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Neutrosophic Sets and Systems: A Decade of Scientific Contribution and Growth

Neutrosophic Sets and Systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and Systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Blockchain in Concurrent Green IoT-Based Agriculture: Discussion, Analysis, and Implementation 6th

Blockchain in Concurrent Green IoT-Based Agriculture: Discus...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Alam, Md Janay Choudhury, Ashiquzzaman Al Maksud, Kazi Sifat Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031503269

The adaptation of IoT-based technology in agriculture is increasing rapidly in recent times. IoT devices are of great assistance to farmers in monitoring dairy farms, greenhouses, and other agricultural facilities. The most crucial component of an IoT device is ensuring data security and privacy. Data leakage can have significant negative consequences as it may result in financial loss for farmers. It is important to prioritize data security to protect the interests of farmers and ensure the success of their agricultural endeavors. Blockchain technology can be a solution to secure data transactions in the network layer. With the concept of private and public key pairs, data hashing, and decentralized systems;important and sensitive data will be secured. In this study, we discuss the structure of a three-layer architecture for green IoT technology and identify the privacy issues presented in each layer. Then we propose a solution by implementing a secure Ethereum-based blockchain system for data transactions at the network layer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Losses

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Cross-Domain Few-Shot Sparse-Quantization Aware Learning for Lymphoblast Detection in Blood Smear Images 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Aboutahoun, Dina Zewail, Rami Kimura, Keiji Soliman, Mostafa I. Department of Computer Science and Engineering Egypt-Japan University for Science and Technology Alexandria Egypt Department of Computer Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9783031476655

ISBN: (纸本)9783031476648

Deep learning for medical image classification has enjoyed increased attention. However, a bottleneck that prevents it from widespread adoption is its dependency on very large, annotated datasets, a condition that cannot always be satisfied. Few-shot learning in the medical domain is still in its infancy but has the potential to overcome these challenges. Compression is a way for models to be deployed on resource-constrained machines. In an attempt to tackle some of the challenges imposed by limited data and high computational resources, we present a few-shot sparse-quantization aware meta-training framework (FS-SQAM). The proposed framework aims to exploit the role of sparsity and quantization for improved adaptability in a low-resource cross-domain setting for the classification of acute lymphocytic leukemia (ALL) in blood cell images. Combining these strategies enables us to approach two of the most common problems that encounter deep learning for medical images: the need for extremely large datasets and high computational resources. Extensive experiments have been conducted to evaluate the performance of the proposed framework on the ALL-IDB2 dataset in a cross-domain few-shot setting. Performance gains in terms of accuracy and compression have been demonstrated, thus serving to realize the suitability of meta-learning on resource-constrained devices. Future advancements in the domain of efficient deep learning computer-aided diagnosis systems will facilitate their adoption in clinical medicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Large dataset

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Food Supply Chain Management Using Hyperledger 7

Food Supply Chain Management Using Hyperledger

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Mali, Ajit S. Jagtap, Amol M. Katekar, Shubham Shinde, Shubham Ashtekar, Kunal Rajarambapu Institute of Technology Department of Computer Science and Engineering Islampur India Rajarambapu Institute of Technology Computer Science & Engineering Maharashtra Islampur India

ISBN: (纸本)9798350397284

This research study investigates the potential of blockchain technology, specifically the Hyperledger platform, in addressing challenges in food supply chain management, The aim of this study is to propose a comprehensive framework for the implementation of Hyperledger in food supply chain management. The paper begins by highlighting the issues faced in food supply chain management, including food safety, traceability, and transparency problems. It then provides an overview of the Hyperledger platform and its features, and explores how it can be leveraged to address these challenges, The study focuses on the implementation of Hyperledger in food supply chain management, examining its potential benefits, challenges, and limitations. It also discusses the key stakeholders involved in the food supply chain, and how the adoption of Hyperledger can benefit each of them. Furthermore, the paper presents a case study of a pilot project that used Hyperledger in food supply chain management, The research findings demonstrate that Hyperledger can be an effective solution for addressing challenges in food supply chain management, particularly in ensuring food safety, traceability, and transparency. However, the study also highlights some challenges and limitations of implementing Hyperledger in this context, such as the need for standardized data formats and the high cost of implementation. Overall, this paper provides valuable insights for organizations and policymakers considering the adoption of blockchain technology in food supply chain management. © 2023 IEEE.

关键词： Supply chain management

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Leveraging Machine Learning to Predict Asthma following Vaccination 5

Leveraging Machine Learning to Predict Asthma following Vacc...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Gull, Seema C. NaveenKumar, C.G. Gull, Karuna C. Sanskruti, V.B. Laddi, Mahantesh Karnataka State Open University Department of Studies in Computer Science Mysuru India S.G.Balekundri Institute of Technology Department of Computer Science & Business Systems Belagavi India M.S. Sheshgiri College Engineering and Technology Department of Biomedical Engineering Belagavi India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350376685

This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is important to monitor and prevent issues such as asthma. The goals for future research include the creation of risk-adjusted models that may predict the risk of asthma following vaccination, depending on patients' demographic information and the characteristics of the vaccines. The research uses several sophisticated techniques such as Artificial-Neural-Networks (ANN), Random-Forests (RF), Naïve-Bayes (NB), and logistic regression (LR) to improve the ability to predict and identify prevention measures and healthcare management. Thus, a significant innovation of this work is the comprehensive assessment of these algorithms when exposed to noise, which is a common issue in many medical data sets. The study also showed that Random-Forest and ANN models were significantly more accurate and yielded larger ROC AUC scores than other models;moreover, they were much less sensitive to noise with noise values ranging from 0 to 0.5. This places RF and ANN on a better pedestal to offer a better prediction of post-vaccination asthma than the other models, especially in a scenario where there is the possibility of data contamination. The present findings can thus help create better and more resistant models of prediction that are useful in better health management and treatment of asthma over and after vaccination. © 2024 IEEE.

