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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Alamsyah, Doni Purnama Arifin, Toni Ramdhani, Yudi Hidayat, Febriansyah Achmad Susanti, Leni Bina Nusantara University Entrepreneurship Department Jakarta Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Bank Rakyat Indonesia (BRI) is an Indonesian state-owned bank;the operations of the bank certainly aim to provide satisfaction to customers. This can be realized by quickly responding to the bank's response to complaints. The number of complaints received by BRI banks to date is large, but the categories of complaints are still mixed so each division of the bank has difficulties and must first filter complaints. This study aims to classify bank complaints using the Text Mining field to make it easier for BRI to provide complaints directly to each division. The classification method used is Neural Network and the data preprocessing method uses the TF -IDF method. The dataset used in the form of text is one million datasets with 5 classes, namely debit cards, credit cards, customer service, mobile banking, and loans. The results of this study indicate that the accuracy of the neural network method is better and becomes a recommendation to be implemented at BRI. This research is useful for banking in Indonesia or the service industry in managing customer relationship management. © 2022 IEEE.

关键词： Customer satisfaction

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MQTT-Chain: An MQTT approach using blockchain and smart contracts to achieve a new Quality of Service level

MQTT-Chain: An MQTT approach using blockchain and smart cont...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Bruno Machado Agostinho Stefano Chessa Raffaele Perego Mario Antônio Ribeiro Dantas Alex Sandro Roschildt Pinto Computer Science Postgraduate Program Federal University of Santa Catarina Florianópolis Brazil Computer Science Department University of Pisa Pisa Italy ISTI National Research Council of Italy Pisa Italy Computer Science Postgraduate Program Federal University of Juiz de Fora Brazil

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

The proliferation of Internet of Things (IoT) applications has surged in recent years, necessitating efficient communication protocols. The Message Queuing Telemetry Transport (MQTT) protocol, designed specifically for IoT devices, has gained prominence due to its lightweight publisher/subscriber model. However, inherent concerns arise within the conventional MQTT architecture, particularly pertaining to broker-side vulnerabilities. In addressing these concerns and enhancing data security, we advocate the utilization of blockchains and smart contracts for storing and transmitting broker messages. We designed and compared two different approaches, bringing a detailed latency analysis for each step and validating its functional viability, establishing a robust environment for IoT applications.

关键词： Smart contracts Quality of service Orbits Blockchains Internet of Things Proposals Telemetry Reliability Optimization Testing

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Optimized Fetal ECG Feature Extraction with Genetic Algorithm Based Heart Rate Detection 4

Optimized Fetal ECG Feature Extraction with Genetic Algorith...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Pokkuluri, Kiran Sree Soni, Nidhi Sahu, Amit Mukherjee, Tilak Prasanthi, Y. Sharma, Disha Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal462044 India Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Haldia Institute of Technology Electronics and Communication Engg. Dept West Bengal Haldia721657 India Kkr & Ksr Institute of Technology and Sciences Dept of CSE-Data Science Guntur India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

Congenital heart problems are the most common primary cause of death that can be attributed to birth defects. It is possible to identify cardiac defects by analyzing the geomorphology of cardiac electric indications. Additionally, the examination of fetal cardiac signals, which does not involve any intrusive procedures, can be an efficient method for monitoring the health of the fetal heart and identifying cardiac abnormalities at an early stage. One method that can be utilized to evaluate the health and condition of the fetus is the ECG signal, which is a graphical copy of the electric potential that is generated in conjunction with the action of the heart. However, there are several technical issues that arise during the process of non-invasive FECG extraction. These issues include interference from maternal electrocardiograms and maternal electromyograms, interference from power lines operating at 50 Hz, and baseline drift. Extraction and analysis of the fetal electrocardiogram signal are the key goals of electronic fetal monitoring;they are the primary objectives. In the process of extracting features from the fetal ECG indication, which is a nonlinear mix of the gentle ECG, the fetal ECG indication, and other interfering signals, digital signal processing techniques constitute a key part of the process. It is possible to find solutions to problems involving abnormality detection by employing data mining techniques and soft computing approaches for categorization. © 2024 IEEE.

关键词： Fetal monitoring

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The Impact of Language Expressions and Visual Exploration on Neurodivergent Artist Quotes in Educational Setting 9

The Impact of Language Expressions and Visual Exploration on...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Indrawati, Desi Sjaerodji, Yuda Suryasa Darisman, Aris Waridha, Imaniar Rizki Bina Nusantara University Language Center Faculty of Humanities Computer Science Department West Java Bandung Indonesia School of Design Bina Nusantara University Visual Communication Design Study Program West Java Bandung Indonesia

ISBN: (纸本)9798350383010

In recent years, neurodiversity has gained popularity among those proponents of people's inclusion, dignity, and rights. Demonstrations and images help them learn more effectively. Give written instructions, visual clues, and brief spoken instructions when feasible. Furthermore, the researchers included seventeen student populations, eleven officers and staff, and twelve lecturer populations. For two days in a row, they attended the interactive quotation display by neurodivergent artists, and in response, they contributed drawings that reflected their interpretations of the quotes. An interview served as the second method of gathering data. The researchers also conducted a structured interview with four students, four officers, and three lecturers acting as spokespeople. Quotes from the works of Neurodivergent Artists such as Nintendo, Love, Amsterdam, Suzanne, Kacamata (Glasses), Teman (Friends), Mudik (Homecoming), Menjahit (Sewing), and Iggle can be found in the Interactive Installation activity. Using a mixed-methods approach, the researchers collected and examined the data (Creswell, 2014). It is an approach that blends qualitative and quantitative research techniques. According to the findings, the Amsterdam quote is liked by lecturers, staff, and students alike. In addition, the Love quote sparks curiosity in both students and instructors, indicating a mutual admiration for its meaning. © 2024 IEEE.

