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Construction of quantitative prediction model of metal mineral resources based on big data mining 3

Construction of quantitative prediction model of metal miner...

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3rd international conference on Computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

作者： Fu, Taiyu Chongqing Test Center of Geology and Minerals Chongqing400042 China

ISBN: (纸本)9781510672925

With the vigorous development of big data technology, the application of data mining program to complete quantitative prediction and evaluation of mineral resources has become a new trend in the development of mineral exploration industry and an important way to promote the digital transformation of traditional prospecting technology. In this regard, this paper will combine geological theory analysis and data mining algorithm to build a quantitative prediction model of metal mineral resources, realize the analysis, processing and visual interpretation of deep geoscience data, and provide necessary data science support for actual mineral exploration. The prediction model takes the machine learning algorithm as the core, and relies on the class libraries such as Numpy, Pandas and Sklearn in Python environment to complete the development and training. Combined with the spatial attribute database in ArcGIS, the prediction elements and importance evaluation indicators are obtained, and the final quantitative prediction results are obtained, and the result evaluation is completed. Practice has proved that the quantitative prediction model of mineral resources based on machine learning algorithm has high prediction accuracy, and the visual results can clearly show the high probability mineral resources areas, which has high practical promotion value. © 2024 SPIE.

关键词： Minerals

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Natural Language Processing for Sentiment Analysis with Deep learning 3

Natural Language Processing for Sentiment Analysis with Deep...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sruthi, Srigiri Trinath, V.G. Jayanth, V. Balaji, V. Pavan Singh, Tripty Mandal, Adhirath Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India Pai Chai University Korea Republic of

ISBN: (纸本)9798350381931

In order to identify and analyse the thoughts, feelings, and sentiments indicated in textual data, the field of sentiment analysis is crucial. Deep learning models have become effective tools for sentiment analysis thanks to their capacity to recognise semantic links and understand complicated patterns. In-depth analysis of the use of deep learning algorithms for sentiment analysis tasks is presented in this research report. We investigate different deep learning architectures, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models, to assess how well they are able to capture nuances in sentiment and contextual information. On benchmark datasets, numerous trials are run, and the models are assessed using important performance indicators as accuracy, precision, recall, and F1 score. applications that are in this field. we also investigate methods like pre- trained word embeddings and transfer learning. The study also investigates the effects of regularisation and hyperparameter tuning methods on the accuracy and applicability of models. In addition, we discuss issues with sentiment analysis's class imbalance, model interpretability, and data pre-processing. The findings show that deep learning models regularly perform better than conventional machine learning techniques, attaining cutting-edge performance in sentiment classification tasks. The results of this study open the door for more advanced applications in this field by giving important new insights into the capabilities and constraints of deep learning models for sentiment analysis. © 2024 IEEE.

关键词： Sentiment analysis

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Analysis of machine learning Models for Wearable Devices Centric Human Activity Recognition 3

Analysis of Machine Learning Models for Wearable Devices Cen...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Zara, Namia Jalal Rifat, Rashiqur Rahman Tasnim, Jarin Baker, Md. Khan, Riasat North South University Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350375190

This work presents a comprehensive study on applying machine learning models for human activity recognition. The increasing number of wearable devices and the growing interest in health monitoring and intelligent environments have fueled the demand for robust and accurate systems that automatically recognize human activities. This study explores and compares the performance of different machine learning (ML) models in human activity recognition. This study intends to identify the most effective ML model to classify and predict human activities accurately. In this study, various models are evaluated, including ensemble-based classifiers such as Decision Tree, Random Forest, K-Nearest Neighbor, Logistic Regression, AdaBoost algorithm, and XGBoost algorithm. All models have been evaluated using the publicly available dataset 'Human Activity Recognition with Smartphones,' which captures six daily activities: Lying, Standing, Sitting, Walking, Walking Upstairs, and Walking Downstairs. Among all the applied models, XGBoost accomplished the highest accuracy of 99.52% with 100% precision, 100% recall (100%), and 1.0 F1 score. Hyperparameter tuning on the ML models is implemented to attain the best accuracy. © 2024 IEEE.

