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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： K. Babu Vijayalakshmi. G P. Umadevi Neeraj Patel S. Gopalakrishnan SRM Institute of Science & Technology Kattankulathur ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Computer science and Business systems KPR Institute of Engineering and Technology IES Institute of Technology and Management IES University Bhopal India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Kelambakkam India

Because of the fast changes that climate change is bringing about in glacial environments, real-time monitoring is essential to comprehending and reducing its consequences. In order to observe glacial change in real time, this research study provides a novel method that makes use of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). We start by providing a brief overview of the problem's urgency and the shortcomings of the conventional monitoring techniques. The study then goes on to talk about the main elements of our AI and ML-based system, which include data integration from IoT devices and distant sensors, machine learning algorithms, and analysis of satellite images. Researchers can more accurately understand the intricate dynamics of glacier behaviour thanks to the automated detection of glacial movement, changes in ice thickness, and patterns of meltwater made possible by AI and ML models. As we dig further, the article looks at creating an approachable interface that would make real-time glacier data accessible to scholars, decision-makers, and the general public. It also emphasises how early warning systems may help reduce the risks associated with glaciers and provide guidance for plans aimed at adapting to climate change. This multi-faceted approach, from the simplicity of AI and ML implementation to its intricate data analysis capabilities, in conjunction with IoT, represents a significant advancement in our ability to monitor and respond to glacial changes in real-time.

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

arXiv

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

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IoT Networks: Security Vulnerabilities of Application Layer Protocols

IoT Networks: Security Vulnerabilities of Application Layer ...

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International Conference on Mathematics, Actuarial science, computer science and Statistics (MACS)

作者： Mohit Lalit Sunil Kumar Chawla Arun Kumar Rana Kashif Nisar Tariq Rahim Soomro Muhammad Asghar Khan Computer Science and Engineering department Chandigarh University Gharaun India Computer Science and University Institute of Engineering Chandigarh University Gharuan Mohali Punjab India Electronics and communication Engineering Panipat Institute of Engineering and Technology Samalkha Panipat Haryana India Victorian Institute of Technology (VIT) Adelaide South Australia College of Computer Science and Information Systems Institute of Business Management Karachi Sindh Pakistan

ISBN: (纸本)9781665460729

Enabling objects to work intelligently through various communication technologies is a common phenomenon. Objects become intelligent and are allowed to work autonomously through the internet, and the concept is popularly known as the Internet of Things (IoT). Due to the diversity in application areas of IoT, it has various aspects to be concerned about too. The part researchers are more worried about is transferring data among objects securely. This paper aims to discuss numerous security concerns of application layer protocols and to look into various types of attacks that the application layer might suffer.

关键词： computer science Protocols Information sharing Network security Media Access Protocol Mathematics Energy efficiency

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Application of factor analysis method to support the users acceptance model of ERP systems implementation 4

Application of factor analysis method to support the users a...

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4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020

作者： Sardjono, W. Sudirwan, J. Priatna, W. Putra, G.R. Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Factor analysis

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A short note on the characterization of countable chains with finite big Ramsey spectra

arXiv

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arXiv 2023年

作者： Barbosa, Keegan Dasilva Mašulović, Dragan Nenadov, Rajko Fields Institute for Research in Mathematical Science 222 College St TorontoONM5T 3J1 Canada Department of Mathematics and informatics Faculty of Sciences University of Novi Sad Trg Dositeja Obradovića 3 Novi Sad21000 Serbia School of Computer Science University of Auckland New Zealand

In this short note we confirm the deep structural correspondence between the complexity of a countable scattered chain (= strict linear order) and its big Ramsey combinatorics: we show that a countable scattered chain has finite big Ramsey degrees if and only if it is of finite Hausdorff rank. This also provides a complete characterization of countable chains whose big Ramsey spectra are finite. We expand the notion of big Ramsey spectrum to monomorphic structures and give a sufficient condition for a monomorphic countable structure to have finite big Ramsey spectrum. Copyright © 2023, The Authors. All rights reserved.

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： incremental event log Predictive process monitoring process mining remaining time prediction

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SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases

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computers in Biology and Medicine 2024年 175卷 108503-108503页

作者： Liu, Xiao Du, Peng Dai, Zhiguang Yi, Rumeng Liu, Weifan Wu, Hao Geng, Daoying Liu, Jie School of Computer and Information Technology Beijing Jiaotong University Beijing100089 China Department of Radiology the Second Affiliated Hospital of Xuzhou Medical University Jiangsu221000 China CSSC Systems Engineering Research Institute Beijing100094 China College of Science Beijing Forestry University Beijing100089 China Huashan Hospital Fudan University Shanghai200020 China

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival. © 2024

关键词： Tumors

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Dialog chatbot as an interactive online tool in enhancing ESP vocabulary learning

Saudi Journal of Language Studies

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Saudi Journal of Language Studies 2023年第2期3卷 76-86页

作者： Fawaz Qasem Mukhtar Ghaleb Hassan Saleh Mahdi Ahmed Al Khateeb Hind Al Fadda Department of English College of Sciences and Arts and the Applied College at Al-Namas University of Bisha Bisha Saudi Arabia Department of Information Systems College of Computing and Information Technology University of Bisha Al Namas Saudi Arabia Faculty of Computer Science and Information Technology Sana'a University Sana'a Yemen Department of English Hodeidah University Hodeidah Yemen English Language Centre Taif University Taif Saudi Arabia Department of English Language College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Curriculum and Instructions King Saud University Riyadh Saudi Arabia

Purpose Based on an experimental study on English for Specific Purposes (ESP) students, at the Business department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well. Design/methodology/approach The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive. Findings The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group. Research limitations/implications The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development. Originality/value Examining and exploring whether the use of chatbots play

关键词： Chatbot English for specific purposes (ESP) ESP vocabulary English as second language (ESL) Online platform

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