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Human Age Estimation Based on Dental Images 3

Human Age Estimation Based on Dental Images

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Raidzan, Nur Ain Mohtar, Itaza Afiani Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Computer Science Studies College of Computing Teknologi MARA Perak Branch Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality. © 2023 IEEE.

关键词： age estimation convolutional neural network forensic Inception-V3 machine learning pre-teen dental images)

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data mining for Fraud Detection in Telecommunications: Detecting Anomalous Behaviors in Real-Time 3

Data Mining for Fraud Detection in Telecommunications: Detec...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Nidhi Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In the recent past, telecom experts have been confronted with so many problems emanating from fraudulent deeds that apart from causing loss of money discredits organizations. To mitigate these problems, real-time fraud detection systems are very essential in detected those unusual and unauthorized activities. This study aims at examining how data mining can be used in the identification of the actual fraud in the telecommunication area through users' interactions in the course of which a huge amount of data is produced. In this study, the case uses different machine learning algorithms including clustering, decision trees, and anomaly detection for mapping out unusual call pattern, unauthorized access and Identity theft. To capture anomalous behavior, the research uses both time-domain and frequency domain data from different telecom networks for training and testing models. The research also intends to extend existing theories and practices of Naive Bayes, Support Vector machines (SVM), and deep learning networks to improve accuracy and effectiveness of the sort of systems used in the detection of fraud. The performance of these models is measured in terms of accuracy in classification and prediction of fraudulent actions thereby reducing potentials false negative and false positive cases. Further, the paper outlines how these models can be integrated into existing network security architecture to assess the prospects for scalability of these solutions within a maturing fraud environment. The findings also affirm that data mining is effective in real-time monitoring and prevention of frauds as such highlighting the importance of frequent updates of the fraud detection solutions within the telecommunication sector. © 2025 IEEE.

关键词： Anomalous data mining Fraud Detection SVM Telecommunication

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A Hybrid Feature Selection Approach for data Clustering Based on Ant Colony Optimization 29th

A Hybrid Feature Selection Approach for Data Clustering Base...

引用

29th international conference on Neural Information Processing

作者： Dwivedi, Rajesh Tiwari, Aruna Bharill, Neha Ratnaparkhe, Milind Indian Inst Technol Indore Indore 453552 India Mahindra Univ Ecole Cent Sch Engn Hyderabad 500043 India ICAR Indian Inst Soybean Res Indore 452001 India

ISBN: (纸本)9783031301100;9783031301117

machine learning, data mining, and pattern recognition all require feature selection when working with high-dimensional data. Feature selection helps in improving the prediction accuracy and significantly reduces the computation time. The problem is that many of the feature selection algorithms use a sequential search strategy to choose the most important features. This means that each time you add or remove a feature from the dataset, you get stuck in a local optimum. This paper proposes a hybrid feature selection technique based on ant colony optimization that randomly selects features and quantifies their quality using K-means clustering in terms of silhouette index and laplacian score. The proposed hybrid feature selection technique allows for random selection of features, which facilitates a better exploration of feature space and avoids the problem of being trapped in a local optimal solution, while also generating a global optimal solution. Furthermore experimental investigation shows that the proposed method outperforms the state-of-the-art method.

关键词： Ant Colony Optimization Jaccard index K-means clustering Laplacian Score Silhouette Index

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Research on public opinion analysis of COVID-19 based on machine learning 3

Research on public opinion analysis of COVID-19 based on mac...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Jing, Jiaxin Zhang, Lu Zhang, Duoyao Yang, Liangbin School of Cyberspace Security University of International Relations Beijing China

ISBN: (纸本)9781510671423

[Purpose] Social media is a platform for the participation of the whole people, and the "COVID-19" is a topic closely related to the whole people. Based on this, this paper conducts sentiment analysis on the hot topic of social media network public opinion "COVID-19", which can well present the public's emotional evolution process and results of this public opinion event, so as to provide certain guidance for relevant national departments to guide and control public opinion. [Method] This article chooses Twitter as the data source platform, collects tweets related to the "COVID-19" in January this year, and builds a simple public opinion analysis system using natural language processing models and different machine learning methods. [Conclusion] The research results show that for the topic of "COVID-19", which is a long-lasting topic that is closely related to everyone, the proportion of positive emotions is relatively large, which also protects the security of the country from the level of public opinion. In other words, for a hot topic, when negative emotions prevail, relevant departments need to intervene in time to supervise, guide the direction of public opinion, and maintain social stability. © 2023 SPIE.