关键词： Adaptive boosting

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Enhancing Startup Matchmaking through machine learning Technological Innovations and Applications 3

Enhancing Startup Matchmaking through Machine Learning Techn...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Astriyani, Erna Oganda, Fitra Putri Tarigan, Riswan E. Khanza, Aulia Yulian, Firdaus Dwi Sadan, Sadan University of Raharja Department of Information System Tangerang Indonesia Alphabet Incubator Department of Information System Tangerang Indonesia Pelita Harapan University Department of Information System Tangerang Indonesia Pandawan Sejahtera Indonesia Department of Digital Business Tangerang Indonesia Kreatif Desain Department of Information Technology Tangerang Indonesia Cai Sejahtera Indonesia Department of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

The entrepreneurial landscape has evolved significantly, particularly in the startup matching process where innovative companies connect with partners, investors, and resources. Integrating machine learning in this process can revolutionize startup matchmaking by providing data-driven insights and enhancing decision-making capabilities. This study explores the paradigm shift in startup matching due to machine learning, focusing on factors influencing its acceptance. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Technology Acceptance Model (TAM) were used to examine data from online questionnaires intended for potential entrepreneurs using SmartPLS software. The findings show that intentions to utilize machine learning-enhanced matchmaking services are highly influenced by perceived utility, simplicity of use, social influence, and enabling conditions. Statistical tests confirm the validity of these variables. The findings highlight the significant impact of machine learning on startup matchmaking, suggesting that understanding technology acceptance can enhance the effectiveness and efficiency of these platforms. Future research should explore the long-term effects and continuous advancements in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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A Signal Processing and machine learning Framework for Enhanced TDS Simulation 3

A Signal Processing and Machine Learning Framework for Enhan...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shuvo, Shuvendu Pal Pal Shibazee, Shirshendu Khuki, Arifa Akter Das, Chaitee Paul, Goutam Mita, Mitaly Paul Khulna University of Engineering and Technology Dept. of CE Khulna9203 Bangladesh Bangladesh University of Engineering and Technology Dept. of EEE Dhaka1000 Bangladesh Khulna University of Engineering and Technology Dept. of MSE Khulna9203 Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of Pharmacy Gopalganj8100 Bangladesh Khulna University Dept. of Economics Khulna9208 Bangladesh

ISBN: (纸本)9798331534400

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features;hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes. © 2024 IEEE.

关键词： Empirical mode decomposition

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Research on the Application of College English Teaching System Using Random Forest 3

Research on the Application of College English Teaching Syst...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Huang, Xiaojing Dou, Anqi Hainan Vocational University Of Science And Technology Foreign Language Teaching Department Haikou China

ISBN: (纸本)9798350318609

With the growth of language research and teaching, people realize that the grammatical competence is a significant thing of the communicative competence. Presently, there are various Artificial Intelligence (AI) assisted educational applications in schools as well as universities. This research utilizes an AI approach integrated with the knowledge recommendation to the system as well as develops an online English teaching system. The system of English teaching is helpful in determining the potential as well as internal connections among the estimation results as well as different factors. In this research, the machine learning (ML) approach of Random Forest (RF) is proposed for the College English Teaching System. The proposed approach initially performed the correlation analysis on the developed estimation data. After that, the integration principles were followed to estimate the coordination among indexes in the estimation of the teaching. The proposed RF approach attains the interaction analysis of 98.2 %, 96.3%, 95.2 %, 97.2 % and 96.2 % in the number of students of 20,40, 60, 80 and 1 00 respectively. © 2024 IEEE.

关键词： machine learning

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Prediction of Software Cost Estimation Using Stacking Ensemble learning method 3

Prediction of Software Cost Estimation Using Stacking Ensemb...