关键词： Logistic regression

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A trust-based clustering approach to form stable clusters in vehicular ad hoc networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16209-16228页

作者： Singh, Dinesh Maurya, Ashish Kumar Ranvijay Yadav, Rama Shankar Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Over the last two decades, vehicular ad hoc networks (VANETs) have evolved to disseminate real-time traffic information, emergency information, and multimedia data to vehicles on highways and urban roads. Due to vehicles’ high-speed mobility, the topology in VANETs changes dynamically and causes instability in networks. It further degrades the performance of vehicular applications and consumes network resources. Clustering is one of the approaches in VANETs that is utilized to achieve stability. This paper proposes a Trust-based Stable Clustering (TSC) approach to form stable clusters in VANETs. This approach encompasses a significant cluster head election, cluster joining, and cluster leaving algorithms. The cluster head election algorithm forms stable clusters based on several parameters such as vehicles’ trust value, degree, position, remaining travel time, and speed. The cluster joining and cluster leaving algorithms improve the stability of formed clusters by reducing the cluster reformation frequency and selecting alternate cluster heads. The performance of the TSC is compared with the well-known clustering methods on different parameters such as average cluster head duration, number of state changes, and number of cluster formation messages. The results show that the TSC outperforms than the compared approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

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SN computer science 2023年第6期4卷 710页

作者： Patil, Manoj A. Manohar, M. Department of Computer Science and Engineering Christ (Deemed to be University) School of Engineering and Technology Kengeri Karnataka Bengaluru 560074 India Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad 500104 India

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Hybrid classifier LSTM SISS-OB

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X-ray Image Analysis for Aided Detection of COVID-19

X-ray Image Analysis for Aided Detection of COVID-19

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Avinash Barua, Tarkeshwar Garg, Prachi Saxsena, Rini Goyel, Parul Singh Parmar, Vivek Chandigarh Group of Colleges Department of Computer Science and Engineering Chandigarh Engineering 140307 India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Mmec Department of Computer Science and Engineering Mullana India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India Chitkara University Department of Cse India

ISBN: (纸本)9798331508432

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instruments. Chest X-rays offer a readily available and relatively inexpensive Tomography proficiency. This stuff explores the potential of X-ray image analysis using Profound learning algorithmic program for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instruments for COVID-19 diagnosis, manual analysis is time-consuming and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers promising solutions for automated and accurate COVID-19 detection from these Tomography modalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancements, discuss remaining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests remain the gold standard for diagnosis, delays in processing limit their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary immunization face significant logistical hurdles. Faster and more reliable identification instruments are crucial to complement these ongoing efforts. © 2024 IEEE.

关键词： Vaccines

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YOLO Dietician 3

YOLO Dietician

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Vaishnavi, Sirikonda Shetty, Sanne Hruthika Maheswari, M. Jany Shabu, J.L. Refonaa, J. Sathyabama Institute of Science and Technology Department of Cse Chennai India Sathyabama Institute of Science and Technology Dept.of Computer Science and Engineering India

ISBN: (纸本)9798350375190

In recent years, the integration of Artificial Intelligence (AI) in healthcare has attracted a lot of attention, particularly in the domain of personalized nutrition. With the rising prevalence of diet-related health issues such as obesity, diabetes, and cardiovascular diseases, the primary objective of this research study is to design and implement a platform capable of analyzing individual preferences, nutritional needs, health goals, and lifestyle factors to offer customized dietary guidance and management. This research study proposes a AI Dietician by utilizing AI algorithms to provide personalized dietary recommendations. This study also covers the technical aspects and methods used in creating a AI dietician, including feature extraction, algorithm selection, data gathering strategies, and model training by ultimately contributing to the prevention of diet-related health issues and promoting long-term health outcomes. © 2024 IEEE.

关键词： Nutrition

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Relative Power Loss Analysis of Poly-Si PV Panels: An Overview in Eastern Indian Climatic Condition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1775-1780页

作者： Pal, Sudipta Basu Das Bhattacharya, Konika Mukherjee, Dipankar Department of Computer Science Technology University of Engineering Management Kolkata India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Department of Electronics and Telecommunication Engineering Indian Institute of Engineering Science and Technology Shibpur India

Renewable energies are generated from natural processes that are continuously renewed. Therefore, it is required to combine the PV modules either in series combinations or parallel combinations for developing the appropriate Photovoltaic (PV) panel/PV arrays. Several PV modules are connected in a series–parallel combination to constitute PV arrays. These PV arrays currently act as a decentralized source of D.C power for onward transmission. When identical wattages PV modules are coupled in a parallel or series connection, there must be a probability to the PV module's manufacturing process that gives rise to power loss. Mainly these types of losses are called Mismatch losses. Mismatch losses (power losses) in PV panels principally occur in terms of Relative Power Loss (RPL). Therefore, the authors carried out I–V characterizations of different technology of PV modules under varying insolation and temperatures. The main aim of the work is to develop the right kind of photovoltaic (PV) panel/PV arrays in a particular Eastern Indian climatic zone. From the authors' characterization of RPL, analysis was conducted to judge which technology is most suitable to build a large size array/BIPV. The experimental results reveal lower mismatch losses of poly-Si PV modules for crystalline silicon and amorphous silicon counterparts. © 2022, The Institution of Engineers (India).

关键词： Solar panels

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