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International Conference on Cyber and IT Service Management (CITSM)

作者： Ankur Singh Bist Bhupesh Rawat Sandy Kosasi Qurotul Aini Fitra Putri Oganda Ahmad Bayu Yadila Computer Sc. & engg. Graphic Era Hill University Uttarakhand India Faculty of Informatics Engineering STMIK Pontianak West Kalimantan Indonesia Information System University of Raharja Tangerang Indonesia Computer System University of Raharja Tangerang Indonesia

Stock prices are highly volatile, dynamic, and non-linear, making it very difficult to predict the exact price at any given time. In addition, stock prices are influenced by several factors, such as political conditions, the global economy, unexpected events, company financial performance, and more. Up to this point, various machine learning techniques have been employed for stock prediction; however, none of these techniques can accurately predict stock prices due to the high volatility in stock prices. Machine learning approaches, such as random forest, SVM, KNN, and logistic regression, represent some of the algorithms used for stock prediction. This paper aims to propose a new framework based on machine learning and deep learning for stock prediction. The prediction relies on the company’s stock fundamentals, past performance, related stocks in the same sector, and other relevant factors.

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Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 827-838页

作者： Kui, Xiaoyan Ji, Zexin Zou, Beiji Li, Yang Dai, Yulan Chen, Liming Vera, Pierre Ruan, Su Central South University School of Computer Science and Engineering Changsha 410083 China Henri Becquerel Cancer Center Department of Nuclear Medicine Rouen France Hunan University of Chinese Medicine School of Informatics Changsha 410208 China Ecole Centrale de Lyon Department of Mathematics and Computer Science Ecully 69130 France University of Rouen-Normandy AIMS - QuantIF UR 4108 Rouen F-76000 France

High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs the extraction of the features and then upscaling based on the features. The features extracted may not be complete for super-resolution. Recent multi-task learning, including reconstruction and super-resolution, is a good solution to obtain additional relevant information. The interaction between the two tasks is often insufficient, which still leads to incomplete and less relevant deep features. To address above limitations, we propose an iterative collaboration network (ICONet) to improve communications between tasks by progressively incorporating reconstruction prior to the super-resolution learning procedure in an iterative collaboration way. It consists of a reconstruction branch, a super-resolution branch, and a SR-Rec fusion module. The reconstruction branch generates the artifact-free image as prior, which is followed by a super-resolution branch for prior knowledge-guided super-resolution. Unlike the widely-used convolutional neural networks for extracting local features and Transformers with quadratic computational complexity for modeling long-range dependencies, we develop a new residual spatial-channel feature learning (RSCFL) module of two branches to efficiently establish feature relationships in spatial and channel dimensions. Moreover, the designed SR-Rec fusion module fuses the reconstruction prior and super-resolution features with each other in an adaptive manner. Our ICONet is built with multi-stage models to iteratively upscale the low-resolution images using steps of 2x and simultaneously interact between two branches in multi-stage supervisions. Quantitative and qualitative experimental results on the benchmarking dataset show that our ICONet outperforms most state-of-the-art approaches. © 2015 IEEE.

关键词： medical imaging multi-task learning reconstruction Super-resolution

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Image Classification of Decapterus Macarellus Using Ridge Regression 8

Image Classification of Decapterus Macarellus Using Ridge Re...

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8th International Conference on Education and Technology, ICET 2022

作者： Jerandu, Charmelia Yunizar Mondolang, Alicia Herlin Sianturi, Corazon Olivia Sianturi, Shine Crossifixio Ximenes, Jenita Felixia Gumelar, Agustinus Bimo Sinlae, Alfry Aristo Jansen Batarius, Patrisius Yuhana, Umi Laili Sooai, Adri Gabriel Universitas Katolik Widya Mandira Dept. of Computer Science Kupang Indonesia Universitas Negeri Malang Dept. of Mathematics Malang Indonesia Universitas Sanata Dharma Dept. of Mathematics Yogyakarta Indonesia Universitas Sanata Dharma Dept. of Computer Science Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes. The dataset used is a primary dataset consisting of fish images, arranged in two classes, namely 71 fresh fish and 96 rotten fish. The methods used are k-NN classifiers, naive bayes and ridge regression. Experiments were drawn up to classify rotten fish and fresh fish. The preprocessing was assisted by InceptionV3 as a feature extraction method. Furthermore, the image data is trained in a ratio of 60:40 for training and testing data. Validation was performed using 2-fold cross validation with the results obtained being 99.4%, 94% and 100% accuracy using the classifiers namely k-NN, naive bayes and ridge regression, respectively. © 2022 IEEE.

