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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

Journal of Ambient Intelligence and Humanized computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification 4

Mobile Medical Assistant System for Laboratory Report Analys...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Warnakulasuriya, Deshani Dewangi, Tharushi Sewwandi, Navodya Rathnayake, Minoli Kodagoda, Nuwan Suriyawansha, Kushanra Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month's results of the medical lab report of the patient using machine learning. © 2022 IEEE.

关键词： Hospitals

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Smart Children Management Using Data Analytics, Machine Learning and IoT 1st

Smart Children Management Using Data Analytics, Machine Lear...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Faruk, Md Jobair Hossain Adnan, Muhamad Hariz Muhamad College of Computing and Software Engineering Kennesaw State University Marietta United States Computing Department Faculty of Art Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia

ISBN: (纸本)9789811621826

One of the biggest challenges faced by parents is managing the difficult or defiant behavior of children of the twenty-first century. Technology can help to provide support in managing children’s activities and helping parents to be in control. Recent advances in IoT have sparked the interest of researchers to develop a smart management system for children. This paper presents a review of the smart management of children using the internet of things (IoT), machine learning, and Data Analytics. The objective is to identify potential approaches regarding smart management for children based on IoT for future researchers. The reviews were conducted from secondary sources, where conference proceedings and journals have been analyzed from Scopus and other databases. The finding indicates that IoT-based smart system has robust accuracy and performance. Thus, exploiting IoT for the development of a smart management system for children will provide significant benefits to the future generation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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ChatGPT, Let Us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

ChatGPT, Let Us Chat Sign Language: Experiments, Architectur...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Shahin, Nada Ismail, Leila Lab UAE University Department of CS and Software Engineering Al-Ain United Arab Emirates School of Computing and Information Systems CLOUDS Lab The University of Melbourne Melbourne Australia

ISBN: (纸本)9798350335590

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions. © 2023 IEEE.

关键词： Audition

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Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions 6th

Hybrid Transfer Learning Approach for Emotion Analysis of ...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Pamod, Dilshan Charles, Joseph Hewarathna, Ashen Iranga Vigneshwaran, Palanisamy Lekamge, Sugeeswari Thuseethan, Selvarajah Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Erlangen Germany Department of Software Engineering Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9783031530814

The ability to recognise and interpret emotional expressions is crucial since emotions play a significant role in our daily lives. Emotions are multifaceted phenomena that affect our behavior, perception, and cognition. As a result, numerous machine-learning and deep-learning algorithms for emotion analysis have been studied in previous works. Finding emotion in an obscured face, such as one covered by a scarf or hidden in shadow, is considerably harder than in a complete face, though. This study explores the effectiveness of deep learning models in occluded facial emotion analysis through a transfer learning approach. The performance of two individual pre-trained models, MobileNetV2 and EfficientNetB3, is compared alongside a hybrid model that combines both approaches. This comparison is conducted using the FER-2013 dataset. The dataset consists of 35,887 images and categorizes emotions into seven emotional categories. The results indicate that the hybrid model attained the highest accuracy, with a score of 93.04% for faces occluded at the top and 92.63% for faces occluded at the bottom. Additionally, the study suggests that top-occluded faces displayed more pronounced emotional expressions in comparison to bottom-occluded faces. Overall, these findings imply that hybrid architecture, which was developed as a state-of-the-art model in the study, proves to be effective for analyzing emotions in facial expressions that are partially obscured. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Efficient Regression with Feature Selection Based on L12-Norm 4

Efficient Regression with Feature Selection Based on L12-Nor...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Lei, Yu Li, Qin You, Jane Shenzhen Institute of Information Technology School of Software Engineering Shenzhen China Xidian University School of Telecommunications Engineering Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350324303

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with 12-norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. Then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by 12-norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the 12-norm regularization iteratively. Experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Feature Selection

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka 4

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Jayawardena, Aparna Ganegoda, Kasuni Imbulana, Sakuni Gunapala, Gavin Kodagoda, Nuwan Jayasinghe, Thilini Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries;leaf disease identification;cover crop identification;and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology. © 2022 IEEE.

关键词： Rubber

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Recent Advances in Edge computing for 6G

Recent Advances in Edge Computing for 6G

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2022 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Alawadhi, Abdulwadood Almogahed, Abdullah University of Science and Technology Faculty of Engineering and Computing Department of Computers and Information Technology Yemen Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Edge computing is a new technology that enables 6G development by bringing cloud capabilities closer to end users to overcome traditional cloud flaws such as high latency and a lack of security. Few studies identify recent advances, challenges, and opportunities in 6G edge computing. As a result, this paper presents a review to identify recent advances in edge computing in 6G. It also discusses the challenges and opportunities of such new trends in the computing landscape. The paper identifies challenges (security and privacy, offloading and task division, bandwidth, service management, advanced communication, general-purpose computing on edge nodes, quantum computing and edge computing, and IoT heterogeneous platforms) as well as opportunities (academic-industry collaborations, algorithms and libraries that are lightweight and virtualization and micro-operating systems). Finally, some conclusions have been drawn after identifying current research directions. © 2022 IEEE.

关键词： Internet of things

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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State of the Practice for Medical Imaging software

arXiv

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arXiv 2024年

作者： Smith, W. Spencer Dong, Ao Carette, Jacques Noseworthy, Michael D. McMaster University Computing and Software Department Canada McMaster University Electrical & Computer Engineering Department Canada

We present the state of the practice for Medical Imaging (MI) software. We selected 29 medical imaging projects from 48 candidates, assessed 10 software qualities (installability, correctness/verifiability, reliability, robustness, usability, maintainability, reusability, understandability, visibility/transparency and reproducibility) by answering 108 questions for each software project, and interviewed 8 of the 29 development teams. Based on the quantitative data for the first 9 qualities, we ranked the MI software with the Analytic Hierarchy Process (AHP). The four top ranked software products are: 3D Slicer, ImageJ, Fiji, and OHIF Viewer. Our ranking is mostly consistent with the community’s ranking, with four of our top five projects also appearing in the top five of a list ordered by stars-per-year. Generally, MI software is in a healthy state as shown by the following: in the repositories we observed 88% of the documentation artifacts recommended by research software development guidelines, 100% of MI projects use version control tools, and developers appear to use the common quasi-agile research software development process. However, the current state of the practice deviates from the existing guidelines because of the rarity of some recommended artifacts (like test plans, requirements specification, code of conduct, code style guidelines, product roadmaps, and Application Program Interface (API) documentation), low usage of continuous integration (17% of the projects), low use of unit testing (about 50% of projects), and room for improvement with documentation (six of nine developers felt their documentation wasn’t clear enough). From interviewing the developers, we identified five pain points and two qualities of potential concern: lack of development time, lack of funding, technology hurdles, ensuring correctness, usability, maintainability, and reproducibility. The interviewees proposed strategies to improve the state of the practice, to address the ident

关键词： Reusability

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