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PMTL: Personality-Aware Multitask learning Model for Emotion Recognition in Conversation 6

PMTL: Personality-Aware Multitask Learning Model for Emotion...

引用

6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Li, Xiaonan Song, Dandan Zhou, Yanru School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350353174

In the field of affective computing, Emotion Recognition in Conversations (ERC) has gained increasing attention. The relationship between emotions and personality traits during conversations is a complex challenge. However, most conversational context models in ERC do not consider the speaker's background. Few works focus on personality traits and their impact on emotion recognition. To address this, we introduce PMTL, a multitask learning model that synergizes emotion recognition with sentiment analysis. PMTL incorporates personality traits into the emotional transition modeling process, leveraging the VAD emotional space to map contextual interdependencies and speaker-specific nuances. Our model outperforms existing models on benchmark datasets. Through an ablation study, we validate the efficacy of each component within PMTL, thereby demonstrating the significance of incorporating personality traits into emotion recognition tasks. © 2024 IEEE.

关键词： Contrastive learning

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Feature Enhanced Semi-Supervised Attention U-Net for Cell Segmentation 15

Feature Enhanced Semi-Supervised Attention U-Net for Cell Se...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Raj, Ardra S. Asha, S.B. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cell segmentation is crucial for accurately assessing disease severity. Despite advances in deep learning, there is still a gap in understanding how U-Net variants perform in addressing the issue of limited labeled data in cell segmentation. We compare various U-Net models to see how they fare with live cell images. We also examine how different feature extraction methods affect their performance. Using the Laplacian and Standard Deviation features improves performance by more than 3%, giving Attention U-Net mean Intersection over Union (IoU) scores of 0.7956 and mean Dice scores of 0.8844. In addition, we introduce a semi-supervised Mean Teacher Attention U-Net that leverages the same feature extraction techniques. Remarkably, it achieves an IoU score of 0.7908 and a Dice score of 0.8831, outperforming the existing approaches even with only 60% labeled data and utilizing limited computational resources. ©2024 IEEE.

关键词： Deep learning

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Neural Networks-Based Predictive Models for Self-Healing in Cloud computing Environments 2

Neural Networks-Based Predictive Models for Self-Healing in ...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Sujatha, D. Michael Raj, T.F. Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. St.Peters College of Engineering and Technology Department of Computer Science and Engineering Avadi Chennai India Sclas Smats Department of Computer Applications TamilNadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350306415

Due to the widespread adoption of cloud computing, protecting the security and uptime of cloud services is of crucial importance. Unforeseen malfunctions and performance bottlenecks in cloud settings can lead to considerable downtime and financial losses. Systems that can automatically detect, diagnose, and recover from errors using self-healing mechanisms are a promising new approach. This study investigates how neural network-based prediction models can be used to improve cloud-based systems' inherent capacity for self-healing. This research introduces a novel method of using neural networks to forecast cloud system failures and performance irregularities. The proposed prediction models are able to spot warning signs by assessing past data and current metrics in real time. Neural networks may accurately anticipate outcomes by recognizing complicated patterns and learning from large, varied information. Various forms of neural networks, including deep learning designs, are evaluated to determine their efficacy in diverse contexts. The study also looks into proactively reducing faults by incorporating these predictive models into self-healing systems. Responses, such as resource reallocation, workload migration, and backup procedures, are triggered automatically when problems are predicted. This preventative method drastically lessens the amount of time users spend waiting for their cloud-based systems to restart. Extensive simulations and real-world case studies yielded experimental data that prove the prediction models based on neural networks proposed for self-healing in cloud computing are effective. The models' proficiency in failure prediction enables corrective measures to be taken promptly and precisely. Implementing these predictive models in massive cloud environments is also discussed, as are their scalability, flexibility, and real-world consequences. by tapping into the potential of neural networks for predictive analysis, this study adds to the develop

关键词： Cloud computing LSTM Neural Network Self-Healing

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Machine learning Based Crop Recommendation System

Machine Learning Based Crop Recommendation System

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Deo, Arpit Bandhu, Kailash Chandra Litoriya, Ratnesh Gupta, Abhay Chelawat, Purvi Gohade, Purvi Medi-Caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

In a country where conventional farming methods are still widely used and often result in small yields and little profit for farmers, our goal is to increase crop yields by presenting a range of farming techniques through a recommendation system for intelligent farming. This system uses a scientific method to forecast the best crops depending on a number of important factors, such as the nutrients in the soil. It pinpoints nutritional deficits in the soil for particular crops. Our system, which is implemented as a website, makes use of crop dataset that include parameters for temperature, rainfall, pH, and humidity. It then applies a variety of ML algorithms to make accurate and efficient crop recommendations, achieving impressive metrics such as accuracy of 96.08%, precision of 93 %, and recall of 45%. © 2024 IEEE.

