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1. Multilingual Knowledge Graphs and Low-Resource Languages: A Review

Author

Kaffee, Lucie-Aimée, Biswas, Russa, Keet, C. Maria, Vakaj, Edlira Kalemi, and de Melo, Gerard

Subjects
knowledge graphs, multilingual, low-resource languages, review, Electronic computers. Computer science, QA75.5-76.95
Abstract

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multilingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages.

Published
2023
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2. Knowledge Graph Embeddings: Open Challenges and Opportunities

Author

Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, and de Melo, Gerard

Subjects
knowledge graphs, kg embeddings, link prediction, kg applications, Electronic computers. Computer science, QA75.5-76.95
Abstract

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.

Published
2023
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3. Large Language Models and Knowledge Graphs: Opportunities and Challenges

Author

Pan, Jeff Z., Razniewski, Simon, Kalo, Jan-Christoph, Singhania, Sneha, Chen, Jiaoyan, Dietze, Stefan, Jabeen, Hajira, Omeliyanenko, Janna, Zhang, Wen, Lissandrini, Matteo, Biswas, Russa, de Melo, Gerard, Bonifati, Angela, Vakaj, Edlira, Dragoni, Mauro, and Graux, Damien

Subjects
large language models, pre-trained language models, knowledge graphs, ontology, retrieval augmented language models, Electronic computers. Computer science, QA75.5-76.95
Abstract

Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.

Published
2023
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4. CODE-ACCORD: A Corpus of Building Regulatory Data for Rule Generation towards Automatic Compliance Checking

Author

Hettiarachchi, Hansi, Dridi, Amna, Gaber, Mohamed Medhat, Parsafard, Pouyan, Bocaneala, Nicoleta, Breitenfelder, Katja, Costa, Gonçal, Hedblom, Maria, Juganaru-Mathieu, Mihaela, Mecharnia, Thamer, Park, Sumee, Tan, He, Tawil, Abdel-Rahman H., and Vakaj, Edlira

Subjects
Computer Science - Information Retrieval
Abstract

Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland. Only the self-contained sentences, which express complete rules without needing additional context, were considered as they are essential for ACC. Each sentence was manually annotated with entities and relations by a team of 12 annotators to facilitate machine-readable rule generation, followed by careful curation to ensure accuracy. The final dataset comprises 4,297 entities and 4,329 relations across various categories, serving as a robust ground truth. CODE-ACCORD supports a range of ML and Natural Language Processing (NLP) tasks, including text classification, entity recognition, and relation extraction. It enables applying recent trends, such as deep neural networks and large language models, to ACC., Comment: This is a preprint of an article submitted to the Scientific Data Journal

Published
2024

6. Utilisation of open intent recognition models for customer support intent detection

Author

Mohammad, Rasheed, Favell, Oliver, Shah, Shariq, Cooper, Emmett, and Vakaj, Edlira

Subjects
Computer Science - Computation and Language
Abstract

Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents., Comment: 9 pages, 3 figures, conference

Published
2023

7. Semantic Carbon Footprint of Food Supply Chain Management

Author

Yadav, Swapnali, Powers, Megan, Vakaj, Edlira, Tiwari, Sanju, Ortiz-Rodriguez, Fernando, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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11. Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model

Author

Akinlade, Oluwatobi, Vakaj, Edlira, Dridi, Amna, Tiwari, Sanju, Ortiz-Rodriguez, Fernando, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jabbar, M. A., editor, Ortiz-Rodríguez, Fernando, editor, Tiwari, Sanju, editor, and Siarry, Patrick, editor

Published
2023
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16. A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports.

Author

Basaad, Abdullah, Basurra, Shadi, Vakaj, Edlira, Eldaly, Ahmed Karam, and Abdelsamea, Mohammed M.

Subjects
LANGUAGE models, GRAPH neural networks, METASTATIC breast cancer, METASTASIS, TUMOR classification
Abstract

Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model's utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Automation in Construction Cost Budgeting using Generative Artificial Intelligence.

