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9th International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2022

作者： Oshimo, Hayato Hironaka, Shiori Yoshida, Mitsuo Umemura, Kyoji Toyohashi University of Technology Department of Computer Science and Engineering Aichi Japan University of Tsukuba Faculty of Business Sciences Tokyo Japan

ISBN: (数字)9781665456685

ISBN: (纸本)9781665456685

Analyzing following behavior is important in many applications. Following behavior may depend on the main intention of the follower. Users may either follow their friends or they may follow celebrities to know more about them. It is difficult to estimate users' intention from their following relationships. In this paper, we propose an approach to analyze following relationships. First, we investigated the similarity between users. Similar followers and followees are likely to be friends. However, when the follower and followee are not similar, it is likely that follower seeks to obtain more information on the followee. Second, we categorized users by the network structure. We then proposed analysis of following behavior based on similarity and category of users estimated from tweets and user data. We confirmed the feasibility of the proposed method through experiments. Finally, we examined users in different categories and analyzed their following behavior. © 2022 IEEE.

关键词： Social networking (online) Buildings Behavioral sciences Informatics

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Analysis of Losses and Voltages in Prosumer-Rich Distribution Feeders

Analysis of Losses and Voltages in Prosumer-Rich Distributio...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Lukic, Ivan Cvek, Kristijan Fekete, Kresimir Dubravac, Marina J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

This paper presents the analysis of voltage profile and active power losses in a low voltage distribution network (0.4 kV) with connected prosumers that have installed photovoltaic (PV) power plants in their houses. A computer model of three radial low voltage feeders is created in DIgSILENT PowerFactory software for simulation purposes. The analysis is done for various scenarios of PV active and reactive power production. Conventional as well as optimal power flows (OPF) are analyzed and compared. In two scenarios, optimal power flows are used to optimize voltage profile and losses using PVs reactive power injections. The obtained results indicate that the OPF did not achieve a significant reduction in active power losses. However, a noticeable improvement in voltage profile is accomplished regarding conventional power flow. © 2022 IEEE.

关键词： Electric power distribution

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Automated COVID-19 screening framework via Deep Convolutional Neural Network with Chest X-ray Medical Images 6

Automated COVID-19 screening framework via Deep Convolutiona...

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6th International Conference on Information technology, InCIT 2022

作者： Damkham, Waris Thaipisutikul, Tipajin Supratak, Akara Kraisangka, Jidapa Mongkolwat, Pattanasak Wang, Jja-Ching Mahidol University Faculty of Information and Communication Technology Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665489126

COVID-19 screening using chest X-rays plays a significant role in the early diagnosis of COVID-19 illness during the ongoing pandemic. Manually identifying this infection from chest X-ray films is a challenging and time-consuming technique due to time restrictions and the competence of radiologists. Also, the manual Covid-19 identification technique is made much more difficult and opaquer by the feature similarity between positive and negative chest X-ray images. Therefore, we propose an automated COVID-19 screening framework that utilizes artificial intelligence techniques with a transfer learning approach for COVID-19 diagnosis using chest X-ray images. Specifically, we employ the transfer learning concept for feature extraction before further processing with modified deep neural networks. Also, Grad-CAM visualization is used for our case study to support the predicted diagnosis. The results of the experiments on the publicly accessible dataset show that the convolutional neural network model, which is simple yet effective, performs significantly better than other deep learning techniques across all metrics, including accuracy, precision, recall, and F-measure. © 2022 IEEE.

关键词： COVID-19

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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DNCP: A Robust Artificial Intelligence Based Alzheimer's Disease Detection Methodology by Using Deep Neural Classification Principle

DNCP: A Robust Artificial Intelligence Based Alzheimer's Dis...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malar, R.Jeya Devanandam, K. Nanammal, V. Kumar, Ch. Hemanth Bonde, A. Afaneh, Suha Kings Engineering College Dept of Computer Science and Engineering Chennai India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Jeppiaar Engineering College Dept of Ece Chennai India Qis College of Engineering and Technology Department of Mca Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of information technology Department of Cybersecurity Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Alzheimer's disease (AD) is a neurodegenerative disorder that causes cognitive decline, memory loss, and ultimately death. Early and accurate detection of AD is crucial for timely intervention and improving patient outcomes. This study proposes DNCP: a robust deep learning (DL) based classification methodology for AD detection. The model integrates advanced preprocessing techniques, such as image registration, intensity normalization, and noise reduction, to enhance the quality of neuroimaging data. A multimodal learning approach is utilized to combine features from specific brain regions of interest (ROI), including the hippocampus, gray matter, and white matter. Comparative analysis was conducted against state-of-the-art models such as CNNs (ResNet, VGG-16, AlexNet), RNNs, and AutoEncoders, demonstrating the superior performance of DNCP. The proposed model achieved an accuracy of 95.7%, sensitivity of 94.1%, and specificity of 96.2%, outperforming all other models. The results show that the DNCP model provides an efficient and reliable solution for early-stage Alzheimer's detection, opening doors for enhanced diagnostic tools in clinical settings. © 2024 IEEE.

