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共 12901 条记录，以下是131-140 订阅

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IT Governance & Management for Smart City: Aligning COBIT 2019 with SPBE Policies for Traditional Market Digitalization Initiatives

IT Governance & Management for Smart City: Aligning COBIT 20...

引用

2024 International Conference on information technology Systems and Innovation, ICITSI 2024

作者： Restavie Bujung, Rizky Dwiputra Hartanto, Rudy Winarno, Wing Wahyu Gadjah Mada University Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798331511470

The digitalization of traditional markets is a significant government initiative aimed at preserving market competitiveness in the digital era. However, the program's success is closely tied to effective IT governance due to the complexity of needs and challenges faced by the government This study proposes an IT Governance and Management (ITGM) framework for traditional market digitalization, leveraging the COBIT 2019 framework in alignment with Indonesia's Electronic-Based Government System (SPBE) policies. Using Yogyakarta City and Sleman Regency as case studies, this research identifies key challenges, including stakeholder engagement, digital literacy gaps, resource limitations, and policy shortcomings. By prioritizing 11 ITGM objectives that address these challenges and align with SPBE policies, the study offers a targeted framework for local governments to advance digitalization efforts within traditional markets, linking them to broader Smart City and SPBE initiatives. This approach offers practical guidance for local governments to effectively structure and sustain traditional market digitalization efforts. Future research could explore its adaptability to other Smart City programs, particularly those lacking robust regulatory and governance frameworks. © 2024 IEEE.

关键词： COBIT 2019 Digitalization IT Governance IT Management SPBE Policy Traditional Market

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Efficient Kidney Tumor Classification and Segmentation with U-Net 46

Efficient Kidney Tumor Classification and Segmentation with ...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Varalakshmi, P. Tilak Soorya, R. Anugeeta, S. Akshaya, S. Anna University College of Engineering Dept. of Information Science and Technology Chennai India

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371499

A novel approach to kidney tumor identification is introduced, which integrates kidney tumor classification and segmentation algorithms. The multi-faceted approach commences with the classification of kidney images, adaptly discerning between normal and tumor instances. For individuals identified as tumor-positive, a sophisticated UNet-based architecture is intricately employed to achieve precise segmentation, capturing nuanced details of both kidney and tumor regions. Many models, including VGG16, MobileNetV3, DenseNet50, and others, were tested in order to achieve this. Among these, MobilenetV3 performs better than the others in terms of accuracy, with a 99.1% accuracy rate and a 99% precision rate for classification. In this research, we applied a novel U-Net model to accurately segregate kidney and kidney tumor from CT scan data. With this, an average dice coefficient score of 0.9445 is obtained. In advancing the landscape of kidney tumor analysis, this proposed strategy not only bridges classification and segmentation but also showcases a significant leap toward refined clinical applications. © 2024 IEEE.

关键词： Image segmentation

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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Revolutionizing Healthcare with IoT: Integrated Monitoring of BMI and ECG

Revolutionizing Healthcare with IoT: Integrated Monitoring o...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Srinivas, M.G.C. Sangeetha, Y. Vinay, P Sai Kumar, S Tharun V R Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350381887

People lacking awareness of the importance of BMI may not acknowledge the potential health hazards associated with being underweight, overweight, or obese. Insufficient awareness may result in neglecting potential concerns, such as cardiovascular diseases and diabetes, commonly associated with BMI. Without knowledge of ECG and its role in monitoring heart health, individuals may not be aware of early signs of cardiovascular problems. Delayed detection of irregular heart patterns or abnormalities may increase the risk of heart disease or other cardiac-related complications. This project combines hardware components with software development to create a holistic health monitoring system that promotes user awareness and engagement. © 2024 IEEE.

关键词： Heart

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Innovative Currency Identifier for the Blind through Audio Output using Deep Learning

Innovative Currency Identifier for the Blind through Audio O...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Maganti, Rajasri Gutla, Jessica Mallimpalli, Yaswanth Yellisetti, Sandeep Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350382723

With an estimated 4.95 million blind people and 70 million visually impaired individuals in India are facing challenges in their day-to-day monetary transactions, worsened by the complexities in identifying tactile features of notes post-demonetization, this research presents an approach to enhance independence and accessibility. The introduction of the Currency Identification System (CIS), which is specifically designed for Indian cash and is accomplished via an android application with a camera interface for voice output and recording. We examine the shortcomings of current systems while highlighting the particular difficulties experienced by the blind people in our society. As a result, we have developed a novel CIS that combines two deep learning algorithms: Convolutional Neural Networks (CNN) and Residual Networks (ResNet). We trained a model using these two algorithms and compared the algorithms to use the best model for our application. The outcomes demonstrate the ResNet's exceptional performance, with an 90% accuracy rate. By demonstrating the effectiveness of deep learning models in these kinds of applications, our research adds to the growing field of assistive technology. With the help of our research, we shed light on the ways in which technology can promote independence and inclusion in the lives of people, which is a critical step in the direction of increased financial accessibility and control. © 2024 IEEE.

