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2019 International Conference of Science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Rusdi, Jack Febrian Salam, Sazilah Abu, Nur Azman Sunaryo, Budi Agustina, Nova Gusdevi, Harya Hermansah Hadhiwibowo, Ari Heryati, Tuty Hardi, Richki Taufiq, Rohmat Jufri, Muhammad Taher Sudarsono, Ignatius Utomo, Eko Wahyu Kodong, Frans Richard Fannya, Puteri Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Electrical Engineering Andalas University Padang Indonesia Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik Dan Sistem Informasi Universitas Yapis Papua Indonesia Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia Department of Informatics Engineering UPN Veteran Yogyakarta Yogyakarta Indonesia Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room - the results of this study used as a hospital facility maintenance reporting system. © Published under licence by IOP Publishing Ltd.

关键词： Hospitals

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A machine learning approach for skin disease detection and classification using image segmentation

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Healthcare Analytics 2022年 2卷 100122-100122页

作者： Ahammed, Mostafiz Mamun, Md. Al Uddin, Mohammad Shorif Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Public Health and Informatics Jahangirnagar University Dhaka Bangladesh

Skin diseases are common health problems around the world. The perils of the infections are invisible, which cause physical health distress as well as initiate mental depression. In addition, it sometimes leads to skin cancer in severe cases. Subsequently, diagnosing skin diseases from clinical images is one of the foremost challenging tasks in medical image analysis. Moreover, when performed manually by medical experts, diagnosing skin diseases is time-intensive and subjective. As a result, both patients and dermatologists require automatic skin disease prediction, which makes the treatments plan faster. In this work, we introduce a digital hair removal technique based on morphological filtering such as Black-Hat transformation and inpainting algorithm and then apply Gaussian filtering to de-blur or denoise the images. In addition, we apply the automatic Grabcut segmentation technique to segment out the affected lesions. For extracting underlying input patterns from the skin images, we apply the Gray Level Co-occurrence Matrix (GLCM) and statistical features techniques. Three computationally efficient machine learning techniques, Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) classifiers are applied using the extracted features for effectively classifying the skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using two standard datasets ISIC 2019 challenge and HAM10000. SVM performs slightly better than the other two classifiers. We have also compared our work with state-of-the-art methods. © 2022 The Author(s)

关键词： Decision Tree Image Segmentation K Nearest Neighbor (KNN) Machine Learning Skin Disease Support Vector Machine

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Analysis of optimized response time in a new disaster management model by applying metaheuristic and exact methods

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Smart and Resilient Transportation 2022年第1期4卷 22-42页

作者： Armin Mahmoodi Milad Jasemi Zergani Leila Hashemi Richard Millar Department of Industrial Engineering Islamic Azad University Masjed Soleyman Iran Department of Computer Informatics Stephens College of Business University of Montevallo Montevallo Alabama USA Department of Industrial Management Islamic Azad University Tehran Iran Department of Engineering Management and Systems Engineering George Washington University Washington DC District of Columbia USA

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

关键词： Humanitarian logistics Drones delivery Operation research in disaster relief Additive manufacturing Cuckoo optimization algorithm Genetic algorithm

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Cybersecurity Revolution via Large Language Models and Explainable AI

Cybersecurity Revolution via Large Language Models and Expla...

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Security of Information and Networks (SIN), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Anuam Ihsan Nidal A. Al-Dmour College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Engineering College of Engineering Mutah University Jordan

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted; hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity.

关键词： Measurement Machine learning algorithms Explainable AI Large language models Predictive models Transformers Threat assessment computer security computer crime Faces

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Linear-time computation of generalized minimal absent words for multiple strings

arXiv

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arXiv 2023年

作者： Okabe, Kouta Mieno, Takuya Nakashima, Yuto Inenaga, Shunsuke Bannai, Hideo Department of Information Science and Technology Kyushu University Japan Department of Computer and Network Engineering University of Electro-Communications Japan Department of Informatics Kyushu University Japan M&D Data Science Center Tokyo Medical and Dental University Japan

