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Fermatean Fuzzy Analytic Hierarchy Process for Supplier Selection on LARG Supply Chain Paradigm

Fermatean Fuzzy Analytic Hierarchy Process for Supplier Sele...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Kabadayi, Nihan Bakkal, Salih Istanbul Univ TR-34310 Avcilar Turkiye Piri Reis Univ TR-34940 Tuzla Turkiye

ISBN: (纸本)9783031671913;9783031671920

Multi-criteria decision-making (MCDM) methods have proven to be successful at solving complex decision problems. The fuzzy set theory, recognized for its ability to capture and represent uncertainty in real-world problems, has been extensively incorporated. Within the MCDM method literature, various fuzzy sets have been created to manage uncertain information inherent in real-world decision-making processes. Fermatean Fuzzy Sets (FFS), an extension of intuitionistic fuzzy sets (IFS), emerge as a robust framework for addressing uncertainty challenges in real-world problems. FFSs, demonstrating enhanced capabilities in mitigating limitations associated with membership functions and effectively representing hesitancy, contribute significantly to advancing MCDM methodologies. One of the most critical MCDM problems is supplier selection. Recently, supply chains have become vulnerable due to risks arising from various internal and external factors. Companies are striving to increase their supply chain performance in a competitive, risky, and ever-changing environment by adopting different paradigms. In this context, the integration of lean, agile, resilient, and green (LARG) paradigms into supply chains serves to elevate the operational performance and strategic competitiveness of these companies. Determining the most suitable supplier group is an important decision-making process crucial for the successful pursuit of integrated supply chain paradigms. This paper aims to provide a comparative examination for distinct types of fuzzy set definitions: Spherical fuzzy sets (SFS), IFSs, and FFSs for MCDM problems involving uncertainty. To test the performance of the FFSs in solving real-world problems, a comparative analysis is applied in a case study using the FF-AHP (Analytic Hierarchy Process) method.

关键词： Fermatean fuzzy sets MCDM AHP LARG supply chain supplier selection

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Cyber Victimization: Tools Used to Combat Cybercrime and Victim Characteristics 9th

Cyber Victimization: Tools Used to Combat Cybercrime and Vic...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Dupuis, Marc Jones, Emiliya Univ Washington Bothell WA 98011 USA

ISBN: (纸本)9789819745807;9789819745814

Cyber victimization is explored through the lens of end users and the tools they use to combat cybercrime. These tools are important in mitigating a variety of threats to the confidentiality, integrity, and availability of information and associated systems for end users, whether through intentional criminal activity, accidents, or system/device malfunction. This is done by examining the characteristics of individuals and the degree to which they have been a victim, including various demographics and personality types. A large-scale survey was used to aid in this endeavor. Gender identification, household income, and education were all related to cybersecurity tool usage. Those that identified as male, had a higher reported household income, and/or were more educated, were more likely to use a variety of cybersecurity tools. Additionally, individuals with higher levels of neuroticism were less likely to use a number of cybersecurity tools. Implications and future directions are discussed.

关键词： Cyber victimization Personality Cybersecurity tools Demographics

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An Adaptive Federated Learning Approach for Efficiency and Privacy Preservation of Dynamic Network of IoT 8th

An Adaptive Federated Learning Approach for Efficiency and P...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Dave, Madhavi Bhatt, Dulari Mundanad, Manjari Adani Inst Digital Technol Management Gandhinagar 382423 Gujarat India

ISBN: (纸本)9789819713226;9789819713233

For training machine learning models in networked systems like the Internet of Things (IoT), the approach of federated learning (FL) has become intriguing. FL makes it possible to learn models collaboratively across many devices by providing efficiency in training and testing. The standard FL technique, however, has significant challenges due to the dynamic nature of IoT networks. In this research paper, we propose an adaptive federated learning (AFL) approach that is capable of accommodating the dynamism of IoT networks along with privacy-preserving. AFL utilizes dynamic model training by using data streaming at edge devices of IoT networks. The proposed algorithm efficiently trains the AFL model at the cloud data store by receiving the encrypted data with homomorphic cryptography from an edge device. It also preserves the privacy of data in transit. The AFL model gets trained by the encrypted data only by maintaining the bucket of data streaming. This paper demonstrates the effectiveness of AFL on a real-world IoT data-set, where it achieves higher accuracy with data privacy.

