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Auto Labeling for Liver CT Image Based on Edge Detection and Region of Interest Segmentation 16th

Auto Labeling for Liver CT Image Based on Edge Detection and...

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16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Horng, Jun-Yan Ho, Jiun-Huei Lo, Chun-Chih Shieh, Chin-Shiuh Horng, Mong-Fong Department of Electronic Engineering National Kaohsiung University of Science Kaohsiung Taiwan Cheng Shiu University Kaohsiung Taiwan

ISBN: (纸本)9789819615346

This study proposes an automatic labeling method for liver regions in Computed Tomography (CT) images, addressing the time-consuming nature of manual medical image labeling. The approach utilizes edge detection to identify overall edge distributions in CT images, constrains the region of interest to locate characteristic liver areas, and performs object segmentation to generate liver masks for labeling. The method tackles key challenges in liver CT image analysis, including image noise, blurred edge features, and minimal pixel differences in suspected tumor areas. It employs Gaussian blur for noise reduction, followed by brightness and contrast adjustments to enhance edge characteristics. The watershed algorithm is then applied to segment the complete liver contour. Performance evaluation using approximately 17,000 images from the LiTS datasets demonstrated an average accuracy of 75% for liver labeling, with a maximum of 80% and a minimum of 62%. The process achieves a labeling speed of about 1 s per 100 images, highlighting its efficiency. The proposed method shows promise in automating liver labeling in CT images, potentially streamlining the image analysis process in medical research and diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Noise abatement

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Semantic Attribute Mapping for Zero-Shot Object Classification in Deep Learning Networks

Semantic Attribute Mapping for Zero-Shot Object Classificati...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Agarwal, Pragya Reddy, B. Ravali Shakir, Sarah Abbas Raju, Hemanth Kumari, B. Geetha Kansal, Lavish Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq New Horizon College of Engineering Bangalore India Griet Department of Cse Bachupally Telangana Hyderabad India Lovely Professional University Phagwara India

ISBN: (纸本)9798331527495

One of the successful approaches to deal with the issue in object classification is the ability of the model to recognize categories that it has not seen before, referred to as the zero-shot learning or ZSL. This work aims at training and testing ZSL methods on a standard benchmark known as Animals with Attributes 2 (AwA2). In this work, CNN, to be precise ResNet is used for feature extraction after which a semantic attribute of each class is used for classification. These then are the semantic attributes of objects The ZSL model aligns the visual features from unseen objects to these attributes for classification. Evaluations based on top 1 and the top-5 accuracy to know if the model is likely to generalize to other classes. To ensure the system is computationally efficient and scalable, TensorFlow/Keras is used for the CNN features, and Hugging Face transformers for attribute features. © 2025 IEEE.

关键词： Zero-shot learning

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Impact of Data Preprocessing on Wind Forecasting Using Machine Learning Techniques 4

Impact of Data Preprocessing on Wind Forecasting Using Machi...

引用

4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Gaikwad, Nidhi Haribhau Mitra, Arghya Payal, Jangilwar Das, Soumyabrata Keshri, Ritesh kumar Department of Electrical Engineering VNIT Nagpur India

ISBN: (纸本)9798331530402

There is a high demand for renewable energy sources as non renewable energy sources are harmful to the environment due to its effects such as global warming. Among non renewable energy source, the wind energy is unpredictable in nature, making Its integration with the grid a concern. By accurately forecasting wind energy, the stability of the grid can be ensured. The aforementioned can be achieved with the help of Machine Learning (ML) techniques. This paper investigate four ML techniques Multiple Linear Regression, Support Vector Regression, Random Forest Regression, and Decision Trees which are utilized and compared based on the metrics such as root mean square error, mean square error, mean absolute error, and R2 for three different cases. Initially errors are calculated without data preprocessing and later with data preprocessing. It is observed that preprocessing reduces error. This paper also investigates the effect of different seasons on the errors generated by these ML techniques. For this research, data is taken from National Solar Radiance database (NSRD) over two year period from January 2018 to December 2019. © 2025 IEEE.

