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College English teaching Ecosystem Based on ML Algorithm 3rd

College English Teaching Ecosystem Based on ML Algorithm

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Nie, Yingbin Yufei, Zhou Peng, Xin Jiangxi University of Applied Science Nanchang China Xi’an Eurasia University Shaanxi Xi’an710000 China

ISBN: (纸本)9783031631290

Mega data application has become the main factor affecting CET, which breaks the balance of the traditional CET ecosystem. Using mega data technology to build an CET ecological environment is the main means to improve the quality of CET. ML is an important way to acquire knowledge and the key to realize artificial intelligence. In modern instructional technology, the rational use of ML can develop an intelligent teaching system. To study the ecological environment of College English is to study the relationship between individual behavior and English learning environment and the relationship between various factors in learning environment. We should make full use of the flexibility, fragmentation and autonomy of mobile learning mode to carry out appropriate flipped classroom teaching. Teachers should adopt various evaluation methods to improve their own quality, such as correcting the network, official account of WeChat, etc., to cultivate students’ awareness of mobile learning strategy, information literacy of learning resources, language practice ability and autonomous learning ability, so as to achieve the goal of CET and promote the sustainable and harmonious development of CET ecosystem. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Ecosystems

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Hyperparameter Tuning Of Machine learning Models: Analysing Student's Depression Data 5

Hyperparameter Tuning Of Machine Learning Models: Analysing ...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Panda, Amiya Ranjan Roy, Sattwik Mishra, Manoj Kumar Sahoo, Soumyashree Mohapatra, Subhransu Gaurisaria, Mahendra Kumar Kiit Deemed to be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

This research focuses on the tuning of the hyper-parameters of some of the selected machine learning models. Tuning the correct hyper-parameter will use three types of searching techniques for the search space of hyperparameters. The dataset has 502 instances and 11 attributes (1 target attribute). It is a balanced dataset. The target attribute is "Depression"(Yes/No). Our objective is to compare the performance of 10 different supervised machine learning classifiers including Random Forest, KNN, SVM, Logistic Regression, Decision Tree Classifier, Naive Bayes, Gradient Boosting Machine, AdaBoost, Feed Forward Neural Network (MLP) and Extra Trees. Once the comparison report is achieved, we select four different classifiers (KNN, SVM, Logistic Regression and Ada-Boost Classifier), create search space of the hyper-parameters for each classifier algorithm. Every search space is then cross validated through split tests, for three different searching algorithm (grid Search CV, random search CV, Bayesin Optimization) to achieve the maximum accuracy and the perfect fit hyper-parameters for the four classifiers. Provided these classifiers are more generalized and less prone to overfitting. There will be visualization charts in the research for visualizing the improving accuracy for each split test. © 2025 IEEE.

关键词： Semi-supervised learning

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PARTICLE SWARM OPTIMIZATION BASED ON SIMULATED ANNEALING RULES

PARTICLE SWARM OPTIMIZATION BASED ON SIMULATED ANNEALING RUL...

引用

2023 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2023

作者： Yu, Meng'ao Chen, Zhong Ding, Linzhi Cheng, Haoyu School of Electrical and Information Engineering Changsha University of Science & Technology Changsha410000 China

ISBN: (纸本)9781510666474

Aiming at the problems of traditional particle swarm optimization (PSO) algorithm such as slow convergence speed and easy to fall into local optimum, this paper proposes to use the core idea of simulated annealing algorithm to improve the particle swarm optimization algorithm. First, the Metropolis criterion is used to select the probability of inertia weight, and for each particle in each iteration, the probability of inertia weight is selected to balance the search ability of particles;Secondly, by analyzing the information exchange of the optimal solution between each iteration, the mutation particles are constructed, and the position information of the next generation of global learning particles is determined through probability selection, which can effectively prevent the particle population from falling into the local optimal region. The simulation results show that the PSO algorithm based on simulated annealing rules has higher convergence accuracy and speed compared with other test algorithms, and can effectively improve the ability of PSO to find the optimal solution. © 2023 SPIE.

关键词： Simulated annealing

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Deep learning Solutions for Real-World Traffic Sign Recognition: A Transfer learning Approach 15

Deep Learning Solutions for Real-World Traffic Sign Recognit...

