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IEEE International Technology Management Conference

作者： Kerem Nazlıel Kerem Kayabay Mert Onuralp Gökalp Ebru Gökalp Aydın P. Erhan Eren Altan Koçyiğit Graduate School of Informatics Middle East Technical University Ankara Turkey Converged Computing HLRS University of Stuttgart Stuttgart Germany Data Analytics Center TÜPRAŞ Ankara Turkey Department of Computer Engineering Hacettepe University Ankara Turkey

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

The rapid advancements in Data Science and Artificial Intelligence require organizations to create strategic roadmaps that align with both technological trends and business objectives. This paper introduces a data-driven approach for developing such strategies, integrating Digital Maturity Assessment, Multivocal Literature Review, Topic Modeling, and SWOT analysis within the Data Science Roadmapping (DSR) framework. The proposed approach aims to enhance the accuracy and relevance of roadmap development by combining multiple tools for comprehensive trend identification and organizational capability assessment. Ongoing validation through case studies will assess its practical application and effectiveness.

关键词： Analytical models Systematics Technology management Data science Strategic planning Market research Data models Reproducibility of results Artificial intelligence Systematic literature review

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Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

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computers, Materials & Continua 2021年第8期68卷 1575-1593页

作者： Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer Department Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for Artificial Intelligence Technology(CAIT) Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer Sciences Universiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmmanJordan

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

关键词： Artificial bee colony automatic clustering natural images validity index number of clusters

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Customizing and Deploying Data Science Roadmapping: A Case Study of an Oil and Gas Company

Customizing and Deploying Data Science Roadmapping: A Case S...

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2022 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2022

作者： Kayabay, Kerem Gokalp, Mert Onuralp Gokalp, Ebru Alagoz, Serhat Murat Eren, P. Erhan Kocyigit, Altan Data Informatics Middle East Technical University Network Technologies Department TÜBITAK ULAKBIM Ankara Turkey Data Analytics Center TÜPRAŞ Ankara Turkey Hacettepe University Computer Engineering Department Ankara Turkey Information Systems Middle East Technical University Ankara Turkey

ISBN: (纸本)9781665470278

Many organizations still face challenges leveraging data science in production and need strategic planning for organization-wide data science efforts and assets. Data Science Roadmapping (DSR) customizes the widely used Technology Roadmapping to facilitate such planning in organizations, aligning market, business, data, technology, and organization perspectives. This paper employs the action research method to customize and apply DSR in an oil and gas company. The approach mostly met the expectations regarding objectives and outcomes, the first step toward generalizability. The case study provides researchers and practitioners insights into roadmapping customization, remote and agile roadmap development process, and strategic planning for technology adoption in a large organization. Lastly, we identify the future research directions using the recent roadmapping literature. © 2022 IEEE.

关键词： Big data

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A novel approach combined transfer learning and deep learning to predict TMB from histology image

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Pattern Recognition Letters 2020年 135卷 244-248页

作者： Wang, Liansheng Jiao, Yudi Qiao, Ying Zeng, Nianyin Yu, Rongshan Department of Computer Science School of Informatics Xiamen University Fujian361005 China School of Informatics Xiamen University Fujian361005 China Department of Instrumental and Electrical Engineering Xiamen University Fujian361005 China

Tumor Mutation Burden(TMB) is a quantifiable clinical indicator that can be used to predict the responses to immunotherapy of a range of tumors. However, the current DNA sequencing-based TMB measurement method represented by Whole Exome Sequencing (WES) is expensive and time-consuming, which limits its utilization in clinical practice. In this paper, we design a method through deep learning in order to predict TMB from available H&E stained whole slide images of gastrointestinal cancer. Experimental results demonstrate that our approach is capable of distinguishing high and low TMB with an AUC higher than 0.75. We further performed post-processing to improve the accuracy on both test sets to above 0.7 (0.71 accuracy for TMB-STAD and 0.77 accuracy for TMB-COAD-DX). Furthermore, the predicted low and high TMB patients with gastric and colon cancer have different survival rates, with p values of 0.348 and 0.8113, respectively, which indicates that our study is potentially helpful for practical treatment. © 2020 Elsevier B.V.

关键词： Tumors

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Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

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International Conference on Image Information Processing

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the “APTOS 2019 Blindness Detection” dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model’s performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment.

关键词： Deep learning Training Diabetic retinopathy Visual impairment Transfer learning Blindness Information processing

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques

State-based Sandbox Tool for Distributed Malware Detection w...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Pavlo Rehida George Markowsky Anatoliy Sachenko Oleg Savenko Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi Ukraine Department of Software Engineering and Game Development Kennesaw State University Marietta USA Department of Informatics Kazimierz Pulaski University of Technology and Humanities Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described.