关键词： Multiple linear regression

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Recurrent Mutual Attention Network with Transformer and LSTM Integration for Radiology Report Generation

Recurrent Mutual Attention Network with Transformer and LSTM...

引用

international conference on Applied Nonlinear Dynamics, Vibration and control, ICANDVC 2024

作者： Li, Zhenhao Wang, Chao Zhang, Yi Lin, Shen Yang, Xiaohui Geng, Haokun College of Information Science and Engineering University of Jinan Jinan250024 China Shandong Provincial Maternal and Child Health Care Hospital Jinan250014 China Jinan Authority Hospital Jinan250001 China Jinan Key Laboratory of Intelligent Medical Frontier Applications in Artificial Intelligence and Automation Jinan250001 China

ISBN: (纸本)9789819633166

Generating radiology reports by analyzing medical imaging data is becoming increasingly important not only in the research field but also in clinical practice. The automatic generation of accurate and coherent reports is of paramount importance to enhancing the efficiency and quality of medical diagnosis. Due to the rapid development of deep learning, automatic radiology report generation has evolved from simple feature transformation to cross-modal interactions between images and text. However, the correspondence between image regions and words remains unclear. Therefore, this paper proposes a recurrent mutual attention network that queries image features through text and then queries corresponding text features through image features. These features are then input into the report generation module. To enrich the semantic information, the long short-term memory (LSTM) is connected with the Transformer through gate control, enabling the automatic generation of radiology reports. The experimental results show that, compared with the other published methods, our proposed method has significantly improved the diagnostic report generation in quality, accuracy, and natural language fluency. Although our method has only been tested in radiology, it has shown potential usability in a wider range of different types of medical imaging analysis, due to its design principles and versatility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mammography

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Enhancing Emotion Recognition from Speech Signals Using Deep Learning and Open-Source Datasets

Enhancing Emotion Recognition from Speech Signals Using Deep...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Anandhi, R.J. Kansal, Lavish Singh, Jay Subhashini, P. Jabbar, Fatimah Dhari Priya, R. Sathiya Prasanna, J. Lakshmi New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this work, we focus on emotions from speech signals using deep learning methods on the RAVDESS database. The audio signals are preprocessed by means of sampling, normalization and the subsequent feature extraction in the form of Mel Frequency Cepstral Coefficients (MFCCs). Classification of emotions is then performed using a Convolutional Neural Network often abbreviated as CNN. The model's performance is therefore measured with performance metrics including accuracy, precision, recall, and F1 score, to give a measure of the model's capabilities in detecting emotions from voices. The results of the confusion matrix analysis help to understand better the ability of the model to predict different emotions. The analysis proves the significance of deep learning approaches, for example, CNNs that significantly outperform classical approaches in terms of emotion recognition rates. The study also finds out the audio features, which have very high impact in the classification of emotions and contains finding that will be useful for further improvements of affective computing. © 2025 IEEE.

关键词： Convolutional neural networks

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Electric Vehicles: Types, Advantages, Difficulties, and Possible Remedies for Broad Adoption: A Review

Electric Vehicles: Types, Advantages, Difficulties, and Poss...

引用

2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Singh, Devendra Singh, Shashi Kant Singh, Satendra Singh, Udayvir Singh, Bholey Gl Bajaj Group of Institutions Department of Mechanical Engineering Mathura281406 India Gl Bajaj Group of Institutions Department of Applied Science Mathura281406 India

ISBN: (纸本)9798331527495

Due to the rise in fuel prices and the toxic emissions released by burning fossil fuels in traditional cars, the use of clean energy in the transportation sector has received a lot of attention during the past 20 years. Since they don't produce greenhouse gas emissions, electrical vehicles (EVs) could replace cars with internal combustion engines (ICEs). However, because to factors including low battery capacity, lengthy charging times, and a lack of infrastructure for charging, the automotive industry has been restricted to EVs with short range. The types, benefits, and challenges are covered in this article. The efforts made by different government agencies throughout the world to promote EV adoption are also covered, as are potential solutions for widespread adoption. © 2025 IEEE.

