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Deep learning-based evaluation system for handwritten Chinese character writing standardization 4

Deep learning-based evaluation system for handwritten Chines...

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4th international conference on machine learning and Intelligent systems engineering (MLISE)

作者： Yan, Zihao Lai, Sirui Zhang, Ruibin Chen, Liyun Zhuhai Coll Sci & Technol Sch Comp Sci Zhuhai Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In today's society, because of the increase in the frequency of people's use of electronic products, it leads to the phenomenon of irregular writing of Chinese characters and forgetting the characters when putting pen to paper, thus affecting the inheritance of the excellent traditional Chinese culture to a certain extent, so this paper proposes a handwritten Chinese character writing specification evaluation system based on deep learning. This system adopts two deep learning multi-classification models based on ViT to reason about the handwritten Chinese character images in order to obtain the calligraphic evaluation factors, and then calculate the overall evaluation of this handwritten Chinese character through the AHP mathematical evaluation model, and at the same time generate comments for the user through the individual evaluation values.

关键词： evaluation of handwritten Chinese characters deep learning ViT AHP Roboflow

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Sentiment Classification of Product Reviews using machine learning

Sentiment Classification of Product Reviews using Machine Le...

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2024 international conference on Recent advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Adarsh, Vedullapalli Mohla, Vaibhav Mahto, Ragven Kumar Radhika, P. College of Engineering SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798350354539

In the fast-paced e-commerce environment, understanding and learning consumer perceptions of products is essential for businesses to enhance user experience, optimize marketing strategies, and better overall customer satisfaction. This paper investigates the use of machine learning models for sentiment classification and sentiment analysis. VADER, known for its simplicity and speed, employs a sentiment lexicon for polarity scoring. RoBERTa and BERT transformer-based models, which excel in capturing contextual information and long-range dependencies, making them extremely powerful for sentiment analysis. The performance of rules-based models typically falls below machine learning models, which on the other hand, generally exhibit better performance. A diverse dataset is used and the preprocessing steps are detailed. This includes duplicate removal, training of models, using NLP etc. In this paper the highest F1 score is obtained in Bert is 68.9 followed by a 57.8 in RoBERTa and then Vader with a 12.7. These show the superior capability of machine learning models like BERT and RoBERTa in capturing sentiment compared to rule based approaches like Vader which is simpler in nature. This shows the superiority of these models over older models like SVM and Naive Bayes often showing lower average F1 scores. This paper contributes valuable guidance for implementing effective sentiment analysis systems in E-commerce. © 2024 IEEE.

关键词： Sentiment analysis

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Survey on machine learning Based Anomaly Detection in Cloud Networks

Survey on Machine Learning Based Anomaly Detection in Cloud ...

引用

2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Harish, G.N. Annapurna, H.S. Sri Siddhartha Academy of Higher Education Tumkur India Ssit Department of Information Science and Engineering Tumkur India

ISBN: (纸本)9798350359688

As cloud computing becomes more popular and cyber threats become more sophisticated, ensuring the security of cloud networks has become a paramount concern for organizations worldwide. In this dynamic environment, traditional security measures are frequently insufficient, necessitating the deployment of new technologies such as machine learning-based anomaly detection systems. In the context of enhancing cloud network security, the goal of this survey research is to provide a comprehensive overview of the current state of machine learning-based anomaly detection systems. The article begins by discussing the challenges and threats that cloud networks face, highlighting the need for proactive security measures. It then delves into the various machine learning techniques and algorithms that have been employed for anomaly detection in cloud environments, covering methods for semi-supervised, supervised, and unsupervised learning. The advantages and limitations of each approach are analyzed, providing valuable insights into their practical implementation. This paper also explores practical application scenarios for anomaly detection systems in cloud networks that are based on machine learning, illustrating their effectiveness in identifying and mitigating security threats. It also discusses the key factors that influence the performance of these systems, such as data quality, feature engineering, and model selection. The survey article also covers the challenges and open research questions in the field, emphasizing the need for continuous innovation and adaptation in the face of evolving threats. The paper concludes with a set of best practices and recommendations for organizations looking to optimize their cloud network security through the implementation of machine learning-based anomaly detection systems. For security experts, researchers, and decision-makers looking to use machine learning approaches to improve the security of their cloud networks, the survey offers

关键词： Cloud security

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Advancements in Fake News Detection Using machine and Deep learning Models: Comprehensive Literature Review

Advancements in Fake News Detection Using Machine and Deep L...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Alkomah, Bushra Sheldon, Frederick Univ Idaho Dept Comp Sci Moscow ID 83843 USA