关键词： Image classification

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Knowledge transfer for Object Detection with Evolution architecture search 4

Knowledge transfer for Object Detection with Evolution archi...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Li, Jiaquan Hong, Haokai Shi, Minghui Lin, Qiuzhen Zhou, Fenfen Tan, Kay Chen Jiang, Min Xiamen University School of Informatics Fujian China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longjing Environment Protection Co. Ltd Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep learning has been proved to achieve excellent results in various fields, and appropriate network architecture and sufficient data play an important role. Due to the high cost of annotation for the task of object detection, domain adaptation methods have been introduced in this field. But these methods are based on the rigid network architecture and bound to the input dimension of the adaptive module, which is not only difficult to better balance accuracy and speed of detection model, but also can not use the multi-scale training method, resulting in the reduction of the application scenario of the model. Inspired by this problem, we propose a new object detection method based on multi-scale adversarial domain adaptation and network architecture search. An evolutionary algorithm is adopted to help search the network architecture to balance accuracy and speed. The ability of domain adaptation can also be effectively improved by the searched architecture. The experimental results have demonstrated the significant improvement that benefited from the framework in terms of its performance and computational efficiency in solving unlabeled object detection. © 2022 IEEE.

关键词： Network architecture

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Real-time Vital Signs Monitoring Based on COTS WiFi Devices

Real-time Vital Signs Monitoring Based on COTS WiFi Devices

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Gu, Yu Zhang, Xiang Yan, Huan Liu, Zhi Ji, Yusheng Hefei University of Technology School of Computer and Information Hefei China The University of Electro-Communications Dept. of Computer and Network Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9781665401265

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method. © 2021 IEEE.

关键词： Wi-Fi

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Horizontal scaling method for a hyperconverged network

Horizontal scaling method for a hyperconverged network

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2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Kovalenko, Andriy Kuchuk, Heorhii Kuchuk, Nina Kostolny, Jozef Kharkiv National University of Radio Electronics Faculty of Computer Engineering and Control Kharkiv Ukraine National Technical University KhPI Faculty of Computer and Information Technologies Kharkiv Ukraine Faculty of Management Science and Informatics Univeraity of Zilina Zilina Slovakia

ISBN: (纸本)9781665436922

Today cloud platforms are gradually being replaced by hyperconverged. With a hyperconverged infrastructure, the servers, networks, storage and computing power are combined. This is done through specific software. The main advantage of hyperconverged network is to reduce operating costs. However, system performance can be reduced due to the standardization of hardware modules and centralization of control, and, consequently, QoS deteriorates. At the same time, companies that operate computer systems are attracted by the ability to quick horizontal scaling and lower operating costs. Hyperconverged networks often use addition of a new hardware modules to meet QoS requirements using a simple horizontal scaling implementation. The purpose of the paper is to develop such a horizontal scaling method, which allows decreasing transaction processing time while total cost of data transmission does not exceed the specified values. The method also allows determining the optimal characteristics of additional hardware. © 2021 IEEE.

关键词： Resource allocation

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Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture Trends in the Digital Transformation...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Irfan Mahendra Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Nusa Mandiri University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives; business architecture; data & information architecture; information system architecture; infrastructure and network architecture; security architecture; human resources architecture; governance, legal, and compliance; and standards & regulations.

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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

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computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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NetDriller-V3: A Powerful Social Network Analysis Tool

NetDriller-V3: A Powerful Social Network Analysis Tool

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Salim Afra Tansel Özyer Jon Rokne Reda Alhajj Department of Computer Science University of Calgary Alberta Canada Department of Computer Engineering Ankara Medipol University Ankara Turkey Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Health Informatics University of Southern Denmark Odense Denmark

The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.

关键词： Measurement Knowledge engineering Social networking (online) Databases Blogs Machine learning Data collection

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On the Evaluation of DHT22 Temperature Sensor for IoT Application 8

On the Evaluation of DHT22 Temperature Sensor for IoT Applic...

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8th International Conference on computer and Communication engineering, ICCCE 2021

作者： Ahmad, Yasser Asrul Surya Gunawan, Teddy Mansor, Hasmah Hamida, Belal Ahmed Fikri Hishamudin, Adam Arifin, Fatchul International Islamic University Malaysia Electrical and Computer Engineering Dept. Gombak Malaysia Universitas Negeri Yogyakarta Electronics and Informatics Eng. Dept. Yogyakarta Indonesia

ISBN: (纸本)9781728110646

Temperature is one of the most monitored items in many industrial and commercial applications. Internet of Things (IoT) temperature sensors are commonly small, low power, and able to measure humidity as well. Obtaining timely, accurate temperature information is critical in an IoT system. This paper aims to evaluate an IoT temperature system's performance by comparing it with an industrial system. In this project, an IoT temperature monitoring system has been developed using a DHT22 temperature sensor and a Raspberry Pi to represent an IoT temperature monitoring system. The system is evaluated by comparing its temperature measurement with a Keithley 6517-TP industrial grade temperature probe connected to Keithley 2110 benchtop digital multimeter as the acquisition system. The IoT temperature monitoring system utilizing the DHT22 sensor provides accurate measurement up to 0.10C but a slower response in detecting temperature change. This finding would be able to provide a reference in developing an IoT system that requires temperature monitoring. © 2021 IEEE.

关键词： Internet of things

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