关键词： Crop Fertilizer Nitrogen Potasium Rainfall Recommendation

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An Overview of ML Techniques for Timely Recognition of Voice Maladies

An Overview of ML Techniques for Timely Recognition of Voice...

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

作者： Ajznblasm, Zaid Kumar, G. Anil College of technical engineering The Islamic University of Al Diwaniyah Department of computers Techniques engineering Al Diwaniyah Iraq College of technical engineering Saveetha University Department of computers Techniques engineering India

ISBN: (纸本)9798331527495

Humans have an astounding ability to control loudness and tone through their complex voice production system. The vocal cords are vulnerable to damage from both internal and external factors, which can alter a person's voice. Both the physiological and psychological aspects of the body are impacted by these changes. To help patients cope with potential consequences and enhance their quality of life, it is crucial to quickly identify any changes in voice quality. Considering that this substantially improves our understanding of these disorders, it is imperative that machine learning methodologies be applied to automatically diagnose voice abnormalities. Computational strategies to aid clinicians in the early diagnosis of speech anomalies have been the attention of many investigations in recent years. An exhaustive assessment of previous work in the area is the goal of this project, which will target in on automated voice disorder identification and how efficiently it can distinguish between different kinds of vocal impairments. The investigation entails looking into different databases, feature extraction procedures, and ML approaches that have been executed in these studies. © 2025 IEEE.

关键词： Speech enhancement

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teaching learning-Based Optimization with learning Enthusiasm Mechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals 7th

Teaching Learning-Based Optimization with Learning Enthusias...

引用

7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： John, Nirmala Janamala, Varaprasad Rodrigues, Joseph Karnataka Bangalore560 074 India

ISBN: (纸本)9789811954818

This study presents the optimal placement and operation of distributed generation (DG) sources in a distribution system embedded with utility-owned DG sources. Cost minimization and technical improvement of the network are the key objectives of the distribution company (DisCo). With the increasing popularity for renewable energy sources, DisCos are installing their own DGs to fulfill their electricity demand partially. When DisCos are the DG owners, the technical and economic considerations overlap. A novel method is proposed in this paper based on the recent variant of the teaching learning-based optimization (TLBO) algorithm and learning enthusiasm-based TLBO (LebTLBO) to optimize locations, sizes, and operational power factors of DGs in a distribution system with DisCo-owned DGs. A multi-objective function to improve voltage stability, reduce distribution losses, and reduce energy costs has been considered for solving the problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electric power distribution

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A Novel Machine learning-Based Framework for Predicting Diabetes in Healthcare

A Novel Machine Learning-Based Framework for Predicting Diab...

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

作者： Oliver, A. Sheryl Gupta, Kimmi Singh, Jagendra Agnihotri, Atul Kumar Vasavi, M. Kaur, Gaganjot Srm Institute of Science and Technology Department of Computational Intelligence Chennai India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Electronics and Communication Engineering Kanpur India Cvr College of Engineering Department of Et -Cyber Security Hyderabad India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798331527495

The rise in diabetes is considered the greatest global health issue;thus, the call for early and accurate predictions of the disease is a dire need in the health sector. The detection of the disease in its initial stages would allow patients to undergo timely interventions and appropriate treatments. A novel approach for the prediction of stages and severity of diabetes by utilizing various models within a machine learning framework, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision tree learning (DT), and Random Forest (RF) is proposed in this paper. The training data would involve the history of medical conditions, age, BMI, insulin, skin thickness, glucose level, the number of pregnancies, and existence or non-existence of diabetes. The models were trained on 70% of the data and tested on the remaining 30%. The results show that SVM outperforms all other models, achieving an accuracy of 96.5%, followed by ANN with 94.5%, DT with 90.2 %, and RF with 86.7%. These results indicate that SVM, followed by ANN, is the most effective model for predicting diabetes stages. The accuracy and reduction of data loss have been constant improvements with epochs 30 to 360;therefore, models can be used in real-time applications in the clinical area. Thus, this research concludes that machine learning models, such as SVM, may be helpful in predicting and diagnosing early cases of diabetes;thus, these models could be helpful in making good decisions on healthcare and improving the patient's health. © 2025 IEEE.

