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Big Data Analytics for Proactive Financial Fraud Detection and Prevention 2

Big Data Analytics for Proactive Financial Fraud Detection a...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Hasudungan, Yehezkeil Oganda, Fitra Putri Daeli, Ora Plane Maria Raihan Santoso, Nesti Anggraini University of Raharja Dept. of Informatics Engineering Tangerang Indonesia University of Raharja Dept. of Information System Tangerang Indonesia University of Raharja Dept. of Digital Business Tangerang Indonesia University of Raharja Dept. of Computer System Tangerang Indonesia

ISBN: (纸本)9798350351613

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics. © 2024 IEEE.

关键词： Data Analytics Financial Fraud Prevention Proactive Detection Risk Management SmartPLS

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Development of a Predictive IoT-Based System for Real-Time Air Quality Monitoring and Route Optimization 6

Development of a Predictive IoT-Based System for Real-Time A...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Inthikhaff, M.I.M. Rimas, M.J.M. Kumari, J.M.D. Pasan, M.G.R. Kahandawaarachchi, K.A.D.C.P. Wijesiri, M.P.M. Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517878

Sri Lanka faces significant air pollution challenges, primarily from vehicular emissions, posing serious public health risks and impacting the quality of life. To address this, we developed a real-time air quality monitoring system that visualizes pollution data through an interactive heatmap. Our system integrates gas sensors that measure methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM.), connected to an ESP32 board and Wi-Fi module, and strategically deployed around smart buildings. The collected data is transmitted to a central hub using IoT technology and visualized on a mobile app developed with Flutter, featuring a color-coded scheme to represent pollution levels. This allows users to easily identify high-risk areas and select cleaner routes for travel. In testing, we focused on predicting PM. concentrations in Colombo using machine learning models, including Random Forest, XGBoost, and Prophet. Random Forest excelled in PM. prediction, while Prophet was highly effective for CO, demonstrating machine learning's potential in air quality forecasting. The system also includes a MIMI Air device that collects, processes, and transmits air quality data every 30 seconds, alerting users in real-time when pollutants exceed WHO thresholds, providing a cost-effective, reliable solution for public health. © 2024 IEEE.

关键词： Decision trees

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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Effectiveness of Recurrent Neural Network with Biomimic Components 3

Effectiveness of Recurrent Neural Network with Biomimic Comp...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Paras Nath Sakhare, Anupama Sarada, T. Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore560037 India Rtm Nagpur University Dept. of Electronics and Computer Science Nagpur440033 India

ISBN: (纸本)9798350343274

In order to improve the energy efficiency and computational capacity of our data processing systems, bioinspired, or neuromorphic, computing draws inspiration from how the brain computes. Emulation of natural systems, patterns, and components is known as biomimetics or biomimicry, and it is done to address challenging challenges in society. The term 'spintronics' refers to the application of a fundamental property of particles (the spin of an electron) known as the spin to information processing. Hopfield was the first to establish a direct connection between physical models and neural networks. To learn any of these models, the neural network must be able to adjust the coupling between each pair of spins. To implement multi-layer perceptron (neural network) structures, current technologies, however, have necessitated the extensive usage of complementary metal-oxide-semiconductor (CMOS) devices, which raises the perceptron's complexity of manufacture, space need, and power consumption. In this paper, it is suggested that the first trainable CMOS-free recurrent neural network with biomimetic components can be built using biomimetic neurons made of domain wall-magnetic tunnel junctions. This analysis shows how well this system performs computationally on benchmark tasks and how much more efficiently it performs computationally than alternative recurrent neural network systems. © 2023 IEEE.

关键词： Biomimetics

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Spice Vision: Deep Learning Enhanced Spice Classification System 3

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Pujari, Laxmi Belavatgi, Meenaz Sajjan, Mahantesh M. Kamatar, Vishwanath Surasura, Pradeep Ammanagi, Nausheen K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580027 India K.L.E Institute of Technolog Dept. of Computer Science and Engineering Hubballi580027 India

ISBN: (纸本)9798350364569

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model. © 2024 IEEE.

关键词： Convolutional neural networks

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Non-Invasive Human Body Temperature Detection for Online Event Data Recorder 3

Non-Invasive Human Body Temperature Detection for Online Eve...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Sani, Muhammad Ikhsan Ibnugraha, Prajna Deshanta Sari, Marlindia Ike Rizal, Moch Fahru Hanifa, Fanni Husnul Salasa, Salsabila Anza Reksa, Anggi Angga Hartono, Aditya Firmansyah Utama, Nur Cahya Telkom University Dept. of Computer Technology Bandung Indonesia Telkom University Dept. of Digital Marketing Bandung Indonesia Telkom University Dept. of Informatics Bandung Indonesia

ISBN: (纸本)9798350394559

The concept of black-box or Event Data Recorder (EDR) implementation on ground vehicle provides an alternative method for preemptive or prescriptive safety procedure. Utilizing all accessible technologies for accident prevention is crucial for ensuring transportation safety and regulation. Much effort has been devoted to developing thermal imaging as an additional sensor for EDR. However, several factors must be considered when using thermal imaging device on Event Data Recorder. Given that fever is a prevalent indication of several contagious illnesses, it is crucial to emphasize the monitoring of human body temperature for the purpose of fever screening. The goal is to reduce the number of individuals with elevated body temperatures from utilizing public transportation. This work aims to introduce a thermal camera application as a supplementary data source for the Event Data Recorder (EDR) in public transportation. The paper also proposed an alternative solution based on the use of a portable Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the online EDR system. The system has been evaluated and can collect data for future analysis, as demonstrated by both indoor and outdoor tests. It may function in a somewhat limited range within small space area up to 3-meter detection range. Further studies will have to clarify on increasing the accuracy with another algorithm or methodology. © 2023 IEEE.

关键词： Automobile bodies

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Proposal for the Support Method Presentation Model Utilizing Nursing Home Insights 9

Proposal for the Support Method Presentation Model Utilizing...

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

作者： Nakajima, Yoko Yuki, Masamune Ptaszynski, Michal Honma, Hirotoshi Akiba, Tomoyoshi Masui, Fumito Institute of Technology Kushiro Colloege Dept. of Creative Engineering Kushiro Japan School of Regional Innovation and Social Design Engineering of Kitami Institute of Technology Kitami Japan Toyohashi University of Technology Dept. of Computer Science and Technology and Engineering Toyohashi Japan

ISBN: (纸本)9798350383010

One of the significant problems faced by dementia group homes is the high caregiver turnover rate. In particular, there is an urgent need to prevent the turnover of new caregivers and work toward ensuring and improving the quality of care. To address this issue, we are developing a care method support system that utilizes two databases: a personal attributes database and a care case database ([1]-[3]). The personal attributes database extracts personal attributes such as hobbies and previous occupations of residents from care records and care conference minutes. The care case database extracts cases of resident problems, their solutions, and the original text containing them. The care method support system presents candidates for appropriate responses to problems faced by residents based on these databases. In this study, we generated two care suggestion models: Care Suggestion Model: This model applies text similarity to the care case database to suggest candidates for appropriate responses to unregistered problems. Personalized Care Suggestion Model: This model applies clustering to the personal attributes database and generates a care suggestion model that considers personal attributes. We compared the two models and tested the usefulness of the model. © 2024 IEEE.

关键词： Nursing

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