关键词： Students

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Jayanth Chowdam Sibarama Panigrahi Department of Computer Science and Engineering Intelligent Data Analytics Laboratory National Institute of Technology Rourkela Odisha India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL.

关键词： Support vector machines Accuracy Computational modeling Feature extraction Skin Vectors Lesions Convolutional neural networks Residual neural networks Principal component analysis

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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computer Modeling in Engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Aided Diagnostic Systems CT scan Deep Learning Liver Tumor Semantic Segmentation

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Link Prediction based on bipartite graph for recommendation system using optimized SVD++

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Procedia computer science 2023年 218卷 1353-1365页

作者： Anshul Gupta Pravin Shrinath Department of Computer Engineerig MPSTME Narsee Monjee Institute of Management Studies Department of Data Science SP Jain School of Global Management

To alleviate the big data difficulties that have created a potential problem for many Internet users, it is necessary to filter, rank, and efficiently communicate the relevant information on the Web, where the diversity of possibilities is overwhelming. Recommender systems, which sift through enormous amounts of dynamically generated data to give consumers with personalised information and services, are able to overcome this problem. Bipartite graphs are currently generally used to store and understand this data due to its sparse nature. data are mapped to a bipartite user-item interaction network where the graph topology captures detailed information about user-item associations, transforming a recommendation issue into a link prediction problem. Earlier, approaches for link prediction in bipartite graphs for various recommendation systems were developed, but the efficacy of the prediction methodology was not close to the standards required by real-time recommenders. So, the primary goal of this research is to offer an effective link prediction-based recommendation system that takes advantage of SVD++ along with K-nearest neighbors and reduces the system's error rate, leading to better outcomes. The proposed system is sorely tested on the MovieLens dataset and compared to some traditional recommendation methods. The results demonstrate that the suggested strategy exceeds all traditional approaches in terms of accuracy, and the actual suggestions are equally encouraging.

关键词： Sparse data Recommendation Bipartite Graph Link Prediction SVD++

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Self-supervised Learning for Geospatial AI: A Survey

arXiv

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arXiv 2024年

作者： Chen, Yile Huang, Weiming Zhao, Kaiqi Jiang, Yue Cong, Gao The College of Computing and Data Science Nanyang Technological University Singapore The Department of Physical Geography and Ecosystem Science Lund University Sweden The School of Computer Science University of Auckland New Zealand

The proliferation of geospatial data in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across various urban applications. Given the vast yet inherently sparse labeled nature of geospatial data, there is a critical need for techniques that can effectively leverage such data without heavy reliance on labeled datasets. This requirement aligns with the principles of self-supervised learning (SSL), which has attracted increasing attention for its adoption in geospatial data. This paper conducts a comprehensive and up-to-date survey of SSL techniques applied to or developed for three primary data (geometric) types prevalent in geospatial vector data: points, polylines, and polygons. We systematically categorize various SSL techniques into predictive and contrastive methods, discussing their application with respect to each data type in enhancing generalization across various downstream tasks. Furthermore, we review the emerging trends of SSL for GeoAI, and several task-specific SSL techniques. Finally, we discuss several key challenges in the current research and outline promising directions for future investigation. By presenting a structured analysis of relevant studies, this paper aims to inspire continued advancements in the integration of SSL with GeoAI, encouraging innovative methods to harnessing the power of geospatial data. © 2024, CC BY.

关键词： Contrastive Learning

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A literature review of crowd-counting system on convolutional neural network 2

A literature review of crowd-counting system on convolutiona...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Chrysler, A. Gunarso, R. Puteri, T. Warnars, H.L.H.S. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the proliferation usage of video surveillance for safety, traffic control, and privacy purposes and with the constant growth of population, it is important to keep monitoring using Closed-Circuit Television (CCTV). With new upcoming developed technologies, new systems and algorithms are introduced and implemented to the crowd counting system today retrieving live video surveillance from the CCTV. However, recent studies show that there are some challenges still faced regarding the crowd counting system which uses the density estimation. The problems that occurred have resulted from the inaccuracy of the system that is caused by several factors. Factors such as the perspective distortion which is caused by the lack of data training and the method such as face detection is an ineffective method to determine the population density. Studies proposed have projected the idea of developing a more robust crowd counting methodology by implementing crowd counting by detection, clustering, and regression. Implementing these methods using the Convolutional Neural Network (CNN) will better the result of the detection since in CNN the image can be inputted and it will undergo several layers which will result in the system being able to differentiate one image from the other. With CNN the process of crowd counting will be able to be more advanced. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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