A string w is called a minimal absent word (MAW) for a string S if w does not occur as a substring in S and all proper substrings of w occur in S. MAWs are well-studied combinatorial string objects that have potential applications in areas including bioinformatics, musicology, and data compression. In this paper, we generalize the notion of MAWs to a set S = {S1, ..., Sk} of multiple strings. We first describe our solution to the case of k = 2 strings, and show how to compute the set M of MAWs in optimal O(n + |M|) time and with O(n) working space, where n denotes the total length of the strings in S. We then move on to the general case of k > 2 strings, and show how to compute the set M of MAWs in O(n⌈k/log n⌉+ |M|) time and with O(n(k + log n)) bits of working space, in the word RAM model with machine word size ω = log n. The latter algorithm runs in optimal O(n + |M|) time for k = O(log n). © 2023, CC BY.

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Programmable large-scale simulation of lattices with photonic synthetic frequency dimensions

Programmable large-scale simulation of lattices with photoni...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Senanian, Alen Wright, Logan G. Wade, Peter F. Doyle, Hannah K. McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

We present a programmable analog simulator with up to 105 lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary lattic...

ISBN: (纸本)9781957171258

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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling

arXiv

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arXiv 2023年

作者： Shahrokh, Fahime Ghadiri, Nasser Samani, Rasoul Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, and electronic health records. The conventional approaches for biomedical NER mainly use traditional machine learning techniques, such as Conditional Random Fields and Support Vector Machines or deep learning-based models like Recurrent Neural Networks and Convolutional Neural Networks. Recently, Transformer-based models, including BERT, have been used in the domain of biomedical NER and have demonstrated remarkable results. However, these models are often based on word-level embeddings, limiting their ability to capture character-level information, which is effective in biomedical NER due to the high variability and complexity of biomedical texts. To address these limitations, this paper proposes a hybrid approach that integrates the strengths of multiple models. In this paper, we proposed an approach that leverages fine-tuned BERT to provide contextualized word embeddings, a pretrained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. In addition, also we propose an enhanced labelling method as part of pre-processing to enhance the identification of the entity's beginning word and thus improve the identification of multi-word entities, a common challenge in biomedical NER. By integrating these models and the pre-processing method, our proposed model effectively captures both contextual information and detailed character-level information. We evaluated our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11. These results illustrate the proficiency of our proposed model in performing biomedical Named Entity *** Codes 68T50, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

Reconfigurable Intelligent Computational Surfaces for MEC-As...

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IEEE Conference on Wireless Communications and Networking

作者： Bo Yang Xueyao Zhang Zhiwen Yu Xuelin Cao Chongwen Huang George C. Alexandropoulos Yan Zhang Mérouane Debbah Chau Yuen School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Cyber Engineering Xidian University Xi'an Shaanxi China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo Norway Center for 6G Technology the Khalifa University of Science and Technology Abu Dhabi UAE School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

关键词： Simulation Vehicle-to-infrastructure Vehicular ad hoc networks Transforms Safety Servers Task analysis

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Prediction of Consumer Purchase Intention on Green Product

Prediction of Consumer Purchase Intention on Green Product

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2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021

作者： Hariyadi, Guruh Taufan Purwanto Isnanto, R Rizal Wibowo, Adi Warsito, Budi Universitas Diponegoro Doctoral Program of Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Engineerig Department of Computer Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Departement of Informatics Semarang Indonesia

ISBN: (纸本)9781665428040

Education to the public on green products has been widely carried out. Consumers who buy green products can be profiled by segmenting green consumers. In green marketing, consumer education on sustainable consumption is increasingly needed in recent years due to the problem of environmental pollution which poses a serious threat to the world population. Concern for the environment is an important topic in most countries, and individuals also study environmental issues. Individuals or groups of individuals who are 'ecologically aware' are also referred to as 'green consumers.' Such consumers are considered to have more concern for the environment. This study examined the predictions on the effect of the Green Perceived Value variables consisting of functional, conditional, and social values on the purchase intention of green consumers. It also tested the comparisons with other classification models. The models used in this research were Random Forest, Decision Tree, and Neural Network.. © 2021 IEEE.

关键词： Consumer behavior

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