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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是1211-1220 订阅

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A Signal Processing and machine learning Framework for Enhanced TDS Simulation

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

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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

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

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

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.

关键词： Support vector machines Adaptation models Accuracy Empirical mode decomposition Adaptive systems Noise Artificial neural networks Solids Wastewater Wastewater treatment

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Improving Multi-Factor Authentication Security with Deep learning-Based User Behaviour Analysis

Improving Multi-Factor Authentication Security with Deep Lea...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Fahad Sulaiman Mohammed Alnafea Alok Singh Sengar Noor Kaylan Hamid Kuttimarks Muthu Selladurai Sharmeen Izzat Hassan R Saravanakumar Department of Accounting College of Business and Economics in Qassim University Buraida Saudi Arabia Department of Computer Science & Engineering Haridwar University Roorkee India Department of Computer Education Al Kitab University Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions; thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely.

关键词： Deep learning Hands Multi-factor authentication Accuracy Neural networks Reflection pattern recognition Convolutional neural networks Integrated circuit modeling Long short term memory

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Neural Network Approaches for Magnetization Surface Prediction in Switched Reluctance Motors: Classical, Radial Basis Function, and Physics-Informed Models

Neural Network Approaches for Magnetization Surface Predicti...

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Informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Galina Demidova Andrey Bogdanov Andrei Iaremenko Zhu Xirong Hao Chen Alecksey Anuchin Electric Drive Department Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Networks. The study utilizes experimental data from a 7.5 kW SRM to assess the performance of these approaches, with all methods implemented in Python. Among the methods analyzed, PINN demonstrates the highest accuracy in predicting the magnetization surfaces, effectively capturing the underlying physical behavior of the motor. The computational costs of each method are evaluated, providing insights into training times, inference speeds, and floating-point operations per sample. The findings reveal that while NN offers a reasonable balance between performance and efficiency, the RBF method excels in training speed but shows limitations in capturing the physical dynamics of the system. In contrast, the PINN not only delivers superior predictive performance but also emphasizes the significance of incorporating physical principles into machine learning frameworks. The results highlight the applicability of these methods across various types of SRMs, suggesting their potential for enhancing control strategies in electrical and power engineering applications.

关键词： Training Power engineering Electric potential Accuracy Magnetization Radial basis function networks machine learning Artificial neural networks Switched reluctance motors Computational efficiency

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BanglaLM: data mining based Bangla Corpus for Language Model Research 3

BanglaLM: Data Mining based Bangla Corpus for Language Model...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Kowsher, Md. Uddin, Md. Jashim Tahabilder, Anik Amin, Md Ruhul Shahriar, Md. Fahim Sobuj, Md. Shohanur Islam Noakhali Science and Technology University Noakhali3814 Bangladesh Western Carolina University CullowheeNC28723 United States Florida Atlantic University Boca RatonFL33431 United States Hajee Mohammad Danesh Science and Technology University Dinajpur5200 Bangladesh

ISBN: (纸本)9780738146270

Natural language processing (NLP) is an area of machine learning that has garnered a lot of attention in recent days due to the revolution in artificial intelligence, robotics, and smart devices. NLP focuses on training machines to understand and analyze various languages, extract meaningful information from those, translate from one language to another, correct grammar, predict the next word, complete a sentence, or even generate a completely new sentence from an existing corpus. A major challenge in NLP lies in training the model for obtaining high prediction accuracy since training needs a vast dataset. For widely used languages like English, there are many datasets available that can be used for NLP tasks like training a model and summarization but for languages like Bengali, which is only spoken primarily in South Asia, there is a dearth of big datasets which can be used to build a robust machine learning model. Therefore, NLP researchers who mainly work with the Bengali language will find an extensive, robust dataset incredibly useful for their NLP tasks involving the Bengali language. With this pressing issue in mind, this research work has prepared a dataset whose content is curated from social media, blogs, newspapers, wiki pages, and other similar resources. The amount of samples in this dataset is 19132010, and the length varies from 3 to 512 words. This dataset can easily be used to build any unsupervised machine learning model with an aim to performing necessary NLP tasks involving the Bengali language. Also, this research work is releasing two preprocessed version of this dataset that is especially suited for training both core machine learning-based and statistical-based model. As very few attempts have been made in this domain, keeping Bengali language researchers in mind, it is believed that the proposed dataset will significantly contribute to the Bengali machine learning and NLP community. © 2021 IEEE.

关键词： learning algorithms

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Effective Visualization and Trend Forecasting of Crime data Through Big data Analytics and mining

Effective Visualization and Trend Forecasting of Crime Data ...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Pd Mahendhiran Apsara S K Harshitha Vasudevan Nithish S Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

A crime is an act that merits public condemnation and penalties, which is usually in the form of a fine or jail. It is a widespread social issue that affects a country's life style, economic progress, and credibility. People are at a worrisome point in terms of reducing criminal activity at a greater rate, as criminality rates have risen dramatically. If people can evaluate and predict the crime pattern and implement preventive steps ahead of time, crime rates can be lowered. In this research, the author has present Crime Cast, a system that analyses crime reports and anticipates trends and crime statistics based on a specified data set using deep learning techniques such as LSTM and DNN at a faster rate and with greater accuracy. Within a huge percentage of crime data, Big data Analytics is utilized to analyze and detect different crime patterns, their relationships, and trends. The information contains cases of kidnapping, murder, rape, stealing, and other crimes. With the help of the LSTM model and DNN several operations performed on the data. The expected outcomes will aid police and law enforcement agencies in clearer grasp crime trends, allowing them to track incidences and improve their decision-making processes. These results will allow the law enforcement and regulatory agencies to further immediately spot inappropriate behavior issues and provide information that will allow them to monitor activities and predict the likelihood of limiting the number of events, transporting property securely, and improving the dynamic cycle.

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Emotion recognition of Speech in Hindi Using Dimensionality Reduction and machine learning Techniques 3rd

Emotion Recognition of Speech in Hindi Using Dimensionality ...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

作者： Agrawal, Akshat Jain, Anurag USIC&T Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (纸本)9783030691424

This study primarily focuses on integrating various types of speech features and different statistical techniques to reduce the data in all dimensions. To predict human beings, the existing machine learning algorithms were applied to train the available dataset to obtain better results. This will result in obtaining much better response from machines by processing the human instruction & related emotions. Through machine learning techniques emotion recognition system can be work effectively and results will be comparable through code of python language. To improve the performance of emotion recognition (ER) statistical techniques principal component analysis (PCA) and linear discriminant analysis (LDA) to fetch the essential information from the dataset. After applying Naïve Bayes classification (NBC) on the speech corpus collected from non-dramatic actor the results shows that, NBC generates better results than the exiting machine learning (ML) classification techniques such as K-means nearest neighbor algorithm (KNN),Support vector machine (SVM) & decision tree(DT). This paper presents the results of various ML techniques that is used for finding out ER. Through SVM prediction rate achieved 39% and decision tree recognition rate achieved 29% only and when applied NBC results is 72.77%. This study presents the change of prediction rate while change of dataset happened. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

Aspect category and sentiment polarity detection using a per...

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

作者： Jayaraman, Ashok Kumar Trueman, Tina Ester Ananthakrishnan, Gayathri Murugappan, Abirami Cambria, Erik Manikandan, G. Mitra, Satanik HCLTech Engineering and R&D Services Tamil Nadu Chennai India Department of Computer Science University of the People CA United States School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result. © 2025 The Authors. Published by Elsevier B.V.

关键词： Emotion recognition

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Analyzing Applicant’s Pre-admission data and Predicting Applicant’s Performance in Pre-Admission Test using data mining Techniques

Analyzing Applicant’s Pre-admission data and Predicting App...

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Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Engr. Nimra Iqbal Raheela Asif Computer Science and I.T Department NED University of Engineering & Technology NED University of Engineering & Technology Karachi Pakistan

data mining methods are frequently employed at many universities nowadays for examining the applicants’ data, finding statistics, understanding patterns, and extracting the knowledge that helps to make a decision and improve the university applicants’ performance. Numerous higher education institutions are adopting the strategy of predicting the student’s performance in their examination results or entry test scores. The focus of this paper is two-fold; the first objective is to analyze the applicant’s pre-admission data to a public sector engineering university in Pakistan. The second objective is to predict the applicant’s performance in the pre-admission entry test from different aspects like gender, educational board of examination, and area of residence.

关键词： Correlation Education data visualization Organizations machine learning Performance analysis Classification algorithms

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Leveraging Neural Networks to Enhance Large-Scale data Processing Performance

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Megha Pandey Vinay Kumar S B Jatin Khurana B. Kalpana Kishor R. Pathak Mukesh Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India −1 Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications.

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Quantum Computing and machine learning Algorithms - A Review

Quantum Computing and Machine Learning Algorithms - A Review

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Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Iram Manan Faisal Rehman Hanan Sharif Naveed Riaz Muhammad Atif Muhammad Aqeel Department of Information Technology Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan Department of Computer Science & Information Technology NUST Islamabad Pakistan

The goal of machine learning (ML) is to develop models that automatically learn from the past without being explicitly programmed. ML has numerous applications, such as pattern recognition, forecasting upcoming trends, and judgement. It can handle massive amounts of huge vectors and tensors representing multidimensional data. To handle these processes on traditional computers, enormous time and computational resources are required. Unlike traditional computers, which use binary bits to compute, quantum computers (Q.C.) use qubits, which can simultaneously hold 0 and 1 combinations through superposition and entanglement. This makes *** an excellent choice for implementing ML algorithms because they are skilled at handling and post-processing large tensors. Although many of the models used for ML on quantum computers are based on concepts from their classical computing counterparts, the use of quantum computers has made them the better of the two. This paper assess the speed and complexity benefits of using quantum computers and gives a general overview of the state of knowledge regarding the use of ML on Q.C.

关键词： Computers Quantum computing Tensors Quantum entanglement Computational modeling Qubit machine learning

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