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Research on Negative learning Emotion recognition in English Classroom using Cross Space based Convolutional Neural Network

Research on Negative Learning Emotion Recognition in English...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Xuanxuan Tan Xiaoli Hou Xiaojia Dai Lilanjun Tan Department of International Business and Economics Hunan Sany Polytechnic College Changsha China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In English classroom the teaching behavior, changes according to the student’s facial emotions which are observed by facial expressions and behavioral phases in the class. Nonetheless, in the English classroom, behavior of the students during learning is changeable and complex to understand by teacher’s and it also difficult for understanding the negative emotions such as bored, stressed and not interested in the English classroom. To overcome these limitations, a Cross-Space based Convolutional Neural Network (CS-CNN) model is proposed to recognize the which is observed from students. The proposed method learns multiscale information such as subtle differences between various emotions and complex negative emotion pattern efficiently by cross-space feature fusion. This helps to enhance the recognition performance to assist the teachers to change their teaching strategies to make the students more focused and improve learning environment of students. For extracting significant features from the facial images are preprocessed by histogram equalization and extracted using DeepNet architecture. The experimental results of the proposed CS-CNN method achieved accuracy of 94.33% which is higher when compared to existing recognition approaches such as Support Vector machine (SVM) and Yolo-V3.

关键词： Support vector machines Image quality Emotion recognition Histograms Accuracy Education Computer architecture Feature extraction Convolutional neural networks data mining

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Educational Analytics using Modern Methods of Classification 3

Educational Analytics using Modern Methods of Classification

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Mohan, Lalit Jain, Sourabh Suyal, Priyanka Gehu Bhimtal India MIET Haldwani India

ISBN: (纸本)9781665438117

In today's Era success rate of students play a major role in overall growth of students, so Higher Educational University use many performance evaluation techniques. Universities use many Statistical, physical examination and data mining technique for evaluating students' performance Educational datasets are very useful in modern day's world for extracting important information from it. These information's can be used to know the important facts about performances. In this paper machine learning and classification techniques are used on educational datasets to predict the important factors for high academic performances. Also, to find out the relation between these individual factors. Prediction is made more accurate by using different data classifications techniques used in data mining and the best suitable classification technique for educational datasets is determined. For this we used modern classifications techniques and kalboard 360 dataset. © 2021 IEEE.

关键词： data mining

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Association Rule Extraction and Correlation Analysis Framework for Dominant Feature Selection in Thyroid Disease data

Association Rule Extraction and Correlation Analysis Framewo...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Sujithra Sankar Sathyalakshmi S Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Thyroid related illnesses are one of the most common among disorders of the endocrinal glands. This paper is an attempt at extracting the principal features of thyroid disease dataset. These relevant features trained on a suitable machine learning model can successfully diagnose thyroid diseases in a machine learning pipeline. In this pa-per, we propose a framework of iterative Association Rule mining and Correlation Analysis, which integrates associa-tion rule mining algorithm and subsequent correlation-based analysis of the result set to extract dominant features in the dataset. Apriori and FP Growth algorithms, the two most popular association rule mining algorithms, were used to extract the major attributes which causes thyroid related sicknesses. The association rule mining is used in dimen-sionality reduction of the dataset. We have used a thyroidi-tis dataset for this study, which is first preprocessed to pre-pare it for association rule mining. Preprocessing is a signif-icant step in our method. Next step is the splitting of the dataset into n data subsets. The Apriori and FP growth algorithms are applied on these multiple data subsets to create frequent k-itemsets. The most relevant of the associa-tion rules are selected by a majority voting from the result set generated after the application of association rule mining. The result set, comprising of the features which are selected as the principal attributes are again filtered using correlation analysis. The results show that gender factor is the most dominant of all features. It shows a predominance in association of women with Thyroid diseases than men. In the proposed method, the dataset undergoes a two-step process of refinement and consequently, association rules generated gives valuable insights into the thyroid related disease’s causative factors. The experimental results show that the performance of the proposed framework helps in principal feature selection, and thus can be used in dimen-sionality

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Cybersecurity Threat Intelligence Automated via machine learning: A System for Analyzing and Responding to data in Real-Time

