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2023 4th international conference on Intelligent Design, ICID 2023

2023 4th International Conference on Intelligent Design, ICI...

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4th international conference on Intelligent Design, ICID 2023

ISBN: (纸本)9798350342574

the proceedings contain 62 papers. the topics discussed include: multi-label text classification based on multi-task dynamic decoding;target protection using multiple unmanned aerial vehicles based on Stackelberg security game;coal grate cleaning device based on YOLOV5s;smart glasses for Alzheimer’s disease;carrot rolling detection based on machine vision;research on the design of guidance service for the elderly under intelligent medical environment;a visual simulation system architecture and implementation method of target damage assessment based on unity 3D interactive technology;financial news sentiment analysis method based on WMSA-Bi-LSTM;evaluation model for public building design based on artificial intelligence algorithms;and application research of computational design aesthetics.

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Epidemic Prediction Algorithm Based on Deep learning and Epidemic Dynamics 4

Epidemic Prediction Algorithm Based on Deep Learning and Epi...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Xu, Bing Chen, Xingyuan Yuan, A. Chen, Zhenting Kunming University Kunming China

ISBN: (纸本)9798400709449

Disease prediction has always been an important research topic. the purpose of this algorithm is to combine climate factors and use deep learning methods to establish a model that can predict the development of infectious disease epidemic. this model focuses on the data of hand-foot-mouth disease as the research object, combining infectious disease dynamics models with deep learning models to learn the historical data of hand-foot-mouth disease and its related influencing factors. By using a fully connected neural network, the infectious disease dynamics model provides key parameters. Finally, based on the infectious disease dynamics model, the model predicts the incidence of hand-foot-mouth disease in the future period and achieves good predictive performance. the aim is to help decision-making departments implement timely prevention and control measures. © 2023 ACM.

关键词： Forecasting

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Soil micronutrient detection using machine learning 4th

Soil micronutrient detection using machine learning

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4th Annual international conference on Data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Dalmeida, Sonal Patreena Kamath, Surekha Udupi India

ISBN: (纸本)9781032426853

this paper presents an algorithm for detecting micronutrients present in the soil using Image Processing and Convolutional Neural Network technique. Soil test is necessary because excessive or lacking use of manure will destroy the crop plant. Huge quantity and less quantity of fertilizer can affect the crop yield. Earlier, farmer used traditional methods to grow the crops to prepare their own farm in the traditional method which they had learnt from their intimates without noticing the micronutrients present in the soil that can change from time to time. this lead to low productivity because the farmer’s did not have the proper understanding of the soil. Nowadays, there are several methods to analyze the soil sample through soil test laboratories but these methods are time consuming and tidous. However, Image Processing and Convolutional Neural Network is used efficiently to determine the micronutrients and pH level of soil i.e., Zinc(Zn), Iron(Fe), pH, Manganese(Mn) and Copper(Cu). the soil images were captured manually from Udupi Soil Health Lab. the system is divided into five sections i.e., Image capturing, Image Pre-Processing, Image Segmentation, Training System and Result. Soil images are captured using Smartphone and are provided as an input to the system, from the input images HSV values (Hue Saturation Value) are extracted and these HSV values(Hue Saturation Value) are given as an input to Convolutional Neural Network and as a result soil micronutrients value of the input image is displayed. Based on the result, the soil micronutrients and Accuracy of the model is calculated and displayed. the dataset is split into training and testing wherein, 80% of data is used to train the model and 20% of data was used for testing the model. this project is implemented on MATLAB. the maximum accuracy of the model obtained was 95%. © 2024 the Author(s).

关键词： Image segmentation

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IHear- Mobile application for Hearing- Impaired Children to Facilitate their Hearing and Speech Abilities 5

IHear- Mobile Application for Hearing- Impaired Children to ...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Koralage, H.C. Liyanage, G.N.J. Senanayake, S.V.M. Mapa, M.H.M.I.D. Attanayaka, Buddhima Mahaadikara, Hansika Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Children with hearing impairments face serious challenges as they are unlikely to develop normal speech and language abilities. It is important to build positive relationships with the external environment to support hearing impaired children's learning;hence, IHear was introduced. 'IHear' is a mobile application meticulously developed that aims to support children with mild to moderate hearing impairments under the age group of 6-10. the IHear mobile application serves as a potent intervention designed to facilitate effective learning while mitigating potential academic setbacks. A positive relationship with the surrounding environment that incorporates learning is crucial for hearing-impaired children since the development aspects of a child are directly connected to external factors. this study investigates IHear's tailored and comprehensive approach to speech and learning development using technology and machine learning. © 2023 IEEE.

关键词： Audition

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Prediction of Fruit Texture Features Using Deep learning Techniques 4

Prediction of Fruit Texture Features Using Deep Learning Tec...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sangeetha, M. Kannan, S. Rajes Boopathi, Sampath Ramya, J. Ishrat, Mohammad Sabarinathan, G. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600069 India Muthayammal Engineering College Department of Mechanical Engineering Tamil Nadu Rasipuram637408 India St. Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600119 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Andhra Pradesh Guntur522502 India Department of Mathematics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350300888

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. the suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. the random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset. © 2023 IEEE.

关键词： Fruits

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application of machine learning Techniques for Software Anomaly Detection 3

Application of Machine Learning Techniques for Software Anom...