ISBN: (纸本)9798350361513;9798350372304

In the age of digital technology, the exponential spread of fake news has become a significant issue for society. In response to this issue, considerable advances have been made to identify fake news using machine learning (ML) models. This literature review investigates the current state of research on detecting fake news. It emphasizes the use of ML models such as TF-LIP, Naive Bayes, and Random Forest, as well as deep learning (DL) models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models such as BERT. The review concisely summarizes the essential findings and discusses the potential future implications offake news identification. It also emphasizes the need for additional research to address numerous challenges, such as effective multimedia content management, protection against adversarial attacks, attainment of model generalizability, facilitation of real-time detection, and adherence to ethical standards when developing detection systems. This review is a resource for researchers and practitioners seeking to develop effective methods for addressing the perpetually expanding problem of detecting fake news.

关键词： Fake new detection machine learning deep learning natural language processing dataset feature engineering

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Optimization of Energy Efficiency in IoT inside Smart Grids Utilizing machine learning and Artificial Intelligence Techniques 1

Optimization of Energy Efficiency in IoT inside Smart Grids ...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thimmappa, Arunkumar Devalapura Singh, Swati Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

It is possible for hard jobs to be done by machines with the help of machine learning. Computers and cell phones could make it simpler to regulate the temperature inside a smart grid (a SG), keep an eye on security, and do regular repair. The Internet of Things, or IoT, links the different parts of smart buildings together. As the idea of the Internet of Things (IoT) grows, SGs are being added to bigger networks. The Internet of Things (IoT) is a big part of Smart Grid (SG) s as it makes life better for everyone. It is proven that current methods for life support are harmless & work well to keep people alive. The main purpose of this study is to find out why IoT devices are being put in smart buildings as well as the grid. Looking at it this way, the system that serves IoT devices along with the parts that make them up is very important. Smart grid tracking systems that can be set up remotely can make buildings safer and more comfortable for the people who live or work there. Everything from household technology to SGs needs sensors to work and be watched. Devices that are tied to a network should use less power and can be monitored from afar. Their goal is to help people come up with solutions that use AI, IoT, as well as SGs. The writers also look into SG, networking, and machine intelligence. The last thing we do is look at studies on SG along with IoT. A number of parts of the IoT technology are up for discussion. The first part of this study talks about the most popular ways to use machine learning to predict how much energy a building will need. The authors then talk about IoT and the ways it works, along with SG along with smart meters, and these are needed to get real-time energy information. Then, researcher look into how the different smart grid, Internet of things & machine learning parts work together and combine utilizing basic design with layers that are grouped into entities that talk to each other through links. © 2025 IEEE.

关键词： Smart power grids

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Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare systems

Systematically Assessing the Security Risks of AI/ML-enabled...

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9th IEEE/ACM international conference on Connected Health - Applications, systems and engineering Technologies (CHASE)

作者： Elnawawy, Mohammed Hallajiyan, Mohammadreza Mitra, Gargi Iqbal, Shahrear Pattabiraman, Karthik Univ British Columbia Vancouver BC Canada Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9798350345025;9798350345018

The adoption of machine-learning-enabled systems in the healthcare domain is on the rise. While the use of ML in healthcare has several benefits, it also expands the threat surface of medical systems. We show that the use of ML in medical systems, particularly connected systems that involve interfacing the ML engine with multiple peripheral devices, has security risks that might cause life-threatening damage to a patient's health in case of adversarial interventions. These new risks arise due to security vulnerabilities in the peripheral devices and communication channels. We present a case study where we demonstrate an attack on an ML-enabled blood glucose monitoring system by introducing adversarial data points during inference. We show that an adversary can achieve this by exploiting a known vulnerability in the Bluetooth communication channel connecting the glucose meter with the ML-enabled app. We further show that state-of-the-art risk assessment techniques are not adequate for identifying and assessing these new risks. Our study highlights the need for novel risk analysis methods for analyzing the security of AI-enabled connected health devices.

关键词： machine learning FDA medical system security risk analysis multi-vendor systems

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Silent Alert: Advancing Women's Security through Smart Sign Recognition and AI

Silent Alert: Advancing Women's Security through Smart Sign ...

