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检索条件"任意字段=International Conference on Machine Learning and Computing"

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RFECV-DT: Recursive Feature Selection with Cross Validation using Decision Tree based Android Malware Detection 15

RFECV-DT: Recursive Feature Selection with Cross Validation ...

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

作者： Awasthi, Naimisha Gautam, Prateek Raj Sharma, Anuj Kumar Aktu Centre for Advanced Studies Department of Computer Science and Engineering Lucknow India Upes School of Computer Science Dehradun India Aktu Centre For Advanced Studies Department of Mechatronics Lucknow India

ISBN: (纸本)9798350370249

The internet is extremely valuable, but it is also vulnerable to assault. Malware is a persistent and dynamic danger. Malware assaults entail introducing malicious code into software. The task was handled using a variety of machine learning approaches. Previous research utilized various methods, such as chi-square, Fisher's score, and Forward/Backward methods. A recent work used a wrapper strategy of Recursive Feature Elimination with Cross-Validation using the nonlinear Support Vector machine (SVM) as an estimator. This lowered the number of features from 215 to 74, yielding an accuracy percentage of 95.58%. Our solution improves on the previous strategy by using the same dataset and a decision tree with Recursive Feature Elimination with Cross-Validation using Decision Tree to identify 53 out of 215 feature. Classifiers other than Naive Bayes and Logistic Regression are all obtaining the highest accuracy of 98%. This paper decreases the number of features by using Gini Impurity and Cross Validation for detection of Android Malware. © 2024 IEEE.

关键词： Decision Tree Feature Importance Feature Selection machine learning Malware Detection Recursive Feature Elimination

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Grid Search Hyperparameters Tuning with Supervised machine learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

引用

2nd international conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Application of machine learning Algorithms in Digital Twin Monitoring Systems: An Overview of Approaches, Methods, and Prospects 3

Application of Machine Learning Algorithms in Digital Twin M...

引用

3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Amirkhanova, Gulshat Amirkhanov, Bayurzham Tyulepberdinova, Gulnur Ishmurzin, Timur Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798331529222

Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in industrial, urban, and transportation domains under the paradigms of Industry 4.0, the Industrial Internet of Things (IIoT), and smart cities. However, effectively harnessing the torrents of time-series data and complex system states integral to Digital Twins (DTs) requires advanced analytical tools. machine learning (ML) methods—ranging from classical ensemble models to deep learning architectures and reinforcement learning—provide powerful mechanisms for anomaly detection, failure prediction, operational optimization, and scenario modeling. This review explores the essential ML algorithms, their application contexts, and the supportive infrastructures required for implementing ML-driven DT solutions. We also discuss interpretability techniques, address security and concept drift challenges, examine the role of MLOps in ensuring reliable deployment, and highlight future research directions, including physics-informed ML, multimodal data integration, and advanced transformers for time-series forecasting. ©2024 IEEE.

关键词： Predictive maintenance

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Strength of Ensemble learning in Voting Classifier for Crop Yield Prediction 15

Strength of Ensemble Learning in Voting Classifier for Crop ...

引用

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

作者： Tripathi, Deeksha Biswas, Saroj K. Das, Akhil Kr. Bhattacharya, Arijit Purkayastha, Biswajit Department of Computer Science and Engineering National Institute of Technology Silchar India Department of Computer Science Gour Mahavidyalaya West Bengal India

ISBN: (纸本)9798350370249

Crop Yield Prediction (CYP) plays a vital role in mitigating emerging food security challenges, particularly when the rapid shifts in global climate patterns occur. machine learning (ML) is crucial for CYP because it can estimate the crop production based on different characteristics such as environmental characteristics, soil fertilizers, and crop management. Researchers have used lots of ML techniques with distinct kinds of data analytics techniques. Thus, this study proposes an ensemble model, named Voting-based Ensemble Expert System for Crop Yield Prediction (VEESCYP) to predict the best crop yield for the Assam state of India. This study tries to present the strength of a voting classifier with Ensemble learning (EL) approaches. The proposed VEESCYP model has performed some data preprocessing tasks before classification, such as the Multiple Imputation by Chained Equation (MICE) to treat the missing values, Isolation Forest (IF) for outlier detection, Genetic Algorithm (GA) for feature selection and Robust Scaling (RS) for data normalization. To show the strength of EL techniques in voting classifier, the proposed VEESCYP model compares with some standard ML algorithms, a Voting-based EL technique using standard ML algorithms and some existing models presented in the literature. © 2024 IEEE.

关键词： Adversarial machine learning

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The Analysis of machine learning Algorithms in Facial Identification

The Analysis of Machine Learning Algorithms in Facial Identi...

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2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Liu, Yanchen Nankai University College of Cyber Science Tianjin300000 China

ISBN: (纸本)9798350313314

Since seventies in last century, face recognition has been one of the pivotal and popular field in computer science field. And the traditional algorithms like SVM and KNN have almost been replaced by the deep neural networks, which in need of large datasets and calculating forces. However, not all situations in our daily life fit to this elephantine process. And this paper is aimed to remind people re-concentrate on the traditional features attracting algorithms. Thus, this paper determines to compare the performances of several classic methods of machine learning algorithms in classifying and identifying the faces from data set. And somehow optimize the primitive algorithm models to reach higher accuracy. And then this paper compares the performance of prevailing and advanced method, convolutional neural network model, with such original models, making some conclusions about the processes of machine learning models development. © 2023 IEEE.