关键词： COVID-19

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Risk Prediction of Earthquakes using machine learning 3

Risk Prediction of Earthquakes using Machine Learning

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Arunadevi, B. Madiajagan, M. Hussain, Mohammed Ihtesham Lakshmi, R. Ramakrishna, M.M. Sengupta Das, Kakali Dr.N.G.P Institute of Technology Department of Ece Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India University of Technology and Applied Sciences Department of Engineering Al-Khuwair Oman M. Kumarasamy College of Engineering Thalavapalayam Department of Mca Tamil Nadu Karur India Wolaita Sodo University Snnpr Department of Ece Ethiopia Budge Budge Institute of Technology Ece Department Kolkata India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

As the population of earthquake-prone areas continues to grow, the number of people killed and injured by earthquakes is also increasing. There are an increasing number of populated places that are at risk from earthquakes, but there is only a limited number of earthquake engineering professionals to deal with the problem. As a result of this, automatic intelligence-monitored warning and mitigation systems are required. According to the findings, in this paper, an artificial intelligence model based on machine learning is being built to enable an autonomous approach for analysing and identifying risk potential, then alerting stakeholders and initiating risk reduction steps. It is possible to monitor slope movement and deformations remotely and autonomously in a variety of ways, including through the use of automated seismograph connections and collaboration with local, state, and national disaster relief organisations. Damage models typical of India current urban infrastructure can be used in modelling and simulation scenarios, and nonlinear seismic analysis scenarios can be used for these models. People who work with machine learning in earth quake prediction applications can use data mining techniques like decision trees and geographic information systems to make their applications smarter by making them smarter. © 2022 IEEE.

关键词： Earthquakes

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Generating Recommendations for Various Problems Using data mining and machine learning Algorithms 3rd

Generating Recommendations for Various Problems Using Data M...

引用

3rd international conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Lobo, L.M.R.J. Birbal, Kale Manoj Department of Computer Science & Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9789811688614

The system available today which generates recommendations is supported by the user’s information collected within the past. It does not include intention at a specific time. The utilization of data mining and machine learning algorithms offers real-time recommendations employing a fitness function and models that estimate the suitability of recommended lists. The utilization of a social network provides user-generated content during a much more convenient scenario. It also uses a user experience. Recommendation systems play an outsized role in providing quality to those. In this paper, we present how recommendations are often produced using data processing and machine learning algorithms. Three systems with varied applications are addressed during this paper. First, a recommendation is used for better crop cultivation using certain parameters of concern with the assistance of association rule mining and genetic algorithms. Second, a way to recommend videos for advertisements from those available with titles, descriptions, and hashtags as extracted features is presented. These use machine learning-associated multi-label classification algorithm. Finally, an anti-vice recommendation system that uses neural networks to get recommendations is brought forward. Altogether the three cases, it is observed that the accuracy and efficiency of recommendations are upgraded. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Detection of Water-Bed Cyber Attacks Using Artificial Neural Networks

Detection of Water-Bed Cyber Attacks Using Artificial Neural...

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

作者： A. Balaji S Ronal Regan V Sunil Kumar R Ranjith Kumar Department Of Information Technology Panimalar Engineering College Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Cyber threat intelligence gathered from previous attacks may provide light on the methods and tools employed by cybercriminals, which can aid in both the reconstruction of assaults and the prediction of their future trajectory. Consequently, to mitigate these consequences, cyber security analysts use threat intelligence, alert correlations, machine learning, and enhanced visualisations. The impact and development of machine learning algorithm are booming in the current scenario. These algorithm are working perfectly by identifying the pattern based on instance based or model based learning. Well the model based learning is more efficient then the instance based learning. Because the instance based learning identify the pattern by memorising its pattern where the model based training try to form a boundary between these and try to classify them.

关键词： Training machine learning algorithