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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimized Image Dehazing using Dark Channel Prior with Minimum Fusion and Improved Atmospheric Light Handling

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IEEE Access 2025年 13卷 100720-100730页

作者： Hussain, Ahmad Iqbal, Mansoor Aslam, Sheraz Alahmari, Saad Alkharashi, Abdulwahab University of Wah Department of Computer Science Cantt Wah Pakistan CTL eurocollege Department of Computer Science Limassol 3077 Cyprus Cyprus University of Technology Department of Electrical Engineering Computer Engineering and Informatics Limassol 3036 Cyprus Northern Border University Applied College Department of Computer Science Arar Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Saudi Arabia

Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments. © 2013 IEEE.

关键词： Atmospheric Light Estimation Dark Channel Prior Image Dehazing Minimum Fusion Technique Visual Clarity

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Factors influencing Romanian teachers' choice of IT training to avoid issues raised by online education: A data mining approach 17

Factors influencing Romanian teachers' choice of IT training...

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17th International Conference on Development and Application Systems, DAS 2024

作者： Simionescu, Corina Bărîlă, Adina-Luminița Danubianu, Mirela Grădinaru, Bogdănel Constantin Doctoral School of Applied Sciences and Engineering Computers and information technology Ștefan cel Mare University Suceava Romania Faculty of Electrical Engineering and Computer Science Ștefan cel Mare University Suceava Romania

ISBN: (纸本)9798350349290

Raising the students' academic performance and improving the overall quality of education is an important goal of our society. With the exploding development of information technologies, Educational Data Mining has become a growing activity that uses the essence of Data Mining concepts to help institutions learn useful information on various academic issues. Romanian schools are facing several problems and inequalities, many of which were accentuated during the Covid-19 pandemic, when, due to restrictions on physical proximity, classes were held entirely or partly online. This paper aims to presents an experimental study on modelling teachers' attitudes towards attending IT professional development courses to deliver quality online lessons. A real data set collected through a questionnaire answered by 956 teachers is used. The final goal was that, after the best-performing model was built, the features that are significant in this approach would be analyzed. These features are influential factors in teachers' choices. We initially considered four classification techniques, after which, we refined the optimization process for Random Forest, which proved, from the first experiments, the best performance. ©2024 IEEE.

关键词： Data mining

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Thermal Error Modelling using Features selected from Particle Swarm Optimization 5

Thermal Error Modelling using Features selected from Particl...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Ajith Kumar, B. Nayana, B.R. Shashank, P. M. S. Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Bengaluru India Ace Designers Ltd. Research and Technology Development Group Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528539

computer Numerical Control (CNC) machines are employed in various industrial manufacturing processes to produce precise components. The CNCs are prone to thermal deviations from the heat generated during machining. Axis motors, rotary bearings, and spindle motors are the main sources of heat. In this context, temperature sensitive areas (TSAs) apply to these locations. The accuracy of the machining process is directly affected by the heat-induced deviation between the tool and the workpiece. The objective is to minimize these deviations between the tool and the workpiece. The most challenging aspect of creating thermal error compensation models is figuring out which TSAs to use. This work proposes employing particle swarm optimization (PSO), a computational intelligence (CI) technique, to choose significant features. First, information about the machine's temperature and thermal deviation is collected. Second, the outliers are eliminated by cleaning the data. Following this, PSO optimizes the error band to identify which TSAs are significant. Advanced machine learning (ML) models like polynomial regression (PR) and random forest (RF) regression are trained utilizing these TSAs as features. The proposed approach is compared with matching models developed with features selected through the brute force (BF) approach. While PSO-RF outperformed BF-RF in providing 16.60% error reduction with different features, PSO-PR and BF-PR provided 47.12% error band reduction with similar features. Based on the findings, it can be concluded that PSO-PR can be used on the machine and that PSO outperforms BF in terms of identifying the ideal subset of influential features. © 2024 IEEE.

关键词： Error compensation

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Evaluating Interactive Concept Maps Produced from E-Portfolios 26

Evaluating Interactive Concept Maps Produced from E-Portfoli...

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26th International Symposium on Multimedia, ISM 2024

作者： Gantikow, Alexander Isking, Andreas Muller, Wolfgang Libbrecht, Paul Rebholz, Sandra University of Education Weingarten Media Education and Visualization Weingarten Germany Iu International University of Applied Science It and Technology Erfurt Germany Ostbayerische Technische Hochschule Amberg-Weiden Electrical Engineering Media and Computer Science Amberg-Weiden Germany

ISBN: (纸本)9798331511111

Natural Language Processing opens new ways to analyze digital works. We are interested in the assessment of e-portfolios which are digital learning logs. Through analysis techniques such as the recognition of topics of a paragraph it is is possible to visualize the topic coverage and content depth. Such visualizations can be useful in the e-portfolio creation and review processes. We have applied these technologies to develop interactive concept maps for exam-relevant e-portfolios and have used them in a real-life setting at university. The experiment has shown that the interactive concept maps have motivated the students and that they have the potential to be useful in the writing process if they are well integrated into the workflow. © 2024 IEEE.