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IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning

arXiv

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arXiv 2023年

作者： Musyaffa, Muhammad Salman Ikrar Yudistira, Novanto Rahman, Muhammad Arif Batoro, Jati Informatics Engineering Faculty of Computer Science Brawijaya University East Java Malang65145 Indonesia Depatement of Biology Faculty of Mathematocs and Natural Science Brawijaya University East Java Malang65145 Indonesia

The rich diversity of herbal plants in Indonesia holds immense potential as alternative resources for traditional healing and ethnobotanical practices. However, the dwindling recognition of herbal plants due to modernization poses a significant challenge in preserving this valuable heritage. The accurate identification of these plants is crucial for the continuity of traditional practices and the utilization of their nutritional benefits. Nevertheless, the manual identification of herbal plants remains a time-consuming task, demanding expert knowledge and meticulous examination of plant characteristics. In response, the application of computer vision emerges as a promising solution to facilitate the efficient identification of herbal plants. This research addresses the task of classifying Indonesian herbal plants through the implementation of transfer learning of Convolutional Neural Networks (CNN). To support our study, we curated an extensive dataset of herbal plant images from Indonesia with careful manual selection. Subsequently, we conducted rigorous data preprocessing, and classification utilizing transfer learning methodologies with five distinct models: ResNet, DenseNet, VGG, ConvNeXt, and Swin Transformer. Our comprehensive analysis revealed that ConvNeXt achieved the highest accuracy, standing at an impressive 92.5%. Additionally, we conducted testing using a scratch model, resulting in an accuracy of 53.9%. The experimental setup featured essential hyperparameters, including the ExponentialLR scheduler with a gamma value of 0.9, a learning rate of 0.001, the Cross-Entropy Loss function, the Adam optimizer, and a training epoch count of 50. This study’s outcomes offer valuable insights and practical implications for the automated identification of Indonesian medicinal plants, contributing not only to the preservation of ethnobotanical knowledge but also to the enhancement of agricultural practices through the cultivation of these valuable resources. The In

关键词： Convolutional neural networks

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Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease 1

Implementation Learning Vector Quantization Using Neural Net...

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1st Lekantara Annual Conference on engineering and Information Technology, LiTE 2021

作者： Samsir, Samsir Daulay, Nelly Khairani Harahap, Syaiful Zuhri Zalmi, Wahyuni Fithratul Sari, Afni Nia Nasution, Fitri Aini Watrianthos, Ronal Department of Informatics Engineering Universitas Al Washliyah Labuhanbatu Indonesia Department of Computer System Universitas Bina Insan Indonesia Department of Information System Universitas Labuhanbatu Indonesia Department of Information System Universitas Putra Indonesia Padang Indonesia Department of Software Engineer Universitas Dharmawangsa Medan Indonesia

This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors were fever, tiredness, nausea, breathing pain, sore throat, hearing loss, allergies, chills and sweating, and thick and transparent mucus. The factors studied were ear canal infections, pharyngitis of the neck, throat, nose, and sinusitis. The findings revealed that ten neurons with an objective value of 0.01 in the learning rate range of 0.01 - 0.05 resulted in categorizing snoring, nose, and ear disorders, including the input layer. The MATLAB program is utilized in this approach, with an average accuracy of 67 per cent and a mean square error of 0.2. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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A Model for Intelligible Interaction Between Agents That Predict and Explain

arXiv

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arXiv 2023年

作者： Baskar, A. Srinivasan, Ashwin Bain, Michael Coiera, Enrico Dept. of Computer Science Information Systems BITS Pilani Goa Campus India Dept. of Computer Science Information Systems and APPCAIR BITS Pilani Goa Campus India School of Computer Science Engineering University of New South Wales Australia Centre for Health Informatics Macquarie University Sydney Australia

Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents. However, relatively little attention has been paid to the interaction between people and ML systems. In this paper we view interaction between humans and ML systems within the broader context of communication between agents capable of prediction and explanation. We formalise the interaction model by taking agents to be automata with some special characteristics and define a protocol for communication between such agents. We define One- and Two-Way Intelligibility as properties that emerge at run-time by execution of the protocol. The formalisation allows us to identify conditions under which run-time sequences are bounded, and identify conditions under which the protocol can correctly implement an axiomatic specification of intelligible interaction between a human and an ML system. We also demonstrate using the formal model to: (a) identify instances of One- and Two-Way Intelligibility in literature reports on humans interacting with ML systems providing logic-based explanations, as is done in Inductive Logic Programming (ILP);and (b) map interactions between humans and machines in an elaborate natural-language based dialogue-model to One- or Two-Way Intelligible interactions in the formal model. © 2023, CC BY.

关键词： Inductive logic programming (ILP)

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Reliable Indoor Localization in Multi-Building Environments: Leveraging Environment-Invariant and Position-Related Features

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IEEE Internet of Things Journal 2025年

作者： Long, Wenhan Wen, Xinlong Liu, Hao Li, Mengyao Li, Songquan Wu, Yonghui Chen, Fuxiang Liu, Lu Zhu, Rongbo Huazhong Agricultural University College of Informatics Wuhan430070 China Ministry of Education Engineering Research Center of Intelligent Technology for Agriculture Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. © 2014 IEEE.

关键词： Floors

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