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CKD Detection in Patients with Reproductive Health Challenges Utilizing Ensemble Models in Machine learning 11

CKD Detection in Patients with Reproductive Health Challenge...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Chandralekha, E. Saravanan, T.R. Vijayaraj, N. SRM Institute of Science and Technology Dept of Computational Intelligence Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Dept. of CSE Chennai India

ISBN: (纸本)9798350308433

Infertility and Chronic Kidney Disease (CKD) have garnered global recognition as significant health challenges, and their interconnected nature has been acknowledged over time. Infertility has affected numerous couples worldwide, while CKD is notorious for its harmful effects on kidney function and overall health. the relationship between these conditions is strengthened by shared factors such as chronic illnesses, lifestyle choices, and CKD's impact on reproductive health. Our research relied on extensive clinical data collected from hospital databases, which provided a wealth of information encompassing both CKD and infertility. the primary goal of this study was to identify CKD among individuals who were already dealing with infertility and to conduct factor-wise analysis. To achieve this objective, we employed a diverse set of machine learning models, including Random Forest, AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost, for diagnosing CKD. Essential data preparation steps, such as encoding and scaling features, played a pivotal role in the research process. After thorough training and evaluation, our findings indicated that CatBoost emerged as the most effective model, demonstrating superior accuracy and ROC curve performance. these results suggested that CatBoost had the potential to facilitate early CKD detection among individuals facing infertility-related challenges. © 2024 IEEE.

关键词： Adaptive boosting

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An Area Restriction Scheme Based on TAC Control Policy for 5G Private Network 11

An Area Restriction Scheme Based on TAC Control Policy for 5...

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11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Li, Wenwen Yan, Ming Chan, Chien Aun Wang, Changling Cai, Qingyu 5G Application Innovation Center China Unicom Digital Technology Co. Ltd Beijing China School of Information and Communication Engineering Communication University of China Beijing China The University of Melbourne Department of Electrical and Electronic Engineering VIC Australia

ISBN: (纸本)9798350311594

With the rapid development of 5G technology, industry users in the process of accelerating to the digital intelligent transformation of the network coverage quality, delay, uplink capacity and stability and other performance has an urgent need. 5G private network has technical characteristics such as open network architecture, virtual intelligent orchestration of network elements, and air interface capabilities. It can provide a dedicated network with differential performance, autonomy and control, agility and security on the basis of meeting the requirements of network performance indicators. In this paper, we discuss an area restriction technology based on tracking area code (TAC) control policy for 5G private network. Based on the design of 5G private network architecture and TAC control policy, we focus on proposing the related 5G private network area restriction technology. this scheme can provide a unified data interaction channel for all kinds of wireless intelligent devices, and continuously integrate all kinds of sensing terminals with sensing and monitoring capabilities into all aspects of the industrial production process. Finally, a typical application scenario is presented to demonstrate the effectiveness and safety of the proposed scheme. © 2023 IEEE.

关键词： Network security

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Improving Lung and Colon Cancer Detection using Ensemble Method Approach 18

Improving Lung and Colon Cancer Detection using Ensemble Met...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Syal, Jessica Singh Jain, Achin Dubey, Arun Kumar Jain, Vanita Bharati Vidyapeeth's College of Engineering Dept. of Information Technology New Delhi India University of Delhi Department of Electronic Science New Delhi India

ISBN: (纸本)9789380544519

Cancer is recognised to represent an extremely high risk of mortality, despite enormous developments having been made in science and medicine. Characterized by widespread metastases, malignant cells spread rapidly and evade drugs, making it a fatal disease with little treatment success. Cancer cells have a heterogeneous nature that makes them resistant to chemotherapy and other forms of radiation. Across the globe, cancer stands to be the second most leading cause of death. Among the many types, lung and colon cancer are the most common and have the highest mortality rate. Early and accurate detection of tumor cells in lung and colon cancer patients can help the medical industry increase patient survival statistics. this study focuses on improving the current state of technology assisted lung and colon cancer detection. A large dataset of 25,000 histopathological photographs of lung and colon tissues is analyzed to build a Deep-learning model using the Ensemble Method approach for accurate and reliable cancer detection. To increase efficiency, the photos are divided into a total of five different classes. the methodology underlying the study aims to increase the detection accuracy by building a model which learns from pre-existing models in the field;thus displaying superiority in terms of predictive power. the core concept of transfer learning is used to leverage the knowledge of pre-trained models and create better and improved ensemble models. the study includes comprehensive data preprocessing, augmentation, model training, validation and testing, and model performance evaluation. With a high accuracy of 0.96, this model achieved high reliability in detecting cancer cells. this effort holds the potential to improve cancer diagnosis through efficient and accurate classification of medical images. Using pre-trained models is an efficient and effective approach to reduce the time and resources required to develop high-accuracy models. © 2024 Bharati Vidyapeeth, New

