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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Halder, Swapna Majumder, Saibal Biswas, Arindam Mandal, Bijoy Kumar Peng, Sheng-Lung Centre for IOT and AI Integration with Education–Industry–Agriculture Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology West Bengal Kolkata India Dr. B.C. Roy Engineering College West Bengal Durgapur India School of Mines and Metallurgy Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering NSHM Knowledge Campus West Bengal Durgapur India Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan32462 Taiwan

ISBN: (纸本)9789819936557

Being a network optimization problem, the shortest path problem (SPP) is considered as one of the popular and frequently encountered optimization problems to address real-world problems. In this paper, we investigate a multi-objective SPP under the paradigm of fuzzy set theory. The proposed problem minimizes both cost and time of a given network, where every edges are associated with cost and time parameters represented as fuzzy triangular numbers to incorporate uncertainty involved with real-world phenomenon. Here, we have modeled a fuzzy chance-constrained model of multi-objective SPP and eventually solved the deterministic transformation of the model using weighted sum method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy set theory

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The Future of Wire Arc Additive Manufacturing Comprehensive Strategies for Improved Close Loop Monitoring and Control

The Future of Wire Arc Additive Manufacturing Comprehensive ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nijhawan, Ginni Lalitha, G. Asha, V. Suresh, V.J. Praveen Abdulzahraa, Zahraa H. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article describes a series of approaches that improve wire are additive manufacturing (WAAM) for manufacturing many metal items. ATM, MCA 2, ALTC 2, and RSM are the five techniques in the framework. Ablation research examined each approach and how they work together to improve WAAM accuracy, distortion, and material compatibility. The thorough performance comparison, including photographs, indicates that WAAM is better in deposition rate, surface quality, clarity, material diversity, cost-effectiveness, and scalability. The integrated framework enables dynamic modifications, tracking, and iterative improvements. This makes WAAM the optimal additive manufacturing process for many applications. The data indicates how significant each strategy is and how they work together to improve WAAM. This study illuminates sophisticated additive manufacturing technologies and indicates that WAAM is a feasible and flexible alternative to large metal component manufacturing. © 2024 IEEE.

关键词： Wire

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Towards improving detection performance for malware with a correntropy-based deep learning method

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Digital Communications and Networks 2021年第4期7卷 570-579页

作者： Xiong Luo Jianyuan Li Weiping Wang Yang Gao Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China China Information Technology Security Evaluation Center Beijing100085China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT ***,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so ***,there are also some *** example,considering the noise and outliers in the existing datasets of malware,some methods are not robust ***,the accuracy of malware classification still needs to be *** at this issue,we propose a novel method that combines the correntropy and the deep learning *** our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of *** present the detailed design process of our ***,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

关键词： Malware detection Mixture correntropy Deep learning Convolutional neural network(CNN)

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A Deep Learning Approach for Detecting Malware Using Autoencoder

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu Madhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, R. Veeraiah, Duggineni Ramesh, Eluri Lakshmi, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

Research in the field of malware detection is currently leaning toward methods based on artificial intelligence algorithms due to the increasing limitations of traditional detection methods and the increasing accuracy of these methods. This study therefore introduces a novel approach to malware detection. This method integrates a deep learning model's autoencoder network with a grayscale graphic depiction of malware. By looking at the autoencoder's reconstruction error, we may assess if the grey-scale picture approach is feasible to use for malware detection. Additionally, it differentiates between malicious and safe software by utilizing the autoencoder's dimensionality reduction capabilities. Using the Android dataset, our newly introduced detection model attained a 95% accuracy and maintained a consistently high F-score of approximately 95%. This outperformed more conventional methods of machine learning detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Malware

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Don’t Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users

arXiv

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arXiv 2025年

作者： Zhang, Mengxi Liu, Huaxiao Zhou, Yuheng Chen, Chunyang Huang, Pei Zhao, Jian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Computer Science Technical University of Munich Weipertstr.8-10 Heilbronn74076 Germany Department of Computer Science Stanford University California9025 United States Department of Computer Science Changchun University Changchun1340012 China

Mobile applications (apps) are integral to our daily lives, offering diverse services and functionalities. They enable sighted users to access information coherently in an extremely convenient manner. However, it remains unclear if visually impaired users, who rely solely on the screen readers (e.g., Talkback) to navigate and access app information, can do so in the correct and reasonable order. This may result in significant information bias and operational errors. Furthermore, in our preliminary exploration, we explained and clarified that the navigation sequence-related issues encountered by visually impaired users could be categorized into two types: unintuitive navigation sequence and unapparent focus switching. Considering these issues, in this work, we proposed a method named RGNF (Re-draw GUI Navigation Flow). It aimed to enhance the understandability and coherence of accessing the content of each component within the Graphical User Interface (GUI), together with assisting developers in creating well-designed GUI navigation flow (GNF). This method was inspired by the characteristics identified in our preliminary study, where visually impaired users expected navigation to be associated with close position and similar shape of GUI components that were read consecutively. Thus, our method relied on the principles derived from the Gestalt psychological model, aiming to group GUI components into different regions according to the laws of proximity and similarity, thereby redrawing the GNFs. To evaluate the effectiveness of our method, we calculated sequence similarity values before and after redrawing the GNF, and further employed the tools proposed by Alotaibi et al. to measure the reachability of GUI components. Our results demonstrated a substantial improvement in similarity (0.921) compared to the baseline (0.624), together with the reachability (90.31%) compared to the baseline GNF (74.35%). Furthermore, a qualitative user study revealed that our method had

关键词： Graphical user interfaces

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Business Process Automation using Robotic Process Automation (RPA) and AI Algorithm's on Various Tasks 8