Cybersecurity Threat Intelligence Automated via Machine Lear...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Shanmugam Muthu P. Deepalakshmi Shipt Inc Birmingham AL USA School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Businesses are relentlessly bombarded with advanced and ever-evolving attacks in today's rapidly evolving cyber threat landscape. Predefined signatures and rule-based methods to traditional security approaches cannot keep pace with threats of today's magnitude and diversity. The focus of this study is an ML-based real-time threat assessment and response cybersecurity threat intelligence solution. Advanced machine learning algorithms, anomaly detection, and data mining are employed to analyze vast amounts of security information, identify new threats, and respond with instantaneous, effective defense. The system detects intrusion attempts, data exfiltration, and ransomware attacks by analyzing diverse streams of data from security appliances like network logs, endpoint behavior, and traffic patterns. Supervised and unsupervised learning discovers known attack patterns, and anomaly detection discovers new threats. The system learns from new data to detect new attack pathways without rule updates. An automatic response method can isolate compromised devices, ban malicious IP addresses, and warn security personnel in real time, decreasing reaction time and human interaction. Threat intelligence feeds add external context to the system's ability to identify advanced persistent threats (APTs) and other high-level cyberattacks, according to the research. Integration with threat-sharing systems keeps the system updated on threat intelligence, ensuring defensive measures are effective against developing TTPs. The suggested ML-based threat intelligence system speeds up and improves cyber threat detection and response, improving security and allowing organisations to scale their defences in a more complex cyber environment. This paper analyses the system's architecture, performance, and potential for future improvements, such as deep learning models, federated learning for privacy-preserving intelligence sharing, and complex decision-making automation. machine learning and aut

关键词： Automation Federated learning Decision making Traffic control Real-time systems Threat assessment Feeds Computer security Anomaly detection Unsupervised learning

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Secure and Efficient IoT Networks: An AI and ML-based Intrusion Detection System

Secure and Efficient IoT Networks: An AI and ML-based Intrus...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Mohammed A. Mahdi Department of Information and Computer Science College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are based on artificial intelligence (AI) and machine learning (ML), this research study proposes a unique strategy for addressing security issues in Internet of Things (IoT) networks. This technique seeks to address the challenges that are associated with these IoT networks. The use of intrusion detection systems (IDS) makes this technique feasible. The purpose of this research is to simultaneously improve the present level of security in ecosystems that are connected to the Internet of Things (IoT) while simultaneously ensuring the effectiveness of identifying and mitigating possible threats. The frequency of cyber assaults is directly proportional to the increasing number of people who rely on and utilize the internet. data sent via a network is vulnerable to interception by both internal and external parties. Either a human or an automated system may launch this attack. The intensity and effectiveness of these assaults are continuously rising. The difficulty of avoiding or foiling these types of hackers and attackers has increased. There will occasionally be individuals or businesses offering IDS solutions who have extensive domain expertise. These solutions will be adaptive, unique, and trustworthy. IDS and cryptography are the subjects of this research. There are a number of scholarly articles on IDS. An investigation of some machine learning and deep learning techniques was carried out in this research. To further strengthen security standards, some cryptographic techniques are used. Problems with accuracy and performance were not considered in prior research. Furthermore, further protection is necessary. This means that deep learning can be even more effective and accurate in the future.

关键词： Deep learning Accuracy Simulation Intrusion detection Real-time systems pattern recognition Internet of Things

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Optimizing Oral Cancer Detection: A Hybrid Feature Fusion using Local Binary pattern and CNN

Optimizing Oral Cancer Detection: A Hybrid Feature Fusion us...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Kumar, Jeevan Pandey, Vijay Tiwari, Rajesh Kumar Jeevan Kumar Department of CSE Ranchi834010 India Vijay Pandey Department of ME BIT Sindri Dhanbad828122 India Rajesh Kumar Tiwari Department of CSE RVSCET Jamshedpur831012 India