关键词： Electric bikes

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Conversational Explanations: Discussing Explainable AI with Non-AI Experts 25

Conversational Explanations: Discussing Explainable AI with ...

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30th international conference on Intelligent User Interfaces, IUI 2025

作者： Zhang, Tong Zhang, Mengao Low, Wei Yan Yang, X. Jessie Li, Boyang Albert College of Computing and Data Science Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore College of Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9798400713064

Explainable AI (XAI) aims to provide insights into the decisions made by AI models. To date, most XAI approaches provide only one-time, static explanations, which cannot cater to users' diverse knowledge levels and information needs. Conversational explanations have been proposed as an effective method to customize XAI explanations. However, building conversational explanation systems is hindered by the scarcity of training data. Training with synthetic data faces two main challenges: lack of data diversity and hallucination in the generated data. To alleviate these issues, we introduce a repetition penalty to promote data diversity and exploit a hallucination detector to filter out untruthful synthetic conversation turns. We conducted both automatic and human evaluations on the proposed system, fEw-shot Multi-round ConvErsational Explanation (EMCEE). For automatic evaluation, EMCEE achieves relative improvements of 81.6% in BLEU and 80.5% in ROUGE compared to the baselines. EMCEE also mitigates the degeneration of data quality caused by training on synthetic data. In human evaluations (N = 60), EMCEE outperforms baseline models and the control group in improving users' comprehension, acceptance, trust, and collaboration with static explanations by large margins. Through a fine-grained analysis of model responses, we further demonstrate that training on self-generated synthetic data improves the model's ability to generate more truthful and understandable answers, leading to better user interactions. To the best of our knowledge, this is the first conversational explanation method that can answer free-form user questions following static explanations. © 2025 Copyright held by the owner/author(s).

关键词： Data assimilation

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Employing Machine Learning to Drive Innovative Strategies for Enhanced Organizational Performance

Employing Machine Learning to Drive Innovative Strategies fo...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Gurulakshmi, A.B. Vybhavi, Gummgatta Yajaman Singh, Jay Prashant, Kumar Srinija, K. Jabbar, Fatimah Dhari Kansal, Lavish New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Grjet Department of Cse Telangana Hyderabad India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798331527495

TalentForgeML is up against six other HR management methods that are thought to be the best in the business. People in business think these tactics are the best. Here are a few examples of these plans: the IntelliHire method and the ClassicScreen method. TalentForgeML is the name of the method that combines HR management with machine learning. The goal of this study is to investigate the methods so that we can better understand them. Our main goal is to come up with new ideas that will help our company run more efficiently. The Inclusive Workplace (IWP) program, the Competence Acquisition (PTA) method, and the Dynamic Skill Development (DSD) algorithm are thought to be three of the most important algorithms in TalentForgeML. © 2025 IEEE.

关键词： Human resource management

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Decentralized Optimal Tracking control of MRMs Under Health Indicator-Based Event-Triggered Mechanism 3rd

Decentralized Optimal Tracking Control of MRMs Under Health...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Ma, Bing Zhang, Hangwei Pan, Qiang An, Tianjiao Dong, Bo Li, Yuanchun Changchun University of Technology Changchun China

ISBN: (纸本)9789819616930

A novel event-based decentralized optimal control is designed to handle the modular robot manipulators (MRMs) with health indicator. Firstly, the dynamics of the MRMs is established. Secondly, the developed switching threshold event-triggered mechanism is proposed. Considering the optimal tracking control performance and health status of the system, an improved value function is proposed, which is composed of the system state, control law, fault observations and health state indexes. And the health status indexes are obtained from the health monitoring of the subsystem by utilizing event-triggered health indicator. Then, an event-based critic-only neural network neural dynamic programming (NDP) algorithm is utilized to implement the event-triggered approximate optimal controller design. Finally, the Lyapunov-based stability analysis demonstrates that the MRMs is uniformly ultimately bounded. And the experimental results show that the proposed method performs effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Modular robots

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