Business Process Automation using Robotic Process Automation...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Dandale, M.N. Mazharunnisa Daniel, D. J. Joel Devadass Priya, R. Sathya Walid, Md. Abul Ala Thulasimani, T. Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities Maharashtra Nagpur India Koneru Lakshmaiya Educational Foundation Kl University Kl Business School Vijayawada Andhra Pradesh Vaddeswaram India VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Knowledge Institute of Technology Department of Cse Tamilnadu Salem India Department of Computer Science and Engineering Bangladesh Bannari Amman Institute of Technology Mathematics Tamilnadu Erode India

ISBN: (纸本)9798350396638

Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous researches are less efficient, slowly working algorithms when classification is performed in a large set of databases. The existing methods could be doing better while comparing the error factors, and in the cross-verification process, they have made inappropriate results, leading to wrong classifications. Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous research on email automation and invoice process automation have needed to improve classification model efficiency and they have less efficient, slowly working algorithms when doing classification in a large set of databases. In this work, the Random Forest algorithm is used for classification, and the Quest method is used to segment texts in emails and invoices, both of which can be automated more effectively. The results of existing categorization algorithms have been less than ideal, especially when used to huge datasets, and are often completely inaccurate. The suggested method outperforms previous ML/AI approaches because it produces highly accurate outcomes with little resource investment. There are a number of benefits to utilizing RPA with AI, such as cost reduction, increased output, and streamlined operations. The advantages of this automation, challenges that must be met, and potential answers to those questions are discussed in this study. © 2023 IEEE.

关键词： Support vector machines

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ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics 10th

ARCH-COMP23 Category Report: Continuous and Hybrid Systems w...

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10th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2023

作者： Geretti, Luca dit Sandretto, Julien Alexandre Althoff, Matthias Benet, Luis Collins, Pieter Forets, Marcelo Ivanova, Elena Li, Yangge Mitra, Sayan Mitsch, Stefan Schilling, Christian Wetzlinger, Mark Zhuang, Daniel Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Universidad de la Repúblic Montevideo Uruguay Computer Science Department Carnegie Mellon University PittsburghPA United States Aalborg University Aalborg Denmark University of Illinois Urbana-Champaign United States

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2023. This year, 6 tools participated: Ariadne, CORA, DynIbex, JuliaReach, KeYmaera X and Verse (in alphabetic order). These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2023, EasyChair. All rights reserved.

关键词： Hybrid systems

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Performance analysis of machine learning techniques for glaucoma detection based on textural and intensity features

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International Journal of Innovative Computing and Applications 2020年第4期11卷 216-230页

作者： Singh, Law Kumar Garg, Hitendra Pooja Khanna, Munish Department of Computer Science and Engineering School of Engineering and Technology Sharda University Knowledge Park III Greater Noida India Department of Computer Science and Engineering Hindustan College of Science and Technology Mathura India Department of Computer Engineering and Applications GLA University Mathura India

Glaucoma is one of the significant causes of blindness, which covers about 15% to 20% of the total population, so early-stage detection is essential. The proposed methods apply fast fuzzy C-means approach to determine optics-cup-to-disc ratio (CDR) followed by textural based and intensity-based features of the eye. Textural features include local binary pattern, grey-level co-occurrence matrix, and Harlick features, whereas intensity-based features include colour moment and skewness. Machine learning techniques are applied to extract entropy, horizontal, vertical diameter of optics disc/cup, textural based and intensity-based features that classify the image as glaucoma or healthy image and obtained ophthalmologists verify results. Own dataset of 298 retinal images consisting of both healthy and glaucomatous images is used for experimental analysis. In the proposed method, various machine learning techniques like support vector machine (SVM), K-nearest neighbour, and naive Bayes, report 95.5%, 93.3%, and 94.35% accuracy, respectively. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Support vector machines

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series 41

An Empirical Examination of Balancing Strategy for Counterfa...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)

关键词： Time series analysis

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Internet of Things Technology for Photovoltaic Smart Sprinkler Systems and Its Analysis

Internet of Things Technology for Photovoltaic Smart Sprinkl...

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2023 SPE Nigeria Annual International Conference and Exhibition, NAIC 2023

作者： Chinonyelum, Ejimuda Kingsley, Okoli Lilian, Uzodiagu Juliet, Olusola Department of Mechanical Engineering Texas A & M University United States Controls Lab Texas A & M University United States Department of Computer Science and Knowledge Discovery Saint Petersburg Electrotechnical University LETI Saint Petersburg Russia Artificial Intelligence Lab University of Nigeria Nsukka Nigeria Department of Electrical and Electronics Engineering Bells University of Technology Ogun State Ota Nigeria Department of Electrical and Electronics Engineering Afe Babalola University Ekiti State Ado-Ekiti Nigeria

ISBN: (纸本)9781959025177

The advancement of technology has brought about ease of life with the help of technology automation which can be applied virtually in all aspects of living. With the emergence of internet of things, prototyping real state systems has become less expensive and easy to embark on thus creating platforms for development in the oil & gas sector. This research aims to analyze, design, and develop an effortless system powered via solar energy for the control of a sprinkler system and liquid flow void of human intervention. The system was programmed using an Arduino board and other hardware at an adjustable set time with Arduino software. The research is geared at reducing human efforts in irrigation systems, to save cost and time in sprinkling liquid on any system. The use of some weather parameters like humidity, temperature, and moisture sensors;Arduino kits and GSM module constitutes the smart irrigation system setup. The system is automated to feed the necessary quantity of liquid to any given surface based on design parameters with the moisture sensor programmed within 0 to 1023 (0-100%) range. The system adapted a method of pump-start at 40% moisture reading. Various analysis of the research findings has been presented in the work with applicability in remote areas and oil fields void of human intervention. Copyright © 2023 Society of Petroleum Engineers.

关键词： Automation

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