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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Cutting-Edge Advanced Machine Learning Model for Enhanced Breast Cancer Diagnostics 9th

Cutting-Edge Advanced Machine Learning Model for Enhanced Br...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Novillo, Esteban Montesdeoca, Maria Hurtado, Remigio Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9789819733019;9789819733026

Breast cancer is one of the most severe and prevalent cancers. It occurs in all of the countries around the world in women at any age. Nowadays it is increasing its morbidity and mortality rates. This paper presents a novel segmentation model to increase the quality measures applied to the detection of benign and malignant pathologies in the context of breast cancer through the application of convolutional operations, segmentation procedures, and bagging between support vector classifiers (SVC) to optimize the diagnostic process. It analyses the Chinese Mammography Database [1] (mammography images of breast carcinoma), aiming to evaluate the performance of these Machine Learning algorithms. It processes and analyses pixels that these techniques have learned as the most relevant, giving insights into the decision-making process of the SVC accuracy, precision, specificity, and sensitivity. The algorithm focuses on breast carcinoma, obtaining results that evidence the promising performance, increasing the quality of the results obtained in contrast with other methodologies, allowing a more efficient procedure to enhance the application of the model and its behavior.

关键词： Data science Breast cancer Tumor Machine learning Diagnostic SVC Medical image processing

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Embedding Artificial Intelligence into Wearable IoMT systems 2nd

Embedding Artificial Intelligence into Wearable IoMT Systems

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Puckett, Steven Menon, Vineetha Jovanov, Emil Univ Alabama Huntsville Elect & Comp Engn Huntsville AL 35899 USA Univ North Alabama Sanders Coll Business & Technol Florence AL 35632 USA Univ Alabama Huntsville Dept Comp Sci Huntsville AL USA

ISBN: (纸本)9783031608391;9783031608407

Recent advances in sensors, wearable technologies, and real-time embedded Artificial Intelligence (AI) allows the creation of wearable systems for continuous health monitoring and real-time detection of critical health issues. In the case of heart arrhythmias, this currently requires data to be transmitted to the cloud for processing by computationally heavy models and human interaction. Instead, real-time analysis by AI models on the wearable device would reduce communication transmissions, improve energy usage, and provide immediate feedback to the user and physicians. We have shown that AI models trained using Big Data datasets on larger platforms can be optimized and then deployed as wearable sensors integrated into Internet of Medical Things (IoMT) systems. We used Artificial Intelligence models to detect abnormal heart beats trained using the MIT-BIH dataset. Our objective is to identify heartbeat abnormalities using a model optimized for embedded wearable sensors. The systemwouldwarn the user in the case of abnormal heart beats and upload all detected abnormal beats to the medical server for evaluation by a cardiologist. The system was 98.4% accurate in classification of normal/abnormal heartbeats and only missed approximately 0.13% arrhythmias per day with an execution time per heart beat classification of 65.6 ms and uses 0.911 microwatt-hours of energy. The program requires 409 KB of additional memory of ESP32 embedded controller. Utilizing the classification model and only transmitting heartbeats classified as abnormal improves battery usage by 85.5% over streaming heartbeats to cloud services for analysis assuming 10% abnormal classifications per day at an average of 100,000 heartbeats per day.

关键词： AI IoMT MIT-BIH Deep Neural Network Convolutional Neural Network

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Integrating Deep Reinforcement Learning into Agent-Based Models for Predicting Farmer Adaptation Under Policy and Environmental Variability

Integrating Deep Reinforcement Learning into Agent-Based Mod...

引用

Intelligent systems Conference

作者： Andrew, Kevin Zia, Asim Rizzo, Donna Univ Vermont Burlington VT 05401 USA

ISBN: (纸本)9783031664274;9783031664281

In designing models of integrated socio-environmental systems, human behaviors in Agent Based Models (ABMs) have been generally modeled with variety of simple to complex behavioral theories predicting behavioral change, such as bounded rational theory, rational choice theory, value-belief-norm theory, and the theory of planned behavior. In an attempt to improve the representation of intelligent agents in ABMs, whose cognition is embedded with memory and foresight, and who are responsive to dynamically evolving feedbacks between human and environmental systems, this paper presents a novel ABM, in which intelligent agents are embedded with deep reinforcement learning derived behavioral rules. A Deep Reinforcement Learning enabled ABM (DRL-ABM) is calibrated in this paper to simulate the behavior of 480 farmer agents engaged in joint production of food and water pollution in Missisquoi Watershed of trans-boundary Lake Champlain Basin. The DRL-ABM specifically focuses on predicting the adoption of adaptation actions by simulated farmers that reduce the adverse impact of agricultural production on water quality in the face of global climate change induced shifting frequencies and intensities of extreme events (e.g., floods and droughts). State-Action-Reward pairs exploiting the conflict between the agent goal to maximize profit while minimizing their impact on environment are configured in this DRL-ABM. Experimental simulations manipulate incentives (taxes and subsidies) endogenously offered by policy agents to farmer agents, frequencies of extreme events and the memory of farmer agents to these extreme events that can cause direct damage to their agricultural production in the absence of adaptation actions. We also test variations in learning related hyper-parameters for homogeneous and heterogeneous configurations of intelligent agents. From the experimental simulations, we discover that adoption of adaptation actions is highly sensitive to the thresholds of incentive

关键词： Deep reinforcement learning Intelligent agents Agent based models Integrated socio-environmental systems

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Content-Based Tagging and Recommendation System for Tamil Songs Based on Text and Audio Input

Content-Based Tagging and Recommendation System for Tamil So...

