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Federated Conference on computer science and information Systems (FedCSIS)

作者： Mateusz Płatek Jan Mielniczuk Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Polish Academy of Sciences Institute of Computer Science Warsaw Poland

It is argued that for analysis of Positive Unlabeled (PU) data under Selected Completely At Random (SCAR) assumption it is fruitful to view the problem as fitting of misspecified model to the data. Namely, it is shown that the results on misspecified fit imply that in the case when posterior probability of the response is modelled by logistic regression, fitting the logistic regression to the observable PU data which does not follow this model, still yields the vector of estimated parameters approximately colinear with the true vector of parameters. This observation together with choosing the intercept of the classifier based on optimisation of analogue of F1 measure yields a classifier which performs on par or better than its competitors on several real data sets considered.

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Analysis of Local Rainfall Prediction Based on Intelligent Algorithm 2

Analysis of Local Rainfall Prediction Based on Intelligent A...

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2nd IEEE International Conference on computer Systems, ICCS 2022

作者： Wu, Shenglin Yang, Ying Wu, Qili Mao, Beibei Liu, Zhimin Yu, Hao Long, Mei Ge, Shao-Ting College of Computer Science Technology Qingdao University Qingdao China Ocean University of China Faculty of Information Science and Engineering Qingdao China School of Information Qingdao Technical College Qingdao China Yantai Yuhuangding Hospital Yantai China College of Basic Education Qingdao Insititute of Technology Qingdao China

ISBN: (数字)9781665460101

ISBN: (纸本)9781665460101

This paper discusses the model of Indian Ocean sea area, simulates the rainfall process and the related air sea interaction process in the bay of Bengal on the intraseasonal scale, explores the spatial distribution and temporal variation law of intraseasonal precipitation, and clarifies its related main dynamic factors, then uses the ConvLSTM which is the intelligent algorithm to provide a basis for the selection of seasonal precipitation prediction factors. © 2022 IEEE.

关键词： Forecasting

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Implementation of Steganography Modified Least Significant Bit using the Columnar Transposition Cipher and Caesar Cipher Algorithm in Image Insertion 5

Implementation of Steganography Modified Least Significant B...

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5th International Conference on Computing and Applied Informatics, ICCAI 2020

作者： Handrizal Tarigan, Jos Timanta Putra, Doni Irwansyah Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Jl. University No. 9-A Medan20155 Indonesia

Steganography technique is a technique to hide data or information into other media such as digital images, text, sound, or video. One of the simplest methods of Steganography in the concept of Steganography is the Least Significant Bit. The Least Significant Bit (LSB) method hides the message by inserting the message at the lower or rightmost bits in the cover work file as a medium to hide the message. Caesar Cipher is one method that replaces each letter in the plaintext with letters that are at odds with certain numbers in the alphabet. Columnar transposition Cipher is one method in which messages are written in rows of a specified length, then are read column by column in the order of reading based on a keyword. The message is inserted into the cover image with the target pixel determination to be inserted based on the results of the Caesar Cipher process of the Columnar Transposition Cipher index to the random value of the Multiply With Carry Generator algorithm. The system implementation uses the C# programming language. The results of this study indicate that the implementation of a combination of Caesar Cipher, Columnar Transposition Cipher, and Multiply With Carry Generator can maintain the confidentiality, integrity, and security of data. © Published under licence by IOP Publishing Ltd.

关键词： Steganography

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IoT-Based Healthcare Monitoring System

IoT-Based Healthcare Monitoring System

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Marwa Abdulrahman Al-Hadi Ghaleb Hamoud Al-Gaphari Ibrahim Ahmed Al-Baltah Francisco Bacallao Julian Ahmed Abdulrahman Al-Hadi Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Spain Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions.

关键词： Heart rate Deep learning Power demand Costs Medical services Electrocardiography Real-time systems Internet of Things Biomedical monitoring Monitoring

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Multi-view Semantic information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yiming Cong, Ruixuan Wang, Sizhe Zhao, Mingyuan Zhang, Yang Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets. © 2023 IEEE.

关键词： Semantic Segmentation

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Long Short-Term Memory and Word Embedding For Sentiment Analysis of User Review

Long Short-Term Memory and Word Embedding For Sentiment Anal...