引用

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

作者： Amruth, A. Ramanan, R. Vimal, C. Beena, B.M. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham karnataka Bengaluru India

ISBN: (纸本)9798350370249

This study addresses the optimization of deep learning and transfer learning models for Traffic Sign Recognition (TSR) under diverse environmental conditions and class imbalances. Traffic Sign Recognition Database (TSRD) is used in this research study. Despite the widespread application of transfer learning in various domains, its implementation in TSR has been relatively unexplored. This research evaluates the efficacy of transfer learning in TSR using three neural network architectures: ResNet-18, ResNet-34, and VGG-19. The study's findings reveal that ResNet-18 outperforms others, achieving an accuracy of 88.47%, precision of 85.20%, and recall of 80.55%, while requiring only 254.136 seconds for training on the A100 GPUs. ResNet-34 also demonstrates considerable efficiency with an accuracy of 87.47%, but at a longer training duration of 372.95286 seconds. In contrast, VGG-19 shows limited suitability for TSR tasks, with a much lower accuracy of 5.32% and the longest training time of 1256.9595 seconds. The results underscore the potential of ResNet-18 and ResNet-34 in TSR applications, highlighting the need for a balanced approach towards accuracy, computational efficiency, and robustness in varying conditions. These insights are significant for advancements in autonomous navigation systems and enhancingroad safety. ©2024 IEEE.

关键词： Contrastive learning

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Enhanced Fish Species Identification Using Transfer learning on Balanced Datasets 15

Enhanced Fish Species Identification Using Transfer Learning...

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

作者： Berlia, Shubham Singh, Vishal Kumar Kumar, Manish Mahato, Ramkaran Mishra, Monalisa C.V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The study aimed to enhance the accuracy and reliability of automatic fish detection and species classification from aquatic images and videos. Traditional manual methods are time-consuming, and existing automatic systems struggle with challenges such as environmental lighting fluctuations, fish camouflage, dynamic backgrounds, murky water, low resolution, shape distortions, and subtle differences between species. To address these issues, the proposed model leveraged the MobileNet V2 architecture for transfer learning and fine-tuned it by incorporating edge detection, texture detection, and shape detection techniques. The integration of these transfer learning methods was critical in achieving the study's objective. Through meticulous preprocessing, balancing the dataset, and effectively applying transfer learning, the model achieved an impressive accuracy of 99.83%. This high accuracy underscores the model's effectiveness and reduced reliance on high-quality images typically required by other detection models. The results indicate significant progress in automated fish detection and species classification, offering a promising tool for marine scientists and conservationists to monitor fish populations more efficiently. © 2024 IEEE.

关键词： Edge Detection MobileNet V2 architecture Shape Detection Texture Detection Transfer learning

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A system for crop recommendation to improve the production management

A system for crop recommendation to improve the production m...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Ramyadevi, R. Sandeep, K. Girish Sairam, V. Vaisak, P. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

The increasing world population leads to a heightened demand for sustainable living and nutritious food. The farming industry, which is one of the most profitable in the world, faces challenges in meeting these needs. Integrated Crop Management offers a solution for ecologically sustainable farming that balances quality, quantity, and profitability. By utilizing tools such as weather forecasting, trend analysis, and productivity monitoring, farmers can minimize uncertainty and errors, leading to increased profitability. We propose a solution for farmers that involves utilizing Machine learning and Data Analytics to manage information related to crop selection, soil type, and production outcomes. This 360-degree approach includes long-term strategies for increased production and provides information on various crop categories and weather predictions to determine the best results based on seasonality. By inputting farm and weather data, farmers can determine the optimal crops to grow for each cycle, predict the return on investment for farm activities. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Crops

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Enhancing Cloud Security: A Comprehensive Review of Machine learning Approaches 2

Enhancing Cloud Security: A Comprehensive Review of Machine ...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Safi, Wassim Ghwanmeh, Sameh Mahfuri, Mahmoud Al-Sit, Waleed T. Higher Institute For Applied Sciences And Technology-HIAST Damascus Syria American University In The Emirates Department Of Computer Science United Arab Emirates International University For Science And Technology Computer And Information Technology Department Damascus Syria College Of Engineering Mu'tah University Department Of Computer Engineering Al-karak Jordan Higher Colleges Of Technology Dubai United Arab Emirates

ISBN: (纸本)9798350394962

Cloud computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to Machine learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds. © 2024 IEEE.

