...

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5th International Conference on computer Information science and Application Technology, CISAT 2022

作者： Huang, Xinyue Qi, Meng Wang, Yihan Yang, Juntian Computer Science and Software Engineering Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Food Science & Technology School of Agriculture and Biology Shanghai Jiao Tong University Shanghai200240 China College of Chemistry and Molecular Engineering Peking University Beijing100081 China Depu Foreign Language School Chongqing500000 China

ISBN: (纸本)9781510660076

The issue of stroke happening in world range acting a significant problem for human health. Until 2019, there were about 411 death cases per day in America which is 3 years earlier than the appearance of e-cigarettes in 2022. However, the judgement between different doctors could be variable and a slightly human error in complex situations could lead to a bad estimation result. To deal with this, the prediction of stroke probability of patients could provide an objective reference for doctors. This study made a data treatment of factors choosing and translation then calculating 4 accuracy of models and finally an application. To be more specific, an online stroke dataset was employed for factor analysis firstly and three machine learning algorithms were utilized to select most important stroke-related factors. 6 most influential factors were further passed into four machine learning models for training. The work gave the result of the highest accuracy performance of Random Forest between the four models which could be assistance of the prediction in stroke. © 2022 SPIE.

关键词： Forecasting

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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad Customization for Streaming Media

Enhancing Viewer Engagement: Exploring Generative Long Short...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Troy University Montgomery Campus AL. Department of electrical engineering Masters in electrical engineering Sr. Principal Systems Engineer in C & RS Platform services DT Amtrak USA University of south alabama

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad Customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, “ad is something that happens with the viewer”. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization.

关键词： Supply chains Collaboration Companies Streaming media Data models History Advertising Long short term memory Monitoring Consumer electronics

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Blockchain-Based Framework for Stock Market Operations: IPOs, Trading, and Dividend Distribution

Blockchain-Based Framework for Stock Market Operations: IPOs...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Muhammad Sharshar Kareem Elzeky Menna Othman SeifAldin Khaled Hosam Moubarak Walid Gomaa Department of Computer Science Center for Secure Cyber-Physical Systems (C2PS) Khalifa University UAE Department of Natural Language Processing MBZUAI UAE Department of Computer Engineering Cyber-Physical Lab Egypt-Japan University of Science and Technology Egypt Accounting Department Egypt-Japan University of Science and Technology Egypt

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

This study examines blockchain technology's potential to enhance the stock market by offering a decentralized alternative to the traditional system. The study explores the challenges of the current traditional stock market, such as long settlement times, weak transparency, dependency on centralized clearing houses, and heightened fees. The proposed novel system leverages blockchain technology to create a fully decentralized stock market encompassing all fundamental operations from Initial Public Offerings (IPOs) to stock trading and extending to distributing profits or dividends. It connects all market participants through an Ethereum consortium blockchain fuelled by smart contracts. This approach replaces traditional intermediaries like brokers with automated regulations, ensuring more rapid transaction settlements, reduced operational errors and fees, and strengthened security measures. The proposed blockchain-based system, consisting of four smart contracts for IPO, stock exchange, dividend distribution, and participant management, demonstrates remarkable performance, showing promise in efficiently handling the current load of the largest stock markets like the London Stock Exchange (LSE). Through theoretical and practical testing, the system achieved a remarkable throughput of 120 transactions per second and latency or finality of just 4 seconds, underscoring its capability to significantly enhance the efficiency of stock market operations. 1 1 The implementation code is available via GitHub upon request.

关键词： Cloud computing Smart contracts Transforms Throughput Regulation Blockchains Security Stock markets Testing software development management

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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A Hybrid Approach Using Survey and Ism Analysis for Security Coding Risks and Best Practices for Gsd Enterprises

SSRN

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SSRN 2023年

作者： Khan, Rafiq Ahmad Akbar, Muhammad Azeem Alzahrani, Abdulrahman Habibullah, Safa Alwageed, Hathal Salamah Alzahrani, Musaad School of Software Northwestern Polytechnical University Shaanxi Xian710129 China Software Engineering Research Group Department of Computer Science & IT University of Malakand Pakistan Software engineering LUT University Finland Information Systems and Technology Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Department of Computer Science Al-Baha University Al-Baha65799 Saudi Arabia

Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM).Methods: Initially, we conduct a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ***: Initially, we identified 13 major security-coding risks and 82 practices to mitigate these risks. The ISM analysis revealed that out of the identified security-coding practices, "never submit security measures to illegitimate authority", "avoiding buffer overflow and format string vulnerabilities", "control the brute force attack", and "identify a middleman attack" were considered fully dependent. While "avoid revealing information to achieve a secure design" is entirely independent within the GSD security coding ***: The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes. © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Model Predictive Control for Automated Network Assurance in Intent-Based Networking enabled Service Function Chains

Model Predictive Control for Automated Network Assurance in ...

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IEEE Symposium on Network Operations and Management

作者： Marios Avgeris Aris Leivadeas Nikolaos Athanasopoulos Ioannis Lambadaris Matthias Falkner Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering Ácole de technologie supérieure Montreal Canada School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Cisco Systems Ottawa Canada

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications’ requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations.

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Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR

Enhancing Road Safety with Real-Time Helmet Detection and E-...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International Conference on

作者： Deepa S Vinoth Kumar C N S Vivek Duraivelu Balamurugan Easwaran Suguna R Aparna H Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India School of IT Texila American University Zambia Africa Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Tamilnadu M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety.

关键词： YOLO Head Traffic control Road safety Real-time systems Safety Data mining

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