One of the most hazardous diseases is cancer, which poses a significant threat to human health due to its ability to manifest in various parts of the body as abnormal, proliferative cells. In the case of oral cancer, diverse behavioral patterns may be observed, making early detection and accurate prognosis crucial for effective and timely treatment. Multi-Layer Visual Feature Fusion (MLVFF) is a novel feature fusion technique that is explor to develop a system to detect and predict oral cancer at its earliest, most treatable stages. MLVFF combines Local Binary patterns (LBP) characteristics with Convolutional Neural Network (CNN). More precisely, MLVFF may successfully improve the discriminating strength of features for Oral Cancer image recognition by combining the LBP and CNN versions, respectively. Test accuracy for the suggested model is 98.00%. In addition, we draw attention to emerging research questions in the field of cancer sickness, offering relevant data for further research that will improve cancer detection and treatments. © 2025 The Author(s).

关键词： Convolutional neural networks

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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

Multiple Manifold Regularization Based on Non-Negative Matri...

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Disruptive Technologies (ICDT), international conference on

作者： Mutyala Sirisha Ghufran Ahmad Khan Jalaluddin Khan Hemlata Pant Kaneez Zainab Zulfikar Ali Ansari Shamshul Haq Ansari Department of Computer and Applications Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow Uttar Pradesh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.

关键词： Manifolds Encyclopedias machine learning Linear programming Internet Performance analysis Online services Iterative methods Matrix decomposition Optimization

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Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny 25

Semi-supervised Lightweight Object Detection Method Based on...

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Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Zhilong Gao Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition Liaoning University Shenyang Liaoning China College of Information Liaoning University Shenyang Liaoning China

ISBN: (纸本)9798400713231

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.

关键词： Object Detection

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Named Entity recognition for Telugu and Kannada on Naamapadam dataset Using Various machine learning and Deep learning Algorithms

Named Entity Recognition for Telugu and Kannada on Naamapada...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Sree, Kuruva Divya Geethika, Gunti Nair, Aarathi Rajagopalan Gupta, Deepa Veena, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Named Entity recognition is a crucial task in Natural Language Processing. It involves detecting and categorizing named entities such as individuals, places, and organizations within a text. This process is vital for transforming un-structured text into structured information, which is useful for various applications like information retrieval, question answering, and text summarization. The proposed study investigates bilingual NER for Telugu and Kannada languages using pre-trained language models and embeddings for contextual vectorization. Utilizing the Naamapadam dataset, the research employs IndicFT, MuRIL and IndicBERT embeddings along with machine learning and deep learning algorithms. Notably, utilized only 10% of the considered dataset and achieved results that surpass those reported in the base paper. Among the models tested, MuRIL combined with XGBoost achieved the best performance, with an F1 score of 89.00 for Telugu and 87.00 for Kannada. Performance evaluation demonstrates varying effectiveness across techniques, shedding light on optimal strategies for NER in under resourced languages. These findings contribute to advancing NLP techniques for Indian languages, facilitating broader accessibility and usability in diverse linguistic contexts. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Question answering

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Research on Objectives recognition of Robotics based on Convolutional Neural Network

Research on Objectives Recognition of Robotics based on Conv...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Guanzhu Ren Yichen Zhao Zhengfeng Wu Pengwei Li Kaiyu Mi College of Wollongong Engineering Taiyuan University of Technology Taiyuan China College of Computer Science and Technology (Big Data College) Taiyuan University of Technology Jinzhong China College of Mechanical and Transportation Engineering Taiyuan University of Technology Taiyuan China

Recently, robotics has widely utilized in tremendous applications including traffic transportation, medical treatments, power system and virtual environment. These applications are almost based on the vision system, which is consisted by detection sensors and cameras. However, existing recognition methods are concentrated on the precision of machine learnings under the ideal setups and ignored the complex environment for robotics. In this paper, we simulate the realistic robotics sensors with non-complete input images and propose a novel convolutional network without redundancy neural layers. Indeed, an attention mechanism is established to enhance the recognition accuracy of proposed framework. Finally, we evaluate our model with existing objectives identification learning methods in the identical testing configurations. From our extensive experiment results and analysis, we can conclude that the proposed model can achieve the objectives recognition in complex robotics scenario with acceptable recognition accuracy and reasonable computation costs.

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