关键词： Federated learning Adaptive federated learning Internet of Things Dynamic network Data privacy Data streaming Edge computing

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Optimum Feature Selection Using Meta-heuristic Algorithms 5th

Optimum Feature Selection Using Meta-heuristic Algorithms

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Saraswat, Mukesh Tyagi, Neha Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9789819720811;9789819720828

In the evolving landscape of machine learning, where image datasets pose unique challenges due to their high dimensionality, this research explores the application of the meta-heuristic algorithms, namely, the Whale Optimization Algorithm, Mayfly-Harmony Search Algorithm, and Binary Social Mimic Optimization for feature selection. The focus lies on strategically curating relevant visual features to optimize computational efficiency, enhance model interpretability, and improve generalization. Evaluations on a benchmark dataset for detecting damaged buildings post-Hurricane Harvey reveal algorithm's efficacy in achieving better accuracy. The document concludes with prospects for future research, emphasizing the need for a deeper comparative analysis of feature selection methods, exploration of algorithm scalability, and integration of domain-specific knowledge for real-world applications.

关键词： Feature selection Image dataset analysis Machine learning models Computational efficiency

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Mini-review on DC-DC Power Converters Used in V2X Battery Chargers 4th

Mini-review on DC-DC Power Converters Used in V2X Battery Ch...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Bella, Hicham Rachid, Aziz Abbou, Ahmed Errifai, Nidale Khamlichi, Seddik Saidi, Elhoussaine Mohammed V Univ Mohammadia Sch Engn Elect Engn Dept Rabat 10090 Morocco Hassan II Univ Casablanca LSIB Lab FST Mohammadia 28806 Morocco

ISBN: (纸本)9783031686498;9783031686504

The widespread adoption of Vehicle-to-Everything (V2X) communication systems has significantly transformed the electric vehicle (EV) charging infrastructure landscape. As V2X Chargers are essential for facilitating bidirectional power flow amid electric vehicles and the grid, efficiency and reliability of the power conversion process become paramount. This mini-review provides a comprehensive overview of recent advancements in DC-DC converters utilized in V2X chargers, focusing on the existing topologies and their features.

关键词： V2X technology Bidirectional DC-DC power converter Isolated converter Non-isolated converter

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A Deep Learning Approach for Evaluating Children's Handwriting 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Kumar, Ashutosh Simha, J. B. Abhi, Shinu REVA Univ REVA Acad Corp Excellence RACE Bangalore Karnataka India

ISBN: (数字)9789819713295

ISBN: (纸本)9789819713288;9789819713295

Facilitating precise writing proficiency in early learners is crucial for their foundational development and cognitive abilities. While traditional handwriting instruction methods are valuable, they may not always provide the specific validation needed to ensure accuracy. Traditional handwriting methods can be slow and often lack personalized feedback, which is important for effective learning. This study presents a method for teaching young learners how to write correctly by offering an interactive user interface in which new learners try to imitate the displayed word while the model checks their imitation and provides constant feedback on the handwritten word at the character level. Assessing children's handwriting is tricky compared to adults because their letters can be hard to distinguish. Creating a model to evaluate children's handwriting could change how we teach and evaluate writing skills. It could make education more efficient and effective. This paper explores various methods for evaluating handwritten words at the character level. First, the K-nearest neighbors (KNN) model is considered, known for its ability to distinguish similar and dissimilar items. However, KNN faces challenges in handling numerous classes and adapting to new data. The second approach is considered to have a Siamese-based twin network consisting of three CNNs and two LSTM layers. This model exhibits promise, achieving over 90% accuracy on training and validation sets. Nevertheless, it encounters difficulties with new, unseen data. The approach considered in this paper presents a simple yet robust model, featuring three CNN layers, one flattened layer, and two dense layers. This model effectively classifies all 52 character classes. Notably, the CNN-based model attains an impressive 96% accuracy on both test and validation sets, even when dealing with handwriting style it has not encountered before. This demonstrates the model's ability to apply learned knowledge and potential for p

关键词： CNN KNN Neural networks Deep learning Computer vision Siamese networks

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Enhancing Pneumonia Detection Accuracy Through ResNet-Based Deep Learning Models and Ensemble Techniques: A Study Using Chest X-Ray Images 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Shirvalkar, Rigved Ajai, A. S. Remya Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Amritapuri Kerala India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Pneumonia, a respiratory infection caused by microbes, is a major health concern, particularly in underdeveloped areas. Factors like high pollution, poor sanitation, crowded housing, and insufficient health care add to its frequency. Pneumonia often progresses to pleural effusion, where fluid fills the lungs, hindering breathing. Timely and precise pneumonia diagnosis is vital for proper treatment and better survival. Chest X-ray is the primary diagnostic method, though analysis can be difficult and variable. This work aims to enhance pneumonia detection accuracy from chest X-rays using various techniques. Key challenges include subjectivity in interpretation and developing robust algorithms for automated detection. Optimizing diagnosis could improve clinical workflows and patient outcomes. The analysis encompasses three main areas: data preprocessing, model experimentation, and performance enhancement through hyperparameter tuning and ensembling. In the data preprocessing phase, various techniques like transforming data to grayscale, applying random rotations, normalizing pixel intensity values, resizing images, and weighted random sampling were employed to enhance model generalization. The model experimentation involved assessing ResNet-9, custom CNN, and ResNet-18 architectures. The ResNet-18 architecture proved to be the most effective for pneumonia classification due to its balance between complexity and generalization. Hyperparameter tuning using Optuna further optimized the model performance, resulting in improved accuracy compared to default parameter settings. We also utilized an ensemble approach, using K-fold cross-validation, resulting in a 3% increase in accuracy compared to using a single model. In this work, we implemented and analyzed a comprehensive methodology combining effective data preprocessing, model architecture exploration, hyperparameter tuning, and ensemble techniques to highlight ways that can be implemented to enhance pneumonia detection