关键词： Financial data processing

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IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network

IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zheng, Yisen Huang, Guoheng Zhang, Feng Cheng, Lianglun Yuan, Xiaochen Zhong, Guo Luo, Shenghong School of Computer Science and Technology Guangdong University of Technology Guangzhou China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781665410205

In recent years, many Lesion segmentation (LS) models based on UNet have been proposed. However, existing researches rarely consider the influence of illumination change leads to the weak boundary area. Such as melanomas and polyps, the demarcation of the boundary between the diseased area and the surrounding tissue remains particularly challenging. To overcome these challenges, we propose an IlluminationAdaptive Multi-scale Lesion Segmentation Network (IAMS-Net). In IAMS-Net, we integrate Illumination-Adaptive MultiStream Attention (IAMA) and Contour Perception Module (CPM). In the decoding stage, the IAMA is used as a bridge between the encoder and the decoder to solve the adverse effects of illumination changes on the segmentation of weak boundary lesions. In order to further enhance the boundary features lost due to illumination change in the low-contrast lesion area, we introduce the CPM to improve the perception of the integrity of the lesion area. Subsequently, we performed comparison and ablation experiments using the publicly available ISIC2018 dataset and the individually collected data set BoreIllumination(BI). © 2024 IEEE.

关键词： Signal encoding

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Advanced Cryptocurrency Price Prediction: Incorporating Time-Reversed ARIMA and BVIC 19

Advanced Cryptocurrency Price Prediction: Incorporating Time...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Luc, Minh Tuan Senkerik, Roman Dang, Tran Khanh Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Republic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Viet Nam Institute for Experiential Technology Van Lang University Ho Chi Minh city Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331507817

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated Information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements. © 2025 IEEE.

关键词： Prediction models

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Social Distancing and Face Mask Detection Using YOLO Object Detection Algorithm 1

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1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Raguraman, Riddhiman Gautham Rajan, T.S. Subbulakshmi, P. Pavithra, L.K. Thirumurthy, Srimadhaven School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Thomas J. Watson College of Engineering and Applied Science Binghamton University State University of New York BinghamtonNY United States

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Due to the COVID-19 pandemic, there has been a huge impact worldwide. The transmission of COVID-19 can be prevented using preventive measures like social distancing and face masks. These measures could slow the spreading and prevent newer ones from occurring. Social distancing can be followed even by those with weaker immune systems or certain medical conditions. With the new normal into play, maintaining distance in social and wearing masks are likely to be followed for the next two years. This paper studies about maintaining distance in social and detection of masks using deep learning techniques. Several object detection models are used for detecting social distance. The inputs used are in the form of images and videos. With this system, the violations can be detected which will reduce the number of cases. In conclusion, the proposed system will be very efficient and can also be used to introduce newer preventive measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Classification of Rice Varieties using Convolution Neural Networks 2

Classification of Rice Varieties using Convolution Neural Ne...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Shoaib Khan, Muhammad Bilal Kamran, Rukshanda Saima, Mahmoud Abu Sohail Irshad, Muhammad Naz, Naila Samar Athar, Atifa Akhuwat College University Kasur Pakistan Universit Malaysia Faculty of Compter Science and Information Technology Sarawak94300 Malaysia United Arab Emirates University College of Science United Arab Emirates Akhuwat College University Department of Informatin Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan CUI Department of Computer Science Lahore Campus Pakistan

ISBN: (纸本)9798350335644

The classification of rice varieties is a crucial task in the agricultural industry, as it helps farmers to identify and manage crops effectively. In recent years, deep learning algorithms have shown promising results in image recognition tasks, and have been applied to agricultural applications to classify crop varieties. This study proposes a deep learning-based CNN approach to classify five variants of rice varieties based on their images. The proposed method utilizes convolutional neural networks (CNNs) to learn the features from rice images and classify them into their respective categories. The dataset used in this study consists of 300 images of five different rice varieties, which are collected from various sources and angles. The experimental results demonstrate that the proposed method achieves high accuracy in classifying the five rice varieties. The accuracy of the classification algorithm is evaluated using different metrics as mentioned in the literature. © 2023 IEEE.

关键词： Crops

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Pre-evaluation of Post-Merger Information System Integration Strategies 15

Pre-evaluation of Post-Merger Information System Integration...

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Forum at 15th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM-Forum 2022

作者： Lace, Ksenija Kirikova, Marite Institute of Applied Computer Systems Faculty of Computer Science and Information Technology Riga Technical University 6A Kipsalas Street RigaLV-1048 Latvia

Mergers and acquisitions (M&A) are frequently used tools for company growth. With M&A, companies can grow bigger and faster by creating a new organisation with more resources to achieve M&A goals. However, there are still ongoing discussions if M&A can achieve its stated goals and assure sustainable performance. One frequently mentioned reason for this is the inability to transform merging organisations. As information systems (IS) play an essential role in organisational architecture, the transformation of IS should take more attention in transformation planning and implementation. There are different possible strategies to transform existing IS;each brings specific value and requires particular circumstances to be realistic and efficient. This research focuses on the decision of a specific IS integration strategy for selected IS by applying a practical approach which evaluates each strategy from several perspectives - contribution to M&A goals, stakeholder support, user satisfaction, cost, risk and time. The process is verified by applying it practical case study. © 2022 Copyright for this paper by its authors.

关键词： Mergers and acquisitions

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