关键词： Classification Models Colon cancer Deep learning Histopathological Images Lung and Colon Disease Lung Disease Machine learning Transfer learning

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TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS 11

TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS

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11th international conference on learning Representations, ICLR 2023

作者： Guo, Ruchi Cao, Shuhao Chen, Long Department of Mathematics University of California Irvine United States Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City United States

A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. the new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks. this research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Inverse problems

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Detection of Chicken Diseases from Fecal Images with the Pre-Trained Places365-GoogLeNet Model 12

Detection of Chicken Diseases from Fecal Images with the Pre...

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12th IEEE international conference on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Cinar, Ilkay Selcuk University Department of Computer Engineering Konya Turkey

ISBN: (纸本)9798350358056

A variety of chicken diseases pose significant challenges to chicken farmers worldwide, posing a threat to the safety of food and potentially resulting in economic losses. In this study, we propose the utilization of the pre-trained deep learning model, Places365-GoogLeNet, for the detection of chicken diseases from chicken fecal images, including Healthy, Coccidiosis, Salmonella, and New Castle Disease. By leveraging the powerful image analysis capabilities of deep learning, our approach achieves a remarkable classification accuracy of 98.91%. this accuracy surpasses the results reported in related studies in the literature. Moreover, our findings highlight the potential of artificial intelligence and machine learning techniques, particularly in the agricultural sector, for automated disease detection. the presented results not only contribute to early disease diagnosis and prompt intervention in poultry farming but also pave the way for future research to develop more advanced methods and utilize larger and diverse datasets to enhance the model's generalization ability. © 2023 IEEE.

关键词： automatic disease detection chicken diseases deep learning fecal images GoogleNet

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Identification And Detection Of Tomato Plant Disease From Leaf Using Deep Reinforcement learning 9

Identification And Detection Of Tomato Plant Disease From Le...

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9th international conference on Smart Computing and Communications, ICSCC 2023

作者： Musthafa, Najila Jemsheer Ahmed, P. Muhammed Salah, K.T. Muhammed Afnan, F. Mohammed Shibilmon, O.T. Mohammed Shameel, P. Computer Science and Engineering Mea Engineering College Perinthalmanna India

ISBN: (纸本)9798350314090

this study introduces a novel deep reinforcement learning method for the automated diagnosis of tomato plant diseases. the proposed method analyses leaf photos to accurately distinguish between healthy and diseased tomato leaves and identify the particular illness affecting the plants by combining Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) within the Deep Q-Network (DQN) agent framework. the study makes use of an online platform called PlantVillage that provides expertly curated images of healthy and diseased crop plant leaves. this dataset is the result of a sustained crowdsourcing initiative to support computer vision techniques for addressing yield losses brought on by infectious diseases in crop plants. the automated diagnosis tool aims to decrease crop losses and boost tomato plant productivity, providing farmers with an effective and affordable solution. the deep reinforcement learning approach allows for model training even with a small amount of labelled data, making it especially appropriate for environments with limited resources. By outlining a cutting-edge and effective method that has the potential to improve farmers' livelihoods and support sustainable food supply chains, this study makes a contribution to the field of plant disease detection. © 2023 IEEE.

关键词： Convolutional neural networks

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A Behavioral Recognition Method with data Fusion and Supervised learning for Tele-Assistance Systems 11th

A Behavioral Recognition Method with Data Fusion and Supervi...

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11th international conference on E-Health and Bioengineering (EHB)

作者： Soviany, Sorin Gheorghe, Cristina-Gabriela Paraschiv, Elena-Anca Bica, Ovidiu Natl Inst Res & Dev Informat ICI Commun Digital Applicat & Syst Dept Bucharest Romania

ISBN: (纸本)9783031625220;9783031625237

the paper presents a learning-based method for human behavioral pattern/activity recognition with design for tele-assistance systems. the method includes a behavioral classification component aiming to make reliable predictions or to early detect abnormal cases potentially revealing the health degradation for the individual. the functional architecture contains Feature engineering (FE) techniques besides the typical processing steps. Adding a FE step helps to enrich the original feature space with new informative variables in order to discover hidden patterns within data, with significant added value for the human behavioral pattern recognition.