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Senthilkumar, Radha Raghavasimhan, T., V Tejeshwini, R. S. Kavipriya, C. Jayanthi, P. Anna Univ Dept Informat Technol Chennai 600044 Tamil Nadu India

ISBN: (纸本)9789819984756;9789819984763

Music recommendation systems play a crucial role in delivering personalized music suggestions by leveraging algorithms to analyze user data. However, the challenge of data sparsity arises when users have limited listening history or ratings. Content-based filtering involves extracting high-dimensional audio feature vectors, enabling comparisons with similar tracks based on audio characteristics unlike collaborative methods rely on user feedback, considering factors like listening duration, usage time and location, and genre/artist popularity. Concerns regarding user privacy and trust have driven a focus on content-based tagging and retrieval, incorporating music and lyrics. This work proposes a content-based approach for music tagging and retrieval through music segmentation and lyrics-based tagging. Particularly, this work focuses on Tamil songs and attempts to cater to the large and diverse audience of Tamil songs. The music segmentation is carried out through a model that utilizes a Gaussian Mixture Model along with an encoder-decoder architecture trained on MUSDB18 dataset which produces a high Source Artifact Ratio (SAR) of 11.004 in the vocal component. The model provides a significant improvement in the Signalto-Distortion ratio compared to leading models. The system also allows the user to enter a text prompt and lists the five most relevant song, allowing the user to exercise more control in the recommendations provided, while ensuring the user's privacy. In order to quantify the relevance of the recommendations, two measures are introduced, namely VibeMatch and ExploreMatch.

关键词： Music recommendation Content-based tagging Music retrieval Tamil music Music autotagging

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MOVRO: Loosely Coupled EKF-Based Monocular Visual Radar Odometry 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Stironja, Vlaho-Josip Persic, Juraj Markovic, Ivan Petrovic, Ivan Univ Zagreb Fac Elect Engn & Comp Lab Autonomous Syst & Mobile Robot LAMOR Zagreb Croatia

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

Reliable and accurate ego-motion estimation constitute an integral part of any autonomous system. To achieve this, it is essential to integrate sensors of complementary characteristics while also minimizing costs. The most often used ego-motion estimation setup usually consists of a monocular camera and an inertial measurement unit (IMU) which can often drift due to inaccuracy of modest performance IMUs. Inlieu of enhancing such setups with a higher-performing IMU, in this paper we propose to fuse the monocular camera with a radar sensor. Specifically, we combine measurements from a 3D millimeter-wave frequency modulated continuous wave radar and a monocular camera within an error-state extended Kalman filter framework. The filter was implemented using loose coupling that allows for an independent and scalable development of sensor models. We evaluated the proposed method against state-of-the-art radar inertial, visual inertial, and radar visual inertial odometries. The results show that the proposed method is comparable with other multi-domain solutions, especially in outdoor sequences.

关键词： Radar visual odometry Error state extended Kalman filter Multisensor fusion

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Internet of Things Platform as a Service for Building Digital Twins and Blockchain

Internet of Things Platform as a Service for Building Digita...

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Intelligent systems Conference (IntelliSys)

作者： Sukaridhoto, Sritrusta Prayudi, Agus Al Rasyid, Muhammad Udin Harun Wicaksono, Hendro Politekn Elekt Negeri Surabaya Jl Raya ITS Surabaya 60111 Indonesia Constructor Univ Bremen Sch Business Social & Decis Sci Campus Ring 1 D-28759 Bremen Germany

ISBN: (纸本)9783031477140;9783031477157

The growth of the Internet of Things (IoT) technology stimulates the growth of the IoT platforms used for developing, managing, controlling, and monitoring physical devices. Many companies have developed physical devices that are ready to use along with an IoT platform. However, it is hard to integrate it with other physical devices from different platforms and other technologies such as blockchain and digital twins (DT). This paper proposes a conceptual model of a new IoT platform architecture with high interoperability and flexibility to connect with other technologies, such as blockchain and digital twins. We analyzed and compared several IoT platforms based on the seven layers architecture to identify the functionality gaps. The gaps are the basis of developing the new IoT platform architecture.