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3rd international conference on Applied Mathematics and computer Science, ICAMCS 2023

作者： Gancheva, Veska Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350324266

A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. the usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. this paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. the suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. the proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase. © 2023 IEEE.

关键词： Network security

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YONA: An Intelligence Question-Answering System Based on Artificial Intelligence 4

YONA: An Intelligence Question-Answering System Based on Art...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Huang, Zhanghan School of International Education Guangxi University of Science and Technology Guangxi Liuzhou China

ISBN: (纸本)9798350343113

the purpose of this paper is designing an intelligent question-answering system. In the context of education informatization, university students tend to lack guidance in their studies. this paper proposes an intelligent Q&A system for university teaching that integrates intelligent dialogue and machine learning technologies. the proposed system includes intelligent Q&A module, the tutor recommend & match module, tutor Q&A system module, Q&A forum Module and user compasrison report module. the system utilizes BERT model, collaborative filtering recommendation algorithm, multi-threaded programming, TF-IDF algorithm and K-Means algorithm to implement the modules in the system. the proposed system deployed with Aliyun cloud server, CDN, load balancing, client testing, and online optimization. the pilot of this system demonstrated excellent performance: an average of 4 queries per person, with an average query duration of 7 minutes. the success rate of problem resolution reached 85%, and the average response time was 30 seconds. this system will promote the health of the education industry and enable intelligent learning diagnosis. the authors hope to contribute to improving the quality of life of college students and society by designing an intelligent question and answer system. © 2023 IEEE.

关键词： Informatization

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application of machine learning to Increase the Efficiency of the Global Search Algorithm for Solving Multicriterial Problems 4th

Application of Machine Learning to Increase the Efficienc...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Barkalov, Konstantin Grishagin, Vladimir A. Kozinov, Evgeny Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod Russia

ISBN: (纸本)9783031812408

Decision making models described as problems of multicriterial optimization are very complicated for investigation, because the property of criteria contradictoriness leads to the notion of the solution as the set of non-dominated parameters (Pareto set). the complexity of these problems increases significantly in the case where criteria are multiextremal. In the paper a novel algorithm for multicriterial black-box optimization with multiextremal criteria is considered. this algorithm applies ideas of complexity reduction when the initial multicriterial problem is reduced to a set of univariate scalar subproblems on the base of Peano mapping and maximum convolution. For solving univariate problems an information-statistical global optimization algorithm with guaranteed convergence to global optimum is used. As a core of novelty, this algorithm includes in its computational scheme a machine learning procedure with combination of accumulating the information of solved subproblems that allows one to significantly accelerate building the Pareto set. Effectiveness of the proposed approach is estimated in the representative computational experiment on test sets of multicriterial problems with multiextremal criteria for different dimensions, in comparison with several nature-inspired multiobjective optimization algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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machine learning Applied to Failure Detection 6

Machine Learning Applied to Failure Detection

引用

6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Qi, Wenbin Zhao, Jingyi 567West Yangsi Road Pudong New District Shanghai200126 China

ISBN: (纸本)9781510668546

the operation of distributed systems highly depends on reliable and continuous communication between nodes. the failure detection phase thus plays an important role in system unavailability. Previous studies have shown potentials for LSTM based failure detectors but the heavy computation cost remains a problem. We propose a technique to reduce the computation time of LSTM based FD by performing forecasting instead of making one prediction at a time (i.e. predicting multiple timestamps at once). Our method achieves better computation time compared to previous LSTM FDs but falls short of BFD. We discuss the heavy computation cost of back-propagation of LSTM and the high frequency nature of FD tasks and conclude that LSTM isn't likely to be the best fit for machine learning based FD. © 2023 SPIE.

关键词： Long short-term memory

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Interpretable Liver Fibrosis Classification Using 1D-CNN 4th

Interpretable Liver Fibrosis Classification Using 1D-CNN

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Krishnan, S. Navaneetha Jothi, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Liver fibrosis, characterized by the excessive accumulation of extracellular matrix proteins, represents a significant global health concern with the potential to progress to cirrhosis and hepatocellular carcinoma if left untreated. Early and accurate diagnosis of liver fibrosis is critical for effective management and treatment, yet traditional diagnostic methods, such as liver biopsy, are invasive, costly, and carry inherent risks. this necessitates the development of non-invasive, reliable, and efficient diagnostic tools. this study explores the application of machine learning techniques for classifying liver fibrosis using the Indian Liver Patient Dataset (ILPD). the dataset underwent rigorous preprocessing, including imputation, transformation, and resampling, ensuring robust model training. Several algorithms, including Random Forest Gradient Boosting, XGBoost, Bagging classifier, ExtraTrees Classifier, Stacking Classifier, KNeighbors Classifier, Artificial Neural Networks (ANN), and a 1D Convolutional Neural Network (1D-CNN) were evaluated. the 1D-CNN achieved the highest accuracy of 97.14%, followed by the Stacking Classifier with 96.64%. SHAP (SHapley Additive exPlanations) has been used to interpret the 1D-CNN, providing insights into feature importance. the findings demonstrate the potential of machine learning for non-invasive, efficient, and reliable liver fibrosis diagnosis.

关键词： Liver Fibrosis Deep learning CNN XAI SHAP

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