关键词： Feature Fusion Tele-assistance Behavior Recognition

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Optimizing Algorithms for Human Behavior Recognition: Single-Modality Spatiotemporal Convolutional Networks 11

Optimizing Algorithms for Human Behavior Recognition: Single...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Zhao, Rui Hao, Jutao Feng, Zhikun Wang, Xianzhi Prasad, Mukesh Huo, Huan School of Computer Science University of Technology Sydney Faculty of Engineering and IT Sydney Australia School of Electric Information Engineering Shanghai Dianji University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331531904

Behavior recognition constitutes a pivotal area of research within computer vision and serves as an integral technology in video surveillance and related domains, thereby possessing substantial research value. Convolutional neural networks (CNNs) have been proven to possess robust learning capabilities. However, due to the high complexity and variability of human behavior, recognizing human behaviors in videos remains challenging due to the difficulty of capturing useful features over long time series and the redundancy of multi-frame information. To address these issues and improve the utilization of continuous frame information, we have designed a cross-frame multiscale information fusion module. this module allows the backbone network to acquire multiscale temporal features from different frames, thereby reducing information redundancy across multiple frames while minimizing additional computational costs. Furthermore, to mitigate the problem of video temporal feature loss, we introduce an easily integrable attention mechanism module, the Easy-Attention Block. this module facilitates rapid capture of long-range dependencies, enabling the model to focus on the most relative parts of continuous video sequences and suppress irrelevant temporal information. Consequently, the model can extract more discriminative spatial-temporal features, thereby enhancing its efficiency in handling long sequence data. Experimental results express that this model can achieve high recognition accuracy and effectively extract the temporal features of video, suitable for real-time applications in critical infrastructure surveillance, which enhancing safety and reducing potential risks in energy power systems. © 2024 IEEE.

关键词： Spatio-temporal data

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Underwater Fish Identification in Real-Time using Convolutional Neural Network 7

Underwater Fish Identification in Real-Time using Convolutio...

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7th international conference on intelligent Computing and Control Systems, ICICCS 2023

作者： Aruna, S.K. Deepa, N. Devi, T. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Artificial Intelligence (AD is the wide application that learns the problem and features by given data and processes the data like the human brain. When a computer program imitates a characteristic of the human brain that is considered 'innovator.' Among the methods are statistical methods, methods for artificial intelligence, and traditional order to verify the validity. the expansion of AI is also related to virtually infinite storage and an abundance of data, including exchanges, geospatial information, video files, photos, text messages, and audio files. Machine learning is divided into deep learning and deep learning is primarily divided into numerous layers of neural networks. this pattern gives it the ability to learn a lot of information and attempt to replicate the brain function. Increasing the efficiency by attaching more covert layers can be beneficial. It is used to gather the data and transfer the data. Aquaculture production has grown into a barrier to the growth of fish culture and the counting operation represents one of the problems experienced during the spawning process. Previous studies have primarily relied on the application of manual and automated counting techniques, which has prevented it from producing accurate results. the proposed method offers a promising method for enhancing image detection by combining the IoT techniques. the image data were divided into three categories: low frequency, intermediate density, as well as high frequency. the proposed method has used 8200 images to train and 2500 images for verification. Only the data relevant data sources were used during the train and verification phase in order to find the proper parameters and create a better VGG19 parameter calibration strategy. Consequently, the improved VGG19 model can achieve an accuracy of 98%. © 2023 IEEE.

关键词： Image enhancement

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Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition 11

Informative Speech Features based on Emotion Classes and Gen...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Yildirim, Huseyin Ediz Iren, Deniz Open Univ Netherlands Informat Sci Heerlen Netherlands

ISBN: (纸本)9798350327458

Emotions manifest in various aspects of human speech. While the tonality of the speech is a crucial indicator of emotions, other aspects such as word selection, pronunciation, and other paralinguistic features also provide valuable insights. Some of these aspects are considered universal, others are influenced by cultural and personal aspects, with gender being one of the most significant factors affecting emotional expressions. In this study, we aimed at investigating the effect of gender on emotional descriptors in speech. Specifically, we used intelligible paralinguistic speech features in Speech Emotion Recognition and employed Shapley values to measure the effect of gender on speech features. Furthermore, we empirically evaluated whether a reduced set of informative features could provide sufficient information for emotion recognition. Additionally, we investigated how gender influences auditory expressions of emotions. Our experiments show that besides the physical impact on fundamental speech frequencies, gender also affects how emotional phrases are spoken, and how prosody and phonology change. In addition to that, reducing the input size using the feature informativeness does not have a significant effect on the model accuracy whereas it shrinks the input size drastically by 98% on average. Finally, our comparative experiments on genders show that some speech features are more informative for capturing particular emotions exhibited by different genders. therefore, we report that with a multi-layer feature set that consists of obscure and interpretable paralinguistic features, a novel data fusion approach could yield an explainable speech emotion recognition model. Furthermore, it is possible to reduce the input size and computational requirements by implementing feature reduction and gender information for speech emotion recognition tasks.

关键词： speech emotion recognition affective computing explainable machine learning feature selection

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