Author

Parsafard, Pouyan, Elezaj, Ogerta, Ekundayo, Damilola, Vakaj, Edlira, Parmar, Milan, and Wani, Mudasir Ahmad

Subjects
AUTOMATION, GENERATIVE artificial intelligence, CONSTRUCTION costs, BUILDING information modeling, LANGUAGE models
Abstract

This paper explores the paradigm of continuous improvement in construction cost management, emphasizing the utilization of Generative Artificial Intelligence (Generative AI) as a pivotal factor in driving this advancement. Embracing the lean construction principle and the plan-do-check-act approach, the study emphasizes the need for requirements and techniques in cost management. Building Information Modeling (BIM) is recognized as crucial for budgeting construction costs, but it faces challenges related to automation and technology. In this study we explore the use of Generative AI in generating Bills of Quantities (BOQs) for building cost management in small and medium-sized enterprises (SMEs). The exploration combines transformer-based techniques and Large Language Models (LLM), introducing a novel approach to development of BOQs in construction projects. Robustly Optimized BERT Pre-training approach (RoBERTa), an effective and reliable transformer architecture, categorizes substructure and superstructure components with an impressive 91% accuracy, forming a solid foundation. Fuzzy similarity matching connects sub-element records to project cost tables, incorporating cost data for meticulous computations. Leveraging the advanced text-generation capabilities of GPT-4, a state-of-the-art language model, the study automates the construction of a detailed and comprehensive BOQ. This departure from traditional rule based BOQ development offers enhanced flexibility and contextual knowledge. This approach, combining transformer-based models for precise categorization with LLM (GPT-4) for automated BOQ generation, presents a simplified and efficient solution for cost budgeting in building projects. This innovative method not only promises improved project documentation but also signifies potential revolutionary progress in building cost-management procedures. The findings provide valuable insights for construction professionals, paving the way for a more efficient and advanced approach to cost management in the construction industry. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Semantisation of Rules for Automated Compliance Checking

Author

Vakaj, Edlira, Lefrançois, Maxime, Dridi, Amna, Beach, Thomas, Gaber, Mohamed, Jutglar, Gonçal Costa, Tan, He, Birmingham City University (BCU), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Informatique et systèmes intelligents ( FAYOL-ENSMSE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE), School of Engineering [Cardiff], Cardiff University, Zagazig University, and La Salle [Ramon Llull University]

Subjects
Machine Learning, Ontologies and Building Data Standards (e.g. IFC), [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Knowledge graphs, [INFO]Computer Science [cs], Rule/compliance checking, Machine Learning Ontologies and Building Data Standards (e.g. IFC) Knowledge graphs Rule/compliance checking
Abstract

International audience; The Architecture, Engineering, and Construction (AEC) industry is subject to numerous regulations and standards that govern the design, construction, and maintenance of buildings and infrastructure. These regulations often involve complex language and technical jargon, which can be difficult to understand and apply in practice. Semantisation, or the process of transforming natural language into machine-readable data with explicit meaning, can address this challenge by creating structured representations of regulations that can be exchanged and processed by computers. This can enable automated compliance checking, facilitate communication between stakeholders, and improve the efficiency and effectiveness of regulatory enforcement.

Published
2023

25. Large Language Models and Knowledge Graphs: Opportunities and Challenges

Author

Jeff Z. Pan and Simon Razniewski and Jan-Christoph Kalo and Sneha Singhania and Jiaoyan Chen and Stefan Dietze and Hajira Jabeen and Janna Omeliyanenko and Wen Zhang and Matteo Lissandrini and Russa Biswas and Gerard de Melo and Angela Bonifati and Edlira Vakaj and Mauro Dragoni and Damien Graux, Pan, Jeff Z., Razniewski, Simon, Kalo, Jan-Christoph, Singhania, Sneha, Chen, Jiaoyan, Dietze, Stefan, Jabeen, Hajira, Omeliyanenko, Janna, Zhang, Wen, Lissandrini, Matteo, Biswas, Russa, de Melo, Gerard, Bonifati, Angela, Vakaj, Edlira, Dragoni, Mauro, Graux, Damien, Jeff Z. Pan and Simon Razniewski and Jan-Christoph Kalo and Sneha Singhania and Jiaoyan Chen and Stefan Dietze and Hajira Jabeen and Janna Omeliyanenko and Wen Zhang and Matteo Lissandrini and Russa Biswas and Gerard de Melo and Angela Bonifati and Edlira Vakaj and Mauro Dragoni and Damien Graux, Pan, Jeff Z., Razniewski, Simon, Kalo, Jan-Christoph, Singhania, Sneha, Chen, Jiaoyan, Dietze, Stefan, Jabeen, Hajira, Omeliyanenko, Janna, Zhang, Wen, Lissandrini, Matteo, Biswas, Russa, de Melo, Gerard, Bonifati, Angela, Vakaj, Edlira, Dragoni, Mauro, and Graux, Damien

Abstract

Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.