关键词： Convolutional neural networks

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Assessing Insider Threats in Ai-Enhanced Cyber Security for Digital Medical Platforms

SSRN

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SSRN 2025年

作者： Manasa, R. Jayanthiladevi, A. Rasmi, A. Murugan, Sivaram Faculty of Computer Applications Marwadi University Gujarat Rajkot India Marwadi University Gujarat Rajkot India Information Science and Engineering RR Institute of Technology Bangalore India Department of Computer Engineering Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas58000 Turkey

technology-fuelled medical premises equipped using AI-promoted suggestions have slowly changed clinical touch through movement-based examination, clinician improvement, and practical efficiency, and they bring all around land mega one-of-a-kind cyber observation, particularly inward threats due to ambitions or inadvertent leads individuals as getting jumbo access needed. Patient privacy, patient record integrity, and system utility can all be compromised by insider threats, which are issues created by people with some knowledge or access to the system. We outline aspects of insider threats, risk factors, and automation defenses against insider risks in AI-augmented cyber, defined within digital healthcare systems, focusing on the interplay between human and AI systems. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Design of Scatterer Configuration for Spectral Optimization of Random Lasers

Design of Scatterer Configuration for Spectral Optimization ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Okamoto, Takashi Kajisa, Kouki Ohshige, Ryo Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan

ISBN: (纸本)9798350350012

The two-dimensional structure of a random gain medium was designed to obtain the specified emission spectrum from a random laser. Structural optimization was performed using a direct binary search method. The simulation results showed that the emitted light can be concentrated within one or more specific wavelength ranges. The effect of the fabrication errors on the optimized laser emission spectrum was also examined. © IEEE 2022.

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Application of VR technology in the process of training engineers 36

Application of VR technology in the process of training engi...

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36th Information Systems Education Conference, ISECON 2023

作者： Salach, Mateusz Dzierwa, Andrzej Belzo, Artur Bolanowski, Marek Paszkiewicz, Andrzej Department of Complex Systems The Faculty of Electrical and Computer Engineering Rzeszow University of Technology Al. Powstańców Warszawy 12 Rzeszów35-959 Poland Department of Manufacturing Processes and Production Engineering The Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology Al. Powstańców Warszawy 12 Rzeszów35-959 Poland

The paper presents a didactic application created in virtual reality to visualize a processes of milling and turning inside CNC machines with pre-modeled raw materials. The paper focuses on preparing and working with 3D models and configuring the VR app. The virtual reality application and classes are part of the idea of Education 4.0 – the new and future method of learning in schools. As a result of the work carried out, the possibilities of using VR tools in the process of training engineers were identified, as well as the areas that should be taken into account in the preparation of this class of solutions. The developed solution was tested in a group of students in order to assess the usefulness of the proposed solution and determine the directions for further work. © 2023 Proceedings of the Information Systems Education Conference, ISECON. All rights reserved.

关键词： 3D modeling

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Customer Segmentation Analysis using K-means Algorithm with Elbow method and Dendrogram

Customer Segmentation Analysis using K-means Algorithm with ...

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Advances in Science and technology (ICAST), International Conference on

作者： Darshan Anil Jethva Siya Milind Khamkar Anish Anand Pachchhapur Snehal Kulkarni Department of Computer Engineering St Francis Institute of Technology Mumbai India Faculty of Computer Engineering St Francis Institute of Technology Mumbai India

In today’s competitive business environment, it is crucial to do marketing effectively based on the customer buying habits and requirements. By analyzing the buying habits of customers, companies can enhance customer’s shopping experiences. The analysis can be done with the help of a method called customer segmentation. It is the process of grouping customers into different clusters based on similar characteristics. In this paper, we have done customer segmentation using the k-means algorithm which is an unsupervised learning method and it focuses on grouping the data points into clusters where each point resides in a cluster with the nearest centroid. The accuracy for the optimal number of clusters is verified using a dendrogram where the number of clusters is equal to the number of points obtained by drawing a imaginary horizontal line bisecting through the most extended vertical line of the graph. This analysis gives insights based on customer buying habits which helps companies build marketing strategies for the targeted audience that will improve client retention and overall revenue of business.

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