引用

2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Senthilkumar, Dheeptha Bhavana, Lone Ranjana, P. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

In today's world, many technologically innovative solutions are developed to prioritize women's safety solutions, especially during nighttime. This research's main aim is to focus on addressing crucial safety issues like emergencies and harassment in public spaces. To build an efficient model the proposed system uses technologies like MediaPipe and machine learning algorithms like random forest, decision tree, support vector machine (SVM), gaussian naive bayes, k-nearest neighbor (KNN), gradient boosting and stacking classifier (hybrid model). Rigorous testing ensures the selection of the most effective model, assessed through metrics like F1 score, precision, and recall. By integrating technology and machine learning, apart from raising awareness and fostering safe environments the proposed work also aims to empower women and enhance their safety. MediaPipe significantly enhances the accuracy of sign language recognition and also plays a vital role in improving the system's effectiveness. At the same time, OpenCV enables real-time video capture for comparison with the trained model. To enhance the system, predictions are made based on the recognized sign language gestures. In the system, predictions are improved by analyzing recognized sign language gestures. It also incorporates a database matching system that activates an alarm and sends a mail with a screenshot upon detecting predefined gestures. This function ensures prompt notifications, enabling authorities to respond promptly to identified incidents concerning women's safety. The system's ability to instantly recognize and react to specific gestures makes it an effective tool for addressing safety issues for women. © 2024 IEEE.

关键词： Decision trees

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Efficient Smart Farm System Using machine learning 1

Efficient Smart Farm System Using Machine Learning

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1st IEEE international conference on advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Samantaray, Rashmi Rani Azeez, Abdul Hegde, Nayana H.K.B.K College of Engineering Dept. of Electronics and Communication Bengaluru India REVA University School of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350348057

The world around us is becoming increasingly mechanized because of technology. Due to their energy efficiency and reduced need for tiresome human labor, automatic systems are preferred over manual ones. Automation of agriculture is necessary to improve efficiency because it is the main economic sector in India and other emerging nations. It takes a lot of work to run a regular farm. Automation may effectively reduce the amount of physical work, facilitate, and speed up farming, and promote agricultural expansion. Farms and farmhouses engaged in agriculture are included in the concept of automation. This paper describes an automatic livestock monitoring system using machine learning for smart farms. The proposed system is implemented using Raspberry pi and sensors. © 2023 IEEE.

关键词： Python

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An Accurate Early Mortality Risk Prediction Model Based on ICU over MIMIC III using machine learning 15

An Accurate Early Mortality Risk Prediction Model Based on I...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sharma, Shilpa Kaur, Mandeep Gupta, Savita UIET Panjab University Chandigarh India

ISBN: (纸本)9798331300579

Mortality risk prediction of ICU patients is a valuable and challenging task due to limited clinical data. Accurate mortality risk prediction can improve the utilization of resources. In this work, we explore the use of feature engineering followed by applying a machine learning approach for predicting mortality risk in ICU patients using the MIMIC III dataset. The results of this study show that our prediction accuracy is 99.99% using a neural network. © Grenze Scientific Society, 2024.

关键词： Prediction models

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TeenSenti - A novel approach for sentiment analysis of short words and slangs

TeenSenti - A novel approach for sentiment analysis of short...

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2024 international conference on advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Kamath, Sahil Padiya, Vaishnavi D'Silva, Sonia Patil, Nilesh Narvekar, Meera D.J. Sanghvi College Of Engineering Dept. Of Computer Engineering Mumbai India

ISBN: (纸本)9798350371567

The present prevalence of online platforms and the internet has seen a substantial increase in the utilization of Generation Z slang, abbreviated expressions, and short words. These Lexical subtleties have become a vital part of a person's daily interactions, and therefore evident in reviews, product comments, and throughout the internet. Resultantly, there exists a need to integrate these Informal expressions such as abbrevi-ations, slang, and short words into natural language processing (NLP) systems. The Informal expressions naturally contain some contextual relevance and therefore can be used to improvise sentiment analysis, as these expressions contribute significantly to the overall context and sentiment of sentences. However, traditional NLP techniques often fall short in recognizing and incorporating these informal expressions, resulting in an atten-uated accuracy of sentiment analysis. To address this issue, this study ventures to provide a comprehensive slang dictionary, encompassing short words, abbreviated expressions, and slang along with the sentiment and sentences of each. This curated slang dictionary is effectively integrated into NLP systems using FastText Embeddings. Through extensive testing across multi-ple machine learning (ML) and deep learning (DL) models, this approach significantly enhances the accuracy of sentiment analysis when compared to conventional methodologies. This research emphasizes the importance of accommodating informal expression in NLP systems thus opening the possibility of future research. © 2024 IEEE.

关键词： Sentiment analysis

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