Published
2023
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26. Knowledge Graph Embeddings: Open Challenges and Opportunities

Author

Russa Biswas and Lucie-Aimée Kaffee and Michael Cochez and Stefania Dumbrava and Theis E. Jendal and Matteo Lissandrini and Vanessa Lopez and Eneldo Loza Mencía and Heiko Paulheim and Harald Sack and Edlira Kalemi Vakaj and Gerard de Melo, Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, de Melo, Gerard, Russa Biswas and Lucie-Aimée Kaffee and Michael Cochez and Stefania Dumbrava and Theis E. Jendal and Matteo Lissandrini and Vanessa Lopez and Eneldo Loza Mencía and Heiko Paulheim and Harald Sack and Edlira Kalemi Vakaj and Gerard de Melo, Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, and de Melo, Gerard

Abstract

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.

Published
2023
Full Text
View/download PDF

27. Multilingual Knowledge Graphs and Low-Resource Languages: A Review

Author

Lucie-Aimée Kaffee and Russa Biswas and C. Maria Keet and Edlira Kalemi Vakaj and Gerard de Melo, Kaffee, Lucie-Aimée, Biswas, Russa, Keet, C. Maria, Vakaj, Edlira Kalemi, de Melo, Gerard, Lucie-Aimée Kaffee and Russa Biswas and C. Maria Keet and Edlira Kalemi Vakaj and Gerard de Melo, Kaffee, Lucie-Aimée, Biswas, Russa, Keet, C. Maria, Vakaj, Edlira Kalemi, and de Melo, Gerard

Abstract

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multilingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages.

Published
2023
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31. Real-Time Birds Shadow Detection for Autonomous UAVs

Author

Bouali, Kassem Anis, Hajdu, András, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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32. AI and Assistive Technologies for Persons with Disabilities - Worldwide Trends in the Scientific Production Using Bibliometrix R Tool

Author

Dange, Pravin, Mistry, Tausif, Mann, Shikha, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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33. An Improved Deep Learning Model Implementation for Pest Species Detection

Author

Agarwal, Nikita, Kalita, Tina, Dubey, Ashwani Kumar, Om, Shreyas, Dogra, Anika, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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34. Food Composition Knowledge Extraction from Scientific Literature

Author

Jiomekong, Azanzi, Folefac, Martins, Tapamo, Hippolyte, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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35. Unveiling the Potentials of Deep Learning Techniques for Accurate Alzheimer’s Disease Neuro Image Classification

Author

Mishra, Debahuti, Lenka, Arundhati, Mishra, Sashikala, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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37. Automated Scene Recognition for Environmental Monitoring: A Cluster Analysis Approach using Intel Image Classification Dataset

Author

Waykole, Yoginii, Kanathey, Yashasvi, Goel, Vaani, Bongale, Anupkumar, Kadam, Prachi, Kadam, Kalyani, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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38. Leaf Disease Detection Using Transfer Learning

Author

Saharan, Mohit, Singh, Ghanapriya, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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39. An Approach Towards Mitigation of Renewable Energy Curtailment

Author

Dhabai, Poonam, Tiwari, Neeraj, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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41. WDNRegClass - A Hybrid ANN + Bayesian Learning Model to Reduce Temporal Predictive In-Variance Towards Mitigation of WDN Revenue Losses

Author

Pandian, C., Alphonse, P. J. A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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43. Conceptual Framework for Representing Knowledge in the Energy Sector

Author

Ben Abbes, Sarra, Temal, Lynda, Seye, Oumy, Calvez, Philippe, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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44. Identification of Diseases Affecting Mango Leaves Using Deep Learning Models

Author

Shaik, Thaseentaj, Swamykan, Sudhakar Ilango, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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45. RWNR: Radial Basis Feed Forward Neural Network Driven Semantically Inclined Strategy for Web 3.0 Compliant News Recommendation

Author

Kannan, Beulah Divya, Deepak, Gerard, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Mishra, Sashikala, editor, Vakaj, Edlira, editor, and Kotecha, Ketan, editor

Published
2023
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