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Smart technology (ICE-SMARTec), IEEE International Conference on

作者： Lia Silviana Erna Budhiarti Nababan Muhammad Zarlis Faculty Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information System Bina Nusantara University Jakarta Indonesia

Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation also affects the performance of sentiment analysis models. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis, it is necessary to use the right labeling method and good text representation to be used as an embedding layer. This study proposes sentiment labeling using Lexicon and Long Short-Term Memory (LSTM) as well as used FastText as embedding words in sentiment classification. As a corpus, the InSet Lexicon Dictionary is employed for feature extraction. The sentiment data used is the reviews given by users on several applications provided on Google Play. The results showed that the LSTM network using Word-embedded FastText with a dimension of 300 words received a small error value of 0.111 with an accuracy of 95.55% for data labeled based on Lexicon.

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Are software projects evaluated using software teams’ success criteria? A systematic literature review

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Procedia computer science 2023年 219卷 1599-1608页

作者： Robert Hans Senyeki Marebane Computer Science Department Tshwane University of Technology Soshanguve South Campus Pretoria 0152 South Africa Faculty of Information and Communication Technology Tshwane University of Technology Emalahleni Campus Emalahleni South Africa

As diverse as software project stakeholders are, so are their project needs and interests. Furthermore, the criteria to measure project success are based on the stakeholder groups’ various needs. It is on the basis of these diverse measurement criteria that project success has become an elusive moving target. Despite this, the measurement and achievement of project success remains a critical milestone in project management for the satisfaction of stakeholders, including software project teams (SPTs). Several research studies have reported project success or failure from various stakeholders’ perspective. However, recent studies have projected SPTs as the most neglected key stakeholder group by software project managers and researchers in the project management field. Given this backdrop, conducting a systematic literature review (SLR) research study to determine if there are empirical studies that have evaluated software project success from SPTs’ perspective would be of interest to many in the field. According to the study's authors there is no study that has been conducted to determine this. The study found one (1) paper, which evaluated a project based on the SPTs’ success criteria, thus showing a research gap and indicating the neglect of this group of stakeholders when it comes to establishing whether software projects meet their needs or not. This study recommends that empirical studies be conducted to close this research gap.

关键词： software project project success project criteria project teams stakeholder

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EMULATING THE HUMAN MIND: A NEURAL-SYMBOLIC LINK PREDICTION MODEL WITH FAST AND SLOW REASONING AND FILTERED RULES

arXiv

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arXiv 2023年

作者： Khojasteh, Mohammad Hossein Torabian, Najmeh Farjami, Ali Hosseini, Saeid Minaei-Bidgoli, Behrouz School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman

Link prediction is an important task in addressing the incompleteness problem of knowledge graphs (KG). Previous link prediction models suffer from issues related to either performance or explanatory capability. Furthermore, models that are capable of generating explanations, often struggle with erroneous paths or reasoning leading to the correct answer. To address these challenges, we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and Slow Thinking with Filtered rules for Link Prediction task), inspired by two distinct aspects of human cognition: "commonsense reasoning" and "thinking, fast and slow." Our objective is to combine a logical and neural model for enhanced link prediction. To tackle the challenge of dealing with incorrect paths or rules generated by the logical model, we propose a semi-supervised method to convert rules into sentences. These sentences are then subjected to assessment and removal of incorrect rules using an NLI (Natural Language Inference) model. Our approach to combining logical and neural models involves first obtaining answers from both the logical and neural models. These answers are subsequently unified using an Inference Engine module, which has been realized through both algorithmic implementation and a novel neural model architecture. To validate the efficacy of our model, we conducted a series of experiments. The results demonstrate the superior performance of our model in both link prediction metrics and the generation of more reliable explanations. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 720-733页

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Implementation Combination of Case-Based Reasoning and Rule-Based Reasoning for Diagnosis of Herpes Disease

Implementation Combination of Case-Based Reasoning and Rule-...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Dian Rachmawati Elviwani Ary Boby Siregar Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798350320725

The skin is the outermost part of the body that is sensitive to external stimuli. One of the skin diseases that can be transmitted is herpes. Belated in understanding herpes skin disease can be fatal to sufferers because herpes disease that is too late to be treated can affect more serious diseases. Therefore, we need a mobile-based application that can identify the infectious skin disease herpes. An expert system is one of the fields of science that can solve a problem in a system. The expert system has several methods that can be used, namely case-based reasoning (CBR) and rule-based reasoning (RBR) methods. CBR can be used to solve a new problem by utilizing previous problems and RBR is a method that can be used to solve a problem using a rule base, where each rule is obtained from experts and literature studies. By applying the combination method of CBR and RBR which can be used to diagnose herpes skin disease, it is hoped that it will prevent the spread of herpes disease from spreading.

关键词： External stimuli Cognition Skin Communications technology Expert systems Diseases

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