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

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Stroke Prediction Framework Based on Missing Value Information and Outlier Detection by Using machine learning Techniques in E-Healthcare 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Lahoti, Saurabh Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Technology improvements have allowed for the collection of massive amounts of data, particularly in the biological and healthcare industries. data is typically unstructured and can take many forms, including but not limited to images, audio, text, etc. That’s why it’s such a challenge to sift through all the data we’ve collected and find the nuggets that really matter. It’s also been noted that early detection of sickness drastically cuts down on potential human casualties. Regrettably, pertinent data cannot be extracted efficiently to facilitate sound decisions. On the flip side, data mining is a burgeoning discipline that reveals hidden significance in large data sets. Therefore, two methods are proposed to address the aforementioned concerns, namely missing data imputation and outlier detection. These methods are like an upgraded version of CNN and KMPSO. In order to properly compute missing value, the enhanced CNN method is used, and an enhanced weight function is incorporated into the CNN method. However, to deal with the outliers, a hybrid method based on K-Means and PSO is used. A better CNN data imputation method is used, and KMPSO is used to deal with data outliers. The outcomes of the proposed framework are compared to those of different SVM classifiers. After comparing the findings of the suggested framework to those of the other SVM variations, it was found that the latter produced more reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Enhancing Intrusion Detection in Wireless Sensor Networks Using Deep Convolutional Neural Networks

Enhancing Intrusion Detection in Wireless Sensor Networks Us...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Nishar Ahamed Manzoor Muhammad Firdaus Akbar M. Arul Prasanna School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia Nibong Tebal Pulau Pinang Malaysia Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The rapidly growing number of Internet users has made security more and more crucial. Researchers have developed a number of intrusion detection systems that use data mining techniques to find intruders. Several articles offer datadriven techniques to safeguard the Wireless Sensor Network (WSN) toward such assaults. These techniques use either classic machine learning (ML) models. These approaches have disadvantages, such as relying on raw characteristics for intrusion detection, which lead to a low level of detection accuracy. This paper proposes an Intrusion Detection System (IDS) to prevent these problems that successfully detects intruders in wireless networks, ensuring secure data exchange by implementing an enhanced deep learning model to identify different sorts of assaults in the WSN. Deep convolutional Neural Network (DCNN) is recommended for avoiding different incursions. Proposed framework effectively addresses potential threats between the application's and access network layers. The proposed work implemented by Python software tool and performed efficiently obtain the results. The results of proposed system are compared to state of art. This study analyzes relevance of results and their future prognosis. This study introduces a proposed DCNN classifier that demonstrates an impressive accuracy of 97.3% and a sensitivity of 92.4%, as determined through comparative analysis of the datasets.

关键词： Deep learning Wireless sensor networks Accuracy Sensitivity Wireless networks System performance Intrusion detection Smart systems Convolutional neural networks Software tools

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Harnessing Random Forest Classifiers and TF-IDF Vectorization: A Text-Based Approach to Detect Stress and Analyze Mental Health Using Artificial Intelligence

Harnessing Random Forest Classifiers and TF-IDF Vectorizatio...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Mohammed Emadulla A Tabrez Shariff Sabqath Ahmed Siddique Syed Zaid Deepak N R Omprakash B Information Science and Engineering Atria Institute of Technology VTU Bengaluru Karnataka India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

As we live in the digital age, the importance of stress detection research lies in the fact that people's interactions on social networking platforms and messaging services accurately reflect their emotional moods and mental health. Similarly to that, an attempt to develop an ML model that can identify human stress levels is made by analyzing the written records obtained from social media. This proposed system chooses a keyword-based method to detect a specific stress indicator statement and compare it with a given data set. The textual data are then preprocessed and vectorized based on the TFIDF method to detect meaningful patterns in the text data. The data were classified as stressed or non-stressed by a Random Forest Classifier with probabilities indicating the intensity of stress. To generate valuable insights by listening to the people's voices, which could help detect stress-based disorders in the early stages, this approach works with data obtained from online sources in compliance with ethical standards and general rule privacy. In conclusion, this study illuminates how AI-driven systems can enhance mental health awareness and intervention by implementing a holistic stress detection instrument using natural language processing approaches and machine learning methodologies.

关键词： Text mining Ethics data privacy Sentiment analysis Social networking (online) Mental health Smart systems Safety Random forests Standards

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machine learning-Based Crop Recommendation System for Mizoram

