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Journal of Network Intelligence 2023年第1期8卷 76-88页

作者： Wang, Jinshui Chen, Junyan Ou, Xuewen Han, Qingfeng Tang, Zhengyi School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China China National Petroleum Corporation China

Automatic Essay Scoring (AES) is one of the significant and challenging research topics in the Natural Language Processing(NLP) area. However, existing AES models majorly consider features derived from vocabulary while failing to integrate sentence and chapter information from multi-level perspectives. In this study, we proposed a multi-level feature fusion model, which was used to capture the multi-level features from different perspectives to improve the accuracy of scoring. Our model consisted of three components to respectively capture the vocabulary-level, sentence-level, and chapter-level features, which were then fed into a CNN and BiLSTM network for the final essay scor-ing. The results show that the proposed model outperforms a set of state-of-the-art AES models on the dataset of the Kaggle Automated Student Assessment Prize (ASAP) com-petition. The average Quadratic Weighted Kappa (QWK) value reaches 0.816, which verifies the efficacy of the model in the task of automated essay scoring. © 2023, Taiwan Ubiquitous Information CO LTD.

关键词： Automation

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Anomaly detection using Federated Learning: A Performance Based Parameter Aggregation Approach 3

Anomaly detection using Federated Learning: A Performance Ba...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Bhat, Pushkar Manohara Pai, M.M. Pai, Radhika M Manipal Institute of Technology Department of Information and Communication Technology Manipal India Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350338607

Anomaly detection is a significant challenge that exists in multiple industries, including healthcare, finance, and manufacturing. Machine Learning techniques are extensively being used to solve these problems. However, data privacy concerns and regulations are making acquiring the necessary data for model training increasingly challenging, particularly in sensitive sectors. Federated learning has emerged as a promising solution, allowing local models to be trained on edge devices using local datasets and model parameters to be shared with a central server for aggregation. This allows the models to reap the benefits of the distributed data without actually sharing any data. However, the current aggregation method used in federated learning is susceptible to models trained on bad data, which can impact the accuracy and reliability of the resulting model. To address this issue, the study proposes a new filtered aggregation algorithm that greatly mitigates the effect of models trained on bad data. The proposed algorithm accounts for models that cannot detect anomalous patterns and gives them much less representation in the final model, resulting in improved accuracy and reliability. © 2023 IEEE.

关键词： Anomaly detection

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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A Mobile App for Seed Grading by Incorporating Deep Learning Approach with Bio Inspired Optimization Technique 7

A Mobile App for Seed Grading by Incorporating Deep Learning...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Mundada, Monica R. Sowmya, J.B. Anurag, G. Mani, G.M. Narayana, Satya Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Ramaiah Institute of Technology Department of Computer Science Bengaluru India

ISBN: (纸本)9798350343144

One of the main production stumbling blocks that hinders the growth of healthy crops is seed quality. The ability to cultivate more nutrient-dense, higher-yielding crops that can help feed a growing population is made possible by high-quality seeds, which are crucial for ensuring food security. As a result, it is crucial to quickly and accurately identify high-quality seeds. Manually identifying the type of wheat requires skill and time. It gets difficult to manually differentiate seeds from an array when they all seem so identical. This prompted us to develop an app that would automate the process of determining seed quality. With the corn seed data set, a convolutional neural network is trained. By eliminating the thick layers, the characteristics of this neural network are recovered. The moth flame optimization technique, a bio-inspired algorithm, is used to reduce noise and extraneous data. The KNN classifier is used to assess the fitness of the features that have been chosen. Lastly, the KNN classifier is used to categorize the seeds based on the fewest attributes © 2023 IEEE.

关键词： Convolutional neural networks

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PATTERNS UI, AN INTERACTIVE TOOL FOR MUSIC EXPLORATION 21

PATTERNS UI, AN INTERACTIVE TOOL FOR MUSIC EXPLORATION

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21st Sound and Music Computing Conference, SMC 2024

作者： Sweeney, Rory Jajoria, Pushkar Diamond, Danny D'Aquin, Mathieu McDermott, James Data Science Institute School of Computer Science University of Galway Galway Ireland Laboratoire Lorrain de Recherche en Informatique et Applications Institut des Sciences du Digital Université de Lorraine Nancy France

ISBN: (纸本)9789893520758

Patterns UI is a novel user interface designed for the exploration of trends within symbolic music corpora through shared melodic patterns. It is an open source tool that caters to musicologists, musicians and enthusiasts, enabling users to discover, compare, and visualize melodic patterns in European folk music. These patterns are represented as short, integer n-grams of accented notes, based on previous musicological research. A central feature is an interactive network visualisation showing tunes, patterns, and the relationships among them, as well as tune families. The interface also features a fuzzy search for tune titles, a search by pattern, and a metadata search. Patterns UI was co-designed and then evaluated with potential users, including musicologists. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

关键词： Open systems

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A RECEPTIVE ENHANCED BASED MULTI-SCALE FEATURE FUSION NETWORK 18

A RECEPTIVE ENHANCED BASED MULTI-SCALE FEATURE FUSION NETWOR...

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18th International Conference on Interfaces and Human computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Mao, Guojun Zhang, Xinchao Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (纸本)9789898704580

Camouflaged Object Detection (COD) aims to identify target objects with high similarity to the surrounding environment in complex scenes, and has high application value in military, medical and other fields. This paper proposes a Receptive enhanced based Multi-scale feature fusion network, termed as RMNet. Firstly, it uses the popular pyramid visual network PVTv2 for feature extraction. By overlaying the target area amplification module OAA and deep interaction guidance module DIDM on it, the position ability for camouflaged objects in complex scenes is improved;Secondly, design an enhanced receptive field module REM to enhance the edge perception ability of camouflaged objects;Finally, the feature fusion module MCFM are designed through cross scale hierarchical connections between adjacent levels, effectively improving the accuracy of camouflage object detection. The presented model is evaluated on three highly challenging benchmark datasets, including CAMO, CHAMELEON and COD10K. The evaluation results show that compared with the current popular methods, our method demonstrates superior performance. © 2024 Interfaces and Human computer Interaction. All Rights Reserved.

关键词： Feature extraction

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TriBoost and Beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector Machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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AgriLLM:Harnessing Transformers for Framer Queries 3

AgriLLM:Harnessing Transformers for Framer Queries

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3rd Workshop on NLP for Positive Impact, NLP4PI 2024, held in conjunction with the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal India Department of Data Science & Computer Applications Manipal Academy of Higher Education Manipal India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9798891761759

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Modeling Speech Emotion Recognition via ImageBind representations

Modeling Speech Emotion Recognition via ImageBind representa...

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2023 International Symposium on Green Technologies and applications, ISGTA 2023

作者： Chakhtouna, Adil Sekkate, Sara Adib, Abdellah Team Data Science & Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Hassan II University Mohammedia Morocco Higher National School of Arts and Crafts Hassan II University Casablanca Morocco

Speech Emotion Recognition (SER) refers to the ability of Machine Learning (ML) and Deep Learning (DL) techniques to accurately predict people's emotional states from speech signals. Significant progress has been achieved in the SER domain involving the incorporation of DL models to introduce novel features extraction processes. This paper introduces the use of deep representations learned from the multi-modal Large Language Model (LLM) called ImageBind. These representations were subsequently provided as input to the Nu-Support Vector Machine (Nu-SVM) with RBF kernel for the classification task. The experiments were executed using the IEMOCAP database within the context of a Speaker-Dependent (SD) scenario. The method achieved a noteworthy overall accuracy rate of 80.58% for the four emotions of IEMOCAP, representing a substantial improvement over well-established methods in the existing body of literature. Thus, affirming that the proposed methodology, founded upon ImageBind representations, introduces a novel perspective to the field of SER. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Support vector machines

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