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Analysis of Banking and Financial Sector Services Using Artificial Intelligence (AI) 1

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Isavnin, Alexey Kosorukova, Irina Madaminov, Inomjon Laxmi Lydia, E. Satapathy, Suresh Department of Business-Informatics and Mathematical Methods in Economics Naberezhnye Chelny Institute KFU Kazan Federal University Naberezhnye Chelny Russia Department of Corporate Finance and Corporate Governance Financial University under the Government of the Russian Federation Moscow Russia Department of Accounting and Auditing Urgench State University Urgench Uzbekistan Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam530049 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9789819748921

ISBN: (纸本)9789819748914

This research proposes the assessment of banking and financial services using AI to monitor how banks apply AI approaches and the feedback they receive from customers. In order to better track, anticipate, and react to customer behavior, the financial information generated by the growing utilization of digital pay and banking can be mined by banks and other financial institutions. Based on research, secondary sources are reviewed to ascertain the data used in banking and financial services. A methodical survey is designed to gather the main information clients have regarding AI applications. The study is that private banks and other financial institutions use a variety of artificial intelligence (AI) services to better serve their clients and ensure that they are happy with their financial services, as some clients are not happy with banking services. Additionally, the results indicated that clients would be more committed to banking and financial services representatives by providing creative preparation for the advancement of AI practices in the workplace. © The Author(s).

关键词： Decentralized finance

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A Comprehensive View on the Progress of Organoid Research with an Emphasis on its Relevance to Disease Characterization

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Biomedical and Environmental Sciences 2023年第10期36卷 959-971页

作者： Chandra KISHORE Vaishali Ji Saurav Mallik Ayan MUKHERJI Namrata TOMAR Soumen Kumar Pati LI Ai Min Sinthia Roy BANERJEE Soumadip GHOSH Raza Ali NAQVI University of California Irvine Medical CenterOrangeCA 92868USA Department of Botany J.D.Women’s CollegeBailey RoadPatna-800023India Harvard T.H.Chan School of Public Health BostonMA 02115USA Department of Information Technology Haldia Institute of TechnologyHaldiaWest BengalIndia Medical College of Wisconsin MilwaukeeWI 53226USA Department of Bioinformatics Maulana Abul Kalam Azad University of TechnologyNadiaWest BengalIndia Center for Precision Health School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX 77030USA School of Computer Science and Engineering Xi’an University of TechnologyXi'an 710048ShaanxiChina Department of Information Technology Guru Nanak Institute of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Sister Nivedita UniversityKolkataWest BengalIndia Center for Precision Health School of Biomedical InformaticsUniversity of IllinoisChicago60612USA

INTRODUCTION Organoids are primary tissue or stem cells derived cell aggregates that have the capacity for self-organization,self-renewal,and the capacity to mimic cellular and tissue level *** can overcome the shortc... 详细信息

关键词： functions. overcome shortcomings

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study 2

Harnessing Deep Learning for Effective Extractive Text Summa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Alsekait, Deema Mohammed Almanaseer, Waref Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Baligh, Salma Alaa, Hana Hegazy, Jasmine Magdy, Julia Eldmrat, Alaa Tariq Abdelminaam, Diaa Salama Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Saudi Arabia Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Midocean University Faculty of Human Sciences United Arab Emirates Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Egypt MEU Research Unit Middle East University Faculty of Computers Amman11831 Jordan

ISBN: (纸本)9798350362633

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context. © 2024 IEEE.

关键词： Contrastive Learning

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Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making 6

Improving Communication Systems Through Federated Learning: ...

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6th World Symposium on Communication Engineering, WSCE 2023

作者： Stergiou, Konstantinos D. Ishibashi, Yutaka Goudos, Sotirios K. Lagkas, Thomas Sarigiannidis, Panagiotis Psannis, Konstantinos E. University of Macedonia Department of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece International Hellenic University Department of Computer Science Kavala Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece

ISBN: (纸本)9798350339505

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems. © 2023 IEEE.

关键词： Learning systems

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical Engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS 12

VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GRO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies Italian National Council of Research Italy Hacettepe University Department of Computer Engineering Turkey Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational linguistics

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From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization

arXiv

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arXiv 2024年

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit HUN-REN Institute for Computer Science and Control Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains. © 2024, CC BY.

关键词：

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Khilenko, Volodymyr Bielokha, Halyna Denysiuk, Serhii Stepanov, Oleksii Kotuliak, Ivan Slovak University of Technology in Bratislava Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Bratislava Slovakia National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic Institute" Power Supply Department Kyiv Ukraine National University of Life and Environmental Sciences Ukr Article Acdsa Department of Information Technologies Khilenko ID 414 Kyiv09-02-2024 Ukraine

ISBN: (纸本)9798350394528

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used. © 2024 IEEE.

关键词： Distributed power generation

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