关键词： Internet of Things IoT platform architecture Blockchain Digital twin Conceptual model

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Predictive Savings Service

Predictive Savings Service

引用

Computing Conference

作者： Kazakov, Dimitar Nikolov, Ventsislav Hakbah Ltd Alwan Ctr King Abdulaziz Rd Riyadh 13321 Saudi Arabia Tech Univ Varna 1 Studentska St Varna 9010 Bulgaria

ISBN: (纸本)9783031622717;9783031622694;9783031622687

This paper highlights the persistent concern of lowpersonal and household savings across various demographics globally. The factors leading to this deficit include stagnant or low wages, high levels of personal debt, cost of living increases, economic instability, and lack of financial literacy. The paper proposes a machine learning driven solution to address prevalent lack of financial education and indebtedness by implementing a two-strategy approach. The first strategy involves analyzing past cash flow and determining earning power, liabilities, and outflows. The second strategy entails implementing a segmentation engine that takes into account various individual characteristics to determine the future likelihood of certain cash-intensive events occurring. By combining both data analytics streams, the proposed solution provides a predictive savings service that can anticipate upcoming events with a degree of likelihood and allocate in advance the finances required to meet those future obligations. The report outlines various algorithms that can be used for cash flow forecasting and recommends the use of Open Banking as a reliable source of customer financial data. The proposed solution is expected to provide short and long-term debt reduction and reduce personal financial risk of bankruptcy. Continuous optimization and feedback will be essential in keeping the cash flow forecasting models up to date.

关键词： Prediction Cash flow Software services

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B2B E-commerce in Europe 10th

B2B E-commerce in Europe

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Dudic, Branislav Spalevic, Velibor Comenius Univ Fac Management Bratislava 82005 Slovakia Univ Business Acad Fac Econ & Engn Management Novi Sad 21000 Serbia Univ Montenegro Biotech Fac Podgorica 81000 Montenegro

ISBN: (纸本)9783031662706;9783031662713

Nowadays, electronic commerce has created many opportunities not only for companies but also for consumers. E-commerce is used a lot today and it is assumed that its use will increase in the future as well. Companies use e-commerce in order to sell their products or services and generate as much profit as possible. Business-to-business (B2B) - describes the electronic exchange of products, services and information between businesses. B2B operation refers to business transactions between businesses, such as manufacturers selling to distributors, wholesalers selling to retailers, or companies providing services to other companies. In the future, around 85% of B2B sales interactions between suppliers and buyers will take place in digital channels.

关键词： e-commerce business-to-business (B2B) market share development

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Emotional Landscape of Social Media: Exploring Sentiment Patterns 5th

Emotional Landscape of Social Media: Exploring Sentiment Pat...

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Jain, Kapish Panwar, Deepak Saini, G. L. Kumar, Sandeep Manipal Univ Jaipur Jaipur Rajasthan India CHRIST Deemed Univ Sch Engn & Technol Dept Comp Sci & Engn Kengeri Campus Bangalore 560074 Karnataka India

ISBN: (纸本)9789819720811;9789819720828

Sentiment analysis, a pivotal research area, involves exploring emotions, attitudes, and evaluations prevalent in diverse public spheres. In the contemporary era, individuals extensively share their perspectives on various subjects through social media platforms. Twitter has emerged as a prominent microblogging site, facilitating users to express opinions and insights globally. However, disrespectful or unfair comments have prompted specific platforms to restrict user comments, highlighting the need to foster productive discourse on social media. This study addresses this imperative by analyzing sentiments using data from Twitter. This work employed various deep learning algorithms and methods to classify elements as negative or positive. The Sentiment140 dataset, sourced from Twitter, serves as the training data for the models to identify the most accurate classification approach. By delving into sentiment analysis on Twitter, the study contributes to a better understanding of the nuances of online expressions. It aims to enhance the overall quality of discourse in social media.

关键词： Sentiment analysis Social media Twitter Tweets Positive Negative Neutral Deep learning

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Bilinear Pooling of Transformer Embeddings for Blind Image Quality Assessment 1

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9th International Congress on Information and Communication Technology (ICICT)

作者： Feng, Yeli Nanyang Technol Univ Singapore Singapore

ISBN: (数字)9789819735594

ISBN: (纸本)9789819735587;9789819735594

Blind image quality assessment finds its practical usage in real-world applications where image distortions are more complex than computer generated synthetic distortions, but high-quality images are not available for reference. In the past decade, research in blind quality prediction has advanced tremendously thanks to the success of convolutional neural networks. However, it is far from human-like performance and remains a challenging research problem. For the first time, this paper investigates the potential of imagenet pre-trained Vision Transformer, a new generation architecture for image understanding, in providing better quality aware features. This paper proposed BPTIQ, a method that leverages multi-level transformer embeddings with bilinear feature pooling and non-monotonic error regularization for blind quality assessment of authentic distortions. The effectiveness of the proposed method was evaluated with four IQA databases with authentic distortions. Experimental outcomes and ablation studies show that the performance of BPTIQ is competitive with nine state-of-the-art IQA methods in comparison that mainly utilized pre-trained convolutional neural networks for feature extraction. BPTIQ performed the best over two of the four single databases and demonstrated a more robust cross-database generalization capability.

关键词： Vision transformer Bilinear pooling Blind image quality assessment Authentic distortions

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