关键词： Pneumonia detection ResNet-9 ResNet-18 CNN

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Future Changes in Computing Infrastructures as a Result of Advancements in Intelligent systems 9th

Future Changes in Computing Infrastructures as a Result of A...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Al Rasch, Firend Amer Univ Malta Cospicua Malta

ISBN: (纸本)9789819733040;9789819733057

This research paper examines the evolution of intelligent systems, and how intelligent systems can revolutionize computing infrastructures. To fulfill the ever-changing needs in efficiency, scalability, and security, traditional computing frameworks are changing by integration artificial intelligence, machine learning, and data analytics as core components of current and foreseeable future evolution. This study explores important issues, specifically, in relation to the future of intelligent computing infrastructures. Methodology applied in this research includes literature review, comparative analysis, expert interviews, and predictive modeling. Findings of this research suggest not only prominent improvements in data processing capacity and efficiency, but also new security and workforce dynamics challenges. This research paper adds to the conversation regarding intelligent systems' influence on the development of next-generation computing infrastructures by outlining to both practitioners, researchers, and policymakers with useful insights that are applicable for both industry and academically.

关键词： Intelligent systems Computing infrastructure Changes Advancement

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Leveraging Artificial Intelligence for Environmental Information Integration in Investment Decision Making 4th

Leveraging Artificial Intelligence for Environmental Informa...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Boussetta, Marwane Ababou, Mariame Faquir, Sanae Private Univ Fez Fez Business Sch Lab Res Entrepreneurship & Management Org REMO Fes Morocco Private Univ Fez Fac Engn Sci Lab Smart Syst Energy & Sustainable Dev SIEDD Fes Morocco

ISBN: (纸本)9783031686498;9783031686504

The growing focus on environmental sustainability in current investment decisions calls for a complete rethinking. Environmental factors are increasingly embedded into how investors calculate risk and opportunities, alongside traditional financial metrics. But incorporating environmental data into investment analysis faces a whole host of challenges, complexity because of inherent diversity, lack of standardization and the list goes on. The present study attempts to investigate the transformative capacity of Artificial Intelligence (AI) in addressing these challenges. As the potential solution, AI burst onto the scene and seemed set to transform environmental data with the promise of novel means to boost its investment relevance. This research employs a rigorous structured methodology, which includes systematic literature review, empirical validation and a theoretical framework development. This study is a comprehensive evaluation of whether AI is practical for using environmental data in investment decisions. Therefore, the research shows how AI can be applied to environmental information intelligence and its actual capabilities in solving problems in the ever-changing world of investment studies.

关键词： Artificial intelligence Environmental information Decision-making investment

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A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion

A Simple But Powerful Graph Encoder for Temporal Knowledge G...

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Intelligent systems Conference (IntelliSys)

作者： Ding, Zifeng Ma, Yunpu He, Bailan Wu, Jingpei Han, Zhen Tresp, Volker Ludwig Maximilians Univ Munchen Munich Germany Siemens AG Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9783031477140;9783031477157

Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC methods integrate advanced deep learning techniques, e.g., Transformers, and achieve superior model performance. However, this also introduces a large number of excessive parameters, which brings a heavier burden for parameter optimization. In this paper, we propose a simple but powerful graph encoder for TKGC, called TARGCN. TARGCN is parameter-efficient, and it extensively explores every entity's temporal context for learning contextualized representations. We find that instead of adopting various kinds of complex modules, it is more beneficial to efficiently capture the temporal contexts of entities. We experiment TARGCN on three benchmark datasets. Our model can achieve a more than 46% relative improvement on the GDELT dataset compared with state-of-the-art TKGC models. Meanwhile, it outperforms the strongest baseline on the ICEWS05-15 dataset with around 18% fewer parameters.

关键词： Natural language processing Representation learning Temporal knowledge graph

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