关键词： Face recognition

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An Intrusion Detection System for Multiclass Classification Across Multiple Datasets in Industrial IoT Using machine learning and Neural Networks Integrated with Edge computing 2

An Intrusion Detection System for Multiclass Classification ...

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2nd international conference on Data, Information and computing Science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

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machine learning for Breast Cancer Detection 4

Machine Learning for Breast Cancer Detection

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kaushik, Priyanka Singh, Sangeeta Goyal, Priyanshi Singh, Vinay Deb, R.K. Steffi, T. Chandigarh University Dept. of Cse Chandigarh India Madhav University Dept. of Cse Pindwara India Mangalayatan University Dept. of Pharmacy Aligarh India Usha Martin University Dept. of Computing & It Ranchi India Lingayas Vidyapeeth University Dept. of Mechanical Engineering Faridabad India Dhanalakshmi Srinivasan University Dept. of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350364729

The increasing number of breast cancer-related deaths annually underscores the pressing need for improved prediction and diagnostic techniques. machine learning offers a promising avenue for enhancing early detection and treatment planning. In this study, we applied various machine learning algorithms - such as K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, Decision Tree (C4.5), and Support Vector machine (SVM) o the Breast Cancer Wisconsin Diagnostic dataset. Through comprehensive evaluation and comparison of these classifiers, our primary objective was to determine the most effective method in terms of confusion matrix performance, accuracy, and precision. The results revealed that the Support Vector machine achieved the highest accuracy at 97.2%, outperforming all other classifiers. The entire analysis was conducted using the Python programming language in Jupyter Notebook, leveraging various Python libraries including Scikit-learn, Pandas, and Numpy. © 2024 IEEE.

关键词： Random forests

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Technical Analysis and Comparison of Intrusion Detection Systems Using Hybrid machine learning Approach 1

Technical Analysis and Comparison of Intrusion Detection Sys...

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1st international conference on Data, Computation and Communication, ICDCC 2024

作者： Sharma, Vishwas Shah, Dharmesh J. Gautam, Sunil Verma, Ravi Sankalchand Patel University Faculty of Engineering and Technology Gujarat Visnagar India Indrashil University Faculty of Ingineering and Technology Gujarat Kadi India Nirma University Department of Computer Science & Engineering Gujarat Ahmedabad India School of Computing Science & Engineering VIT Bhopal University M.P Sehore India

ISBN: (纸本)9798331532956

Intrusion Detection Systems (IDS) are critical components in securing network environments against a myriad of cyber threats. The evolution of machine learning (ML) techniques has significantly enhanced the capabilities of IDS, offering improved detection accuracy, adaptability, and real-time analysis. This research covers a variety of machine learning approaches, such as supervised and hybrid strategies. We assess these models' performance using important measures including computational efficiency, precision, recall etc. The results show that although supervised machine learning models provides high accuracy, but when used in hybrid model including Random Forests and SVM improves performance. The result is a hybrid model that leverages the strengths of each approach. For instance, Random Forest can provide a robust feature representation, while SVM can refine the decision boundary, leading to a more accurate and reliable classification model. This combination often yields better performance than using any single algorithm alone. This paper underscores the importance of selecting appropriate ML techniques tailored to specific network environments and threat landscapes. © 2024 IEEE.

关键词： Semi-supervised learning

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machine learning Based Online English Teaching Monitoring Task System 2

Machine Learning Based Online English Teaching Monitoring Ta...

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2nd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Song, Yiwen Shandong Institute of Commerce and Technology International Exchange Department Shandong Jinan250103 China

ISBN: (纸本)9798350339765

Traditional English listening teaching mainly relies on teachers personally providing oral explanations and demonstrations, and students improve their listening skills by imitating the teacher's pronunciation and speaking speed. However, this method has many limitations, such as limited time and energy for teachers, which makes it difficult to meet the personalized needs of all students. This paper designs a monitoring task system for online English teaching based on machine learning. By collecting data related to English listening learning, preprocessing and feature extraction are carried out on these data to obtain data sets for training and evaluating machine learning models. The system can generate customized listening learning tasks for students according to their individual needs and learning progress. When the system is applied to teaching, it is found that the average score of students has increased by about 8.2%. The application of the system can improve students' enthusiasm and their satisfaction. The online English teaching monitoring task system has brought substantial improvements to English listening teaching and can help students learn better. © 2023 IEEE.

关键词： Data acquisition

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Comparative Analysis of machine learning and Deep learning Algorithms for Plant Disease Classification and Detection 2

Comparative Analysis of Machine Learning and Deep Learning A...

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2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Kaur, Harminder Raheja, Neeraj Bindal, Amit Kumar Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798331519056

Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL - primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artif

关键词： Fuzzy neural networks

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