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3rd international conference on machine learning and data engineering, icmlde 2024

作者： Qassem, Nedaa Thamer Saleh, Ibrahim Ahmed Software Department College of Computer & Math University of Mosul Iraq Software Department College of Computer & Math University of Mosul Iraq

The process of accurate cost estimation is one of the important factors for the success of software projects, so it is necessary to understand the process of estimating the cost and planning in advance to manage the project positively and effectively, which in turn helps the project manager and the client to determine the work budget. This paper introduced Ensemble learning method of stacking. This method works in two stages: the first is to train many individual models called models with basic learners, and the second is to collect model expectations from the first stage as an introduction to an intermediate model called the meta-model that works on the final prediction. The following models (Random Forest (RF), Linear Regression (LR), AdaBoost, XGBoost, Gradient Boost, and K_Nearest Neighbor (K_NN)) are basic learners for the stacking method. The learners were trained on the ISBSG dataset from the ISBSG repository. At the beginning of the work, the dataset was processed to obtain high prediction accuracy, and the characteristics that suit the cost estimation process were determined. The proposed method was applied to it, and the model performance was evaluated using the following accuracy measures (MAE, RMSE, R-squared, MMRE). The results of implementing the stacking model gave a prediction accuracy of 98% and a lower error rate compared to the individual models by 0.0927, thus proving its superiority over the individual models by increasing the estimation accuracy. The results of implementing the algorithm for the accuracy measures, respectively (207,533,0.983,0.0927). The proposed method contributes to producing a software development cost estimation tool with an accuracy of 98%, which in turn helps estimators in pre-estimating the cost and effort with high accuracy, which leads to the success of the work. © 2025 The Author(s).

关键词： Software design

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Heart Disease Prediction Using machine learning Algorithms: Performance Analysis 3

Heart Disease Prediction Using Machine Learning Algorithms: ...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Saha, Subrata Rahman, Md. Motinur Suki, Tahmid Tamrin Alam, Md. Mahbub Alam, Md. Shah Haque, Mohammod Abu Sayid Bangladesh Atomic Energy Commission Institute of Electronics Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078. © 2024 IEEE.

关键词： Forecasting

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Plant Leaf Disease Detection and Classification Using MobileNetV3 and Wavelet Kernel Extreme learning machine 3

Plant Leaf Disease Detection and Classification Using Mobile...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shruthi, B.S. Narasimha Murthy, M.S. Prakash, Chidurala Sai Riad Al-Fatlawy, Ramy Sundaram, S. Meenakshi Malnad College Of Engineering Department Of Information Science Hassan India Bms Institute Of Technology And Management Department Of Computer Science And Engineering Bangalore India Sr University Department Of Electrical And Electronics Engineering Warangal India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

Detection of plant disease is the most important aspect in agriculture, aimed at identifying and diagnosing disease that affect plants. If the plant disease detected earlier, that helps to avoid risk from damaging plants and the soil. However, there is a difficulty to differentiate the diseased and healthy leaves due to similarity of disease symptoms. To solve this problem, an effective Wavelet Kernel Extreme learning machine (WKELM) classifier is proposed for distinguishing diseased and healthy plants. Images from the PlantVillage dataset are preprocessed using Bilateral filter and then segmented by Adaptive Gaussian Threshold (AGT) to separate the diseased portion from leaf. MobileNetV3 a Convolutional Neural Network (CNN) used for effective feature extraction with the hyper parameters which are tuned using effective Emperor Penguin Optimizer (EPO) and classification is done by the WKELM for accurate results of plant disease. The experimental results of the proposed model performed by the evaluation metrics achieved accuracy of 99.7%, recall of 99.6%, precision of 99.6%, F1-score of 99.7% which is greater than other existing deep learning models like CNN, Transfer learning with Deep CNN models, CNN - Visual Geometry Group (VGG). © 2024 IEEE.

关键词： Convolutional neural networks

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