关键词： Cloud security

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Consumer Complaints Classification using Deep learning & Word Embedding Models 14

Consumer Complaints Classification using Deep Learning & Wor...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Vinayak, Vineet Jyotsna, C. Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350335095

The goal of Text classification is to categorize a document into predefined categories. Various supervised and unsupervised classifiers can be used to achieve this. In the research work proposed, SOTA (State of the Art) deep learning models and embedding techniques are used to classify consumers' complaints, which is in form of text, into 6 classes. Words and documents are represented as numeric vectors through embedding, enabling vector representations for related words. These representations are easily ingested by NLP algorithms. The classes represent departments where complaints are routed. Deep learning models like LSTM, Bi-LSTM, GRU and 1D CNN are used, along with word embedding techniques like Word2Vvec, Fasttext, Bert and Distilbert to represent text. The experimental results indicate that DistilBert and CNN achieved a 93% F-score. © 2023 IEEE.

关键词： Embeddings

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Automatic Emotion Detection from Facial Features Extraction and Machine learning Models

Automatic Emotion Detection from Facial Features Extraction ...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Shankar, Raghav Singh Kirar, Bhupendra Upadhyay, Pratham Jee Mishra, Shree Govind Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Information Technology Indian Institute of Information Technology Bhopal India

ISBN: (纸本)9798331529833

In the domain of Interactive Systems (IS), machine learning (ML) emerges as a profound, user-friendly, and accurate approach to enhancing emotional intelligence in digital systems. This research work focuses on facial emotion detection, an important but often-neglected aspect of affective computing. Models used for the assessment include Multinomial Naïve Bayes (MNB), Decision Trees (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machines (SVMs), XG Boost (XGB), and Logistic Regression (LR). By employing the FER2013 dataset and MediaPipe's (MP's) facial recognition technology, this research extracts 478 3D facial landmarks to test. The best Acc in emotion detection is found using LR with an accuracy of 65.57% followed by XGB with an accuracy of 61.24%. This study thus will help as a practical application toward making improvements in IS in matters regarding health, marketing, or even security. © 2025 IEEE.

关键词： Adversarial machine learning

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Next-Gen Breast Cancer Prediction through Adaptive Deep learning 6

Next-Gen Breast Cancer Prediction through Adaptive Deep Lear...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Potluri, Pandarinath Nagarajaiah, Kavyashree Lakshmi, K. Mohana Moolbharathi, Manjula Nagaratnamma Balaram, Allam Department of CSE Swarnandhra College of Engineering and Technology Sitarampuram Narsapuram West Godavari534275 India Department of MCA Sri Siddhartha Institute of Technology Tumkur572105 India Department of ECE CMR Technical Campus Kandlakoya Medchal Telangana Hyderabad India Govt polytechnic Afzalpur Karnataka Kalaburgi India Department of CSE MLR Institute of Technology Hyderabad500043 India

ISBN: (纸本)9798331522667

This study enhances the ongoing advancements in breast cancer diagnostic tools by highlighting the importance of employing adaptive deep learning and optimization strategies to achieve high accuracy rates. Advancements in medical technology have facilitated early diagnosis and enhanced treatment outcomes for breast cancer, potentially providing doctors with a valuable tool for early detection that could significantly improve patient outcomes. This study introduces a novel approach, learning Assisted Breast Cancer Prediction (LABCP), which incorporates an advanced ensemble of Cascaded Inception Model and the Ant Lion Optimization algorithm. The proposed adaptive deep learning scheme harnesses the power of deep neural networks to enhance breast cancer prediction accuracy. The experimental evaluation demonstrates outstanding predictive performance, with an accuracy of 98%. The Cascaded Inception Model effectively captures intricate features within breast cancer images, while the Ant Lion Optimization algorithm optimizes the model parameters, further refining the predictive capability. The combination of these techniques results in a robust and efficient system for early breast cancer detection. © 2025 IEEE.

关键词： Lung cancer

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