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Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy Sets

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computers, Materials & Continua 2024年第9期80卷 4947-4972页

作者： Daekook Kang Ramya Lakshmanaraj Samayan Narayanamoorthy Navaneethakrishnan Suganthi Keerthana Devi Samayan Kalaiselvan Ranganathan Saraswathy Dragan Pamucar Vladimir Simic Department of Industrial and Management Engineering Institute of Digital Anti-Aging Health CareInje UniversityGimhae-si50834Republic of Korea Department of Mathematics Bharathiar UniversityCoimbatore641046India Department of SocialWork SRMV Collge of Arts and ScienceCoimbatore641020India Department of Radiology Karpagam Medical College and HospitalCoimbatore641032India Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Department of Industrial Engineering and ManagementYuan Ze UniversityTaoyuan City320315Taiwan Department of Mechanics and Mathematics Western Caspian UniversityBakuAZ1001Azerbaijan Faculty of Transport and Traffic Engineering University of BelgradeBelgrade11000Serbia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea

This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in *** anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative *** anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this *** on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is *** a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical *** entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM *** analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed *** smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.

关键词： Normal wiggly hesitant pythagorean fuzzy set hesitant pythagorean fuzzy set anti-aging treatment entropy VIse Kriterijumska Optimizacija I Kompromisno Resenje

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Distributed Gradient Clustering: Convergence and the Effect of Initialization 58

Distributed Gradient Clustering: Convergence and the Effect ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Armacki, Aleksandar Sharma, Himkant Bajović, Dragana Jakovetić, Dušan Chakraborty, Mrityunjoy Kar, Soummya Carnegie Mellon University PittsburghPA United States Indian Institute of Technology Kharagpur Kharagpur India University of Novi Sad Faculty of Technical Sciences Department of Power Electronics and Computer Engineering Novi Sad Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Novi Sad Serbia

ISBN: (纸本)9798350354058

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the K-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization. © 2024 IEEE.

关键词： K-means clustering

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Random Forest approach optimized by the Grid Search process for predicting the dropout students

Random Forest approach optimized by the Grid Search process ...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Kumar, Devendra Kothiyal, Arnav Kumar, Raj Hemantha, C. Maranan, Ramya Gla University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Swami Keshvanand Institute of Technology Management & Gramothan Department of Mechanical Engineering Rajasthan Jaipur302017 India Bgs Institute of Technology Department of Mechanical Engineering Karnataka BG Nagar Mandya571448 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350319019

This study explores the application of Random Forest, a popular ensemble learning technique, in predicting student dropout behavior. The Random Forest algorithm is optimized using the Grid Search process to fine-tune its parameters, enhancing its ability to discern patterns indicative of dropout and non-dropout students. Dropout prediction in educational settings is of paramount importance for early intervention and support, aiming to mitigate the negative consequences of dropout on both individuals and society. By leveraging large-scale educational datasets containing diverse student attributes and academic performance metrics, the Random Forest model is trained to effectively classify students into dropout and non-dropout categories. Through rigorous evaluation and validation procedures, the optimized Random Forest model demonstrates promising performance in terms of predictive accuracy, sensitivity, and specificity. The findings suggest that Random Forest, empowered by parameter optimization through Grid Search, holds significant potential as a reliable tool for identifying students at risk of dropout, thereby enabling timely interventions to promote student retention and academic success. This research contributes to the advancement of predictive analytics in education and underscores the utility of machine learning algorithms in addressing critical challenges in student retention and success initiatives. © 2024 IEEE.

关键词： Students

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Fruit Recognition Through Active Grasping of Two-Fingered Gripper Using a Soft Skin Sensor

Fruit Recognition Through Active Grasping of Two-Fingered Gr...

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2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2023

作者： Mahendra, Oka Arifin, Muhammad Pratama, Rian Putra Munandar, Aris Baskoro, Catur Hilman A.H.B. Yulianti, Lista Eka Sakti, Indra Muhtadin Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Research Center for Appropriate Technology National Research and Innovation Agency Subang Indonesia Research Center for Electronics National Research and Innovation Agency Serpong Indonesia Sepuluh Nopember Institute of Technology Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350343892

Recognizing the fruit is a critical function to achieve for an autonomous harvesting system. Since fruit commonly grows in complex environments, it is challenging for the vision system to accurately identify the fruit. Humans, on the other hand, can easily recognize fruits by using their hands only. This paper proposes a method for fruit recognition based on active grasping with two-fingered grippers equipped with a soft skin sensor. The soft skin sensor has a soft silicone that is attached to the fingertip gripper. This sensor provides a 3-axis in normal and shear force based on a hall effect sensor. Active grasping by squeezing was implemented to sense and record time-series data of fruit properties. The evaluation was performed using several machine learning classification methods, such as Random Forest, Support Vector Machines (SVM), and K-Neighbors. The gripper can recognize fruit types with 88% accuracy using the Random Forest classifier. These results confirm the gripper's effective fruit recognition ability using active grasping to mimic human hand capabilities. © 2023 IEEE.

关键词： Fruits

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CoDescribe: An Intelligent Code Analyst for Enhancing Productivity and Software Quality 10th

CoDescribe: An Intelligent Code Analyst for Enhancing Produ...