关键词： Convolutional neural networks

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SmartGuard: Support Vector Machine(SVM)-Powered Defense Mechanism for Phishing Prevention

SmartGuard: Support Vector Machine(SVM)-Powered Defense Mech...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rambharat, Yadav Abhishek Shelke, Dev Ritesh Khaleel, Kundlik Adnan Arunkumar, Anande Aditya Ekhande, Deepa Pillai College of Engineering Dept. Computer Engineering New Panvel India Pillai College of Engineering Dept. Information Technology New Panvel India

ISBN: (纸本)9798331517953

SmartGuard serves as a strong defense system against data breaches and financial risks from hackers by utilizing a range of machine learning algorithms, including Support Vector Machine (SVM). The proposed system involves a website that accepts URLs as input from users and employs various models to predict the legitimacy of the provided URL, distinguishing between legitimate and malicious websites. Additionally, users can input messages containing links for further analysis. Nowadays, many phishing links are received by people on WhatsApp and Emails. These links pretend to be genuine but in reality, are malicious. Clicking on this link can cause data or monetary loss to an individual. The proposed system also incorporates a client-side extension, and leverages many parameters to evaluate the authenticity of a website, protecting users from visiting potentially malicious sites. The integration of AI technologies and the user friendly interface make SmartGuard a robust tool against Phishing and other cyberattacks. © 2024 IEEE.

关键词： Phishing

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Implementing a Dynamic-Static Hybrid Fog Computing System for Content Sharing on Local Networks

Implementing a Dynamic-Static Hybrid Fog Computing System fo...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sugawara, Shinji Chiba Institute of Technology Faculty of Engineering Dept. Information and Communication Systems Engineering Narashino Japan

ISBN: (纸本)9798350324136

We have so far proposed a method for configuring a virtual fog server using mobile user terminals that originally belong to the edge in an information network system, making a three-layer cloud-fog-edge structure logically to give a content sharing service. This method is called dynamic fog. In this method, in each area in a city where a large number of users gather, multiple user terminals work together and dynamically form a fog server, switching roles around each other over time. In this paper, as a step before implementing a complete dynamic fog for content sharing, we describe the design for implementing a dynamic-static hybrid fog. In that fog system, a static, simple terminal, which works as a core of a dynamic fog, is set in the place where users can easily gather and it bundles multiple user terminals to make a fog function. © 2024 IEEE.

关键词： cloud-fog-edge structure content sharing dynamic fog peer-To-peer network

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Performance Investigation of Customized MFCC Feature Extraction in Recognizing Indonesian Conversational Emotion 16

Performance Investigation of Customized MFCC Feature Extract...

引用

16th International Conference on information technology and Electrical engineering, ICITEE 2024

作者： Halimah, Noor Siti Hidayat, Risanuri Mustika, I Wayan Gadjah Mada University Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350375817

Modifying the MFCC extraction process is an opportunity to improve Indonesian SER performance. This work confirms the performance of MFCC features obtained by customized MFCC extraction. This paper utilizes customized MFCC features with single-layer LSTM as a classifier model. MFCC was customized by providing values of pre-emphasis of 0.97, a frame length of 0.025 seconds with an overlap of 0.01 seconds, FFT points of 512, a mel-filter of 40, and 13 MFCCs. The result shows better performance in single-layer LSTM compared with SVM using PCA, which results in a training accuracy of 0.789 and a testing accuracy of 0.787. K-fold cross-validation was confirmed with an average accuracy of 0.816 using k value 5 and 0.879 with k value 10. © 2024 IEEE.

关键词： Emotion Recognition

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LEARNING MEAN FIELD GAMES ON SPARSE GRAPHS: A HYBRID GRAPHEX APPROACH 12

LEARNING MEAN FIELD GAMES ON SPARSE GRAPHS: A HYBRID GRAPHEX...

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12th International Conference on Learning Representations, ICLR 2024

作者： Fabian, Christian Cui, Kai Koeppl, Heinz Dept. of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made significant progress towards solving these systems, solutions for many agents often remain computationally infeasible and lack theoretical guarantees. Mean Field Games (MFGs) address both of these issues and can be extended to Graphon MFGs (GMFGs) to include network structures between agents. Despite their merits, the real world applicability of GMFGs is limited by the fact that graphons only capture dense graphs. Since most empirically observed networks show some degree of sparsity, such as power law graphs, the GMFG framework is insufficient for capturing these network topologies. Thus, we introduce the novel concept of Graphex MFGs (GXMFGs) which builds on the graph theoretical concept of graphexes. Graphexes are the limiting objects to sparse graph sequences that also have other desirable features such as the small world property. Learning equilibria in these games is challenging due to the rich and sparse structure of the underlying graphs. To tackle these challenges, we design a new learning algorithm tailored to the GXMFG setup. This hybrid graphex learning approach leverages that the system mainly consists of a highly connected core and a sparse periphery. After defining the system and providing a theoretical analysis, we state our learning approach and demonstrate its learning capabilities on both synthetic graphs and real-world networks. This comparison shows that our GXMFG learning algorithm successfully extends MFGs to a highly relevant class of hard, realistic learning problems that are not accurately addressed by current MARL and MFG methods. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Learning algorithms

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