关键词： Diseases

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Performance Comparison of Transfer learning Models for X-ray Medical Image Classification 4

Performance Comparison of Transfer Learning Models for X-ray...

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4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Rautaray, Gadadhar Behera, Dayal Kumar Kumar Dash, Sanjeev Mohanty, Jiten Kumar Kumar Behera, Ranjit Gourisaria, Mahendra Kumar Department of Computer Science and Engineering Silicon University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Department of CSE Silicon University Odisha Bhubaneswar India Department of CSE Silicon University Bhubaneswar India School of Computer Engineering KIIT University Odisha Bhubaneswar India

ISBN: (纸本)9798331515720

After the COVID-19 pandemic, the pneumonia cases increased. So, the number of medical X-ray images is increased for the detection of the disease, but to analyze the image in the necked eye, it requires expertise. There is a vast shortage of experts for analysis. Sometimes experts are failing to detect the disease from the image. So, it needs an automatic and efficient image processing technique. Our proposed model uses transfer learning to classify Covid-19, normal, and pneumonia cases. Feature vectors are extracted using pre-Trained models such as Inception-V3, VGG16, VGG19, and ResNet50. The final layer is used for classification. A total of 6432 X-ray images are used, with 566 Covid-19, 1583 Normal, and 4200 Pneumonia images used for training. Experimental results demonstrate that the VGG16 base model transfer learning approach outperforms the other techniques, achieving an accuracy of 96%. However, we can experiment with the model by taking different datasets. © 2024 IEEE.

关键词： COVID-19

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Predictive Health Analysis: Unveiling Disease Prognosis

Predictive Health Analysis: Unveiling Disease Prognosis

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

作者： Reddy, P. Harsha Vardhan Reddy, L. Snehalatha Ganta, Vamsi Krishna Baduguna, Swapna Maddala, Bhanu Prakash Hariharan, Shanmugasundaram Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9798331527495

This paper presents the development of a health aid platform that uses machine learning to predict diseases based on user-entered symptoms. The platform also integrates a chatbot to give personalized advice on disease precautions and preventive measures. By using datasets from Kaggle, the system trains multiple machine learning models such as Support Vector Classifier, Naive Bayes Classifier, and Random Forest Classifier. The chatbot, developed with natural language processing techniques, enhances user interaction by offering tailored guidance on health management. This project aims to improve healthcare accessibility, enabling users to make informed decisions about their health and take proactive steps to prevent diseases. The architecture of the system is designed to ensure seamless integration and robust performance, providing correct predictions and valuable health insights. Evaluation of the system's accuracy and usability is conducted to confirm its effectiveness in real world scenarios. This innovative health management approach can revolutionize how individuals access and use healthcare information, promoting a proactive and informed approach to personal health. © 2025 IEEE.

关键词： Diagnosis

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Design of a Fair Distributed computing Platform Based on Distributed Ledger Technology and Performance Measurements 1

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13th international conference on Frontier computing, FC 2023

作者： Liao, Bo-Yan Chang, Jia-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

We propose a fair distributed computing platform based on Distributed Ledger Technology (DLT) and performance measurements. The platform integrates DLT and federated learning, enabling users to train machine learning models on their local devices without compromising their privacy by sharing their data with a central server. Instead, only the trained model weights are uploaded to a central server for aggregation. To address privacy concerns associated with federated learning, we integrate various privacy-preserving methods, such as differential privacy, model pruning, and homomorphic encryption, into the platform framework. These techniques help protect user privacy while improving model accuracy. To address the non-IID data problem in federated learning, we use performance measurements to balance the training workload among users, and blacklist malicious users while incentivizing participation. DLT ensures the security and integrity of the platform by validating and recording all data transactions on the ledger. Overall, the proposed platform has the potential to revolutionize machine learning model training by making it more efficient, secure, fair, and transparent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Blockchain

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