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10th International Symposium on Applied Computing for Software and Smart systems, ACSS 2023

作者： Das, Souvick Deb, Novarun Cortesi, Agostino Chaki, Nabendu Department of Environmental Science Informatics and Statistics Ca’ Foscari University Venezia30172 Italy Indian Institute of Information Technology Vadodara Gujrat Gandhinagar382028 India Department of Computer Science and Engineering University of Calcutta 67 Sukanta Park Kolkata700101 India

ISBN: (纸本)9789819977826

Automating the documentation, explanation, and modification of source code has been an area of intense research interest for several decades. This paper proposes a novel framework for analyzing source codes to generate natural language summaries, answer complex user queries by reasoning, and even support syntactic error checking and code conversion to other languages. The framework incorporates transformer-based models and knowledge graphs to understand the context of the code and the user’s queries. The contributions of this proposed approach represent a significant leap forward by overcoming the limitations of the most advanced ChatGPT model. The framework ensures the accuracy and reliability of the responses while handling multiple source code files efficiently, making it a valuable asset for software developers and stakeholders. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wan, Zhang Lin, Zhuoyi Rashid, Shamima Ng, Shaun Yue-Hao Yin, Rui Senthilnath, J. Kwoh, Chee-Keong Nanyang Technological University School of Computer Science and Engineering Singapore Institute for Infocomm Research Agency for Science Singapore Nanyang Technological University School of Chemical and Biomolecular Engineering Singapore University of Florida Department of Health Outcome and Biomedical Informatics GainesvilleFL United States

ISBN: (纸本)9798350337488

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM's success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

关键词： deep learning paratope-epitope interaction SARS-CoV-2 set modeling Transformer

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Quality of Service-aware 6G-Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains

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IEEE Transactions on Consumer Electronics 2025年

作者： Roy, Bipasha Guha Roy, Deepsubhra Guha Dutta, Piyali Khan, Surbhi Bhatia Asiri, Fatima Ayadi, Manel Institute of Engineering and Management Kolkata India Institute of Engineering and Management Kolkata India University of Salford Chitkara University Institute of Engineering and Technology Chitkara University Department of Data Science United Kingdom University Centre for Research and Development Chandigarh University India Centre for Research Impact and Outcome Punjab Rajpura140401 India King Khalid University College of computer science Informatics and computer systems department Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia

Internet of Things connectivity in home health monitoring is a high-in-demand application area. The electronics industry and procedural researchers seek high-end, secured, on-time, cost-effective ways to build reliable quality of service (QoS) proved autonomous systems using existing wireless techniques. Also, the continuous availability of a traffic-free gateway, particularly in isolated places, is necessary for large-scale data gathering and real-time data update processes to function the sensor nodes in an Internet of Things (IoT) network. Improved Narrowband IoT (NB-IoT) is one of the most desirable networks offered by 6th generation (6G) connectivity in IoT-associated remote-monitoring proceedings. In this article, we propose a QoS-aware narrow bandwidth allocation-based prototype model for healthcare monitoring in long-distance high-speed trains during the patient transfer from home to the healthcare center or vice versa. This article demonstrates a possible enhancement in social aspects of cognitive IoT applications with large data systems in industrial informatics. © 1975-2011 IEEE.

关键词： Frequency allocation

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A Comparative Analysis for Android Malware Detection Using Machine Learning Models

A Comparative Analysis for Android Malware Detection Using M...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Harsh Shah Vidit Shah Nirmay Soni Vaishali Vadhavana Krishna Patel Department of Computer Science & Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of science and Technology(CHARUSAT) Anand India Department of Computer Science & Engineering Parul Intitute of Engineering and Technology (PIET) Parul University Waghodia Gujarat India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study explores the usability of machine learning algorithms in classifying whether Android applications are excellent or malicious. The data used here for the study is obtained from Kaggle and named “TUANDROMD.” Both kinds of application samples are present in the data. From application displays and runtime behaviour, pertinent features have been retrieved using methods including K-Nearest Neighbor(KNN), Support Vector Machine(SVM), and Decision Tree classifiers. With regard to accuracy, precision, recall, F1-score, and ROC AUC metrics, every result is thoroughly examined. The provided SVM model achieved the highest possible accuracy and reliability in malware identification. Thus, the research demonstrates how to combine static and dynamic analysis as a fundamental method for detecting malware and future directions regarding improvements in mobile Security, including deep learning and real-time detection systems. The classification accuracy of the classifier model KNN, SVM, and Decision Tree classify a 97.65%, 98.62%, and 99.10% accuracy rate, respectively.

关键词： Support vector machines Deep learning Accuracy Runtime Operating systems Computational modeling Malware Real-time systems Decision trees Security

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Bottleneck-Minimal Indexing for Generative Document Retrieval

arXiv

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arXiv 2024年

作者： Du, Xin Xiu, Lixin Tanaka-Ishii, Kumiko Waseda Research Institute for Science and Engineering Waseda University Japan Department of Mathematical Informatics The University of Tokyo Japan Department of Computer Science and Engineering Waseda University Japan

We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T , and a neural autoregressive model is trained to map queries Q to T . GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes T . By applying Shannon’s rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes T can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods. © 2024, CC BY.

关键词： Signal distortion

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural Computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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