Machine Learning-Based Crop Recommendation System for Mizora...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： V.D. Ambeth Kumar Ajoy Kumar Khan Vanlalhruaia Saithantluanga Ramesh Prabhakaran R Zaitinkhuma Dept. of Computer Engineering Mizoram University Aizawl India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Agriculture is an important sector of Mizoram domicile as more than half of its population relies on Agriculture as principle source of income and sustenance. Some farmers rely on the knowledge acquire from their parents through explicit explanation, and by observing and modelling their practices. But most farmer often struggle to understand the method and type of crops to cultivate for better crop yield. Even experienced farmer believed that using more fertilizer result in better crop yield in spite of that it damages the soil properties. To resolve this challenge, this paper presents a Crop Recommendation System using machine learning, tailored for Mizoram, enhancing Agriculture practices towards sustainable development. The Crop Recommendation System analyzes historical data, soil properties, weather pattern and crop performance to recommend the best crop for a specific region and its condition. The aim is to provide the Crop Recommendation System with information about the soil and the condition of the region. The study utilizes various machine learning Algorithms such as Random Forest, Decision Trees, Support Vector machine and Logistic Regression to make optimal recommendation. The results indicate that Random Forest provides superior performance of 99% across all the evaluations metrics. Although many prevention measures need to be taken to avoid complications such as overfitting, etc. The overall results suggested that Random Forest achieved the best results as compared to all the other state of the art algorithms utilized with the same preprocessing steps. This approach enhances the crop and soil. After a long and often complicated process of farming method and selection of crop problem the Crop Recommendation System will aid Mizoram farmers to achieve better crops, yield and higher profit.

关键词： Support vector machines machine learning algorithms Soil properties Agriculture Decision trees Sustainable development Recommender systems Random forests Crop yield Overfitting

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Different Techniques for Detecting Plant Leaf Disease Using machine learning 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Gupta, Ashish Gupta, Sanjeev Kumar Yadav, Pritaj Gupta, Deepak Department of CSE Rabindranath Tagore University Bhopal India Department of CSE Institute of Technology and Management Gwalior India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Agriculture is India’s backbone, and it helps to expand the country’s economic system. Plant disease identification is an important aspect of agriculture. Plant diseases have an impact on quality, quantity, and productivity. The most important exercise for increasing production is plant disease predictions. In this research study, the author presents a comparative examination of many types of plant leaf disease as well as all of the research approaches related to plant leaf disease. This was done in the past for two reasons: To keep the paper’s size down, and because there are a few quirks in the procedures for dealing with seeds, roots, and herbal items that would demand a separate investigation. As stated by their purpose, the selected proposition is divided into three instructions: recognition, seriousness measurement, and characterization. As a result, each of these groups is segmented using the primary specialized association used in the calculation. By giving a complete and open definition of this broad topic, this study hopes to assist experts in both vegetable diseases and case recognition. The object of work will be a valuable resource for researchers looking to identify specific types of plant diseases using data-driven methodologies. The development of mobile-based applications based on the investigated ML/DL techniques will undoubtedly improve crop production. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Agriculture Disease detection classification features extraction Image acquisition Pre-processing

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Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP

Revolutionizing Legal Intelligence: Advances in Neural Netwo...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Raoul Samuel Noronha Alex Stanley Alenchery Deepa S Jayapriya J Vinay M Department of Computer Science Christ University Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers’ workflows to support them in their decision-making processes.

关键词： Analytical models Law Large language models Neural networks Text summarization Transforms Transformers data models Complexity theory Contracts

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Graph Neural Networks for Functional Connectivity Analysis: Uncovering Neural Network Disruptions in Parkinson’s Disease

Graph Neural Networks for Functional Connectivity Analysis: ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning pattern recognition Biological neural networks Diseases

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An IoT and data mining-Based Tool for Early Identification of Speech Disorders in Children Using Advanced Algorithms 3rd

An IoT and Data Mining-Based Tool for Early Identification o...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Usha, M. Department of Information Technology KG College of Arts and Science College Tamil Nadu Coimbatore India

ISBN: (纸本)9783031862892

Early detection of speech disorders in children is critical for effective intervention and treatment. This paper presents an innovative tool leveraging the Internet of Things (IoT) and advanced data mining techniques to identify speech disorders in children at an early stage. This paper explores the integration of IoT and data mining technologies for the early detection of speech disorders in children by applying advanced algorithms. The aim is to develop an efficient tool for early diagnosis and intervention, enhancing speech therapy outcomes through real-time monitoring and analysis. The system integrates IoT-enabled devices for real-time data collection, capturing various speech parameters such as pitch, tone, and articulation. These data are processed using advanced algorithms, including machine learning and deep learning techniques, to analyze patterns and anomalies indicative of speech disorders. The proposed paper offers a user-friendly interface for parents, educators, and healthcare professionals, providing insights into a child′s speech development and highlighting potential areas of concern. The system′s effectiveness is evaluated through extensive testing with a diverse dataset, demonstrating its accuracy and reliability in identifying various speech disorders, such as stuttering, dysarthria, and apraxia. By facilitating early diagnosis, this tool aims to improve the quality of life for children with speech disorders through timely and targeted interventions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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ProductGuard: Empowering Informed Choices for Safe Living

ProductGuard: Empowering Informed Choices for Safe Living

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Prof. Sandyarani Vadlamudi K. S. Shashikala Suyash Ganesh Patil Rajath U Shetty Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Karnataka

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices.

关键词： Deep learning Image analysis Accuracy Chatbots Skin Real-time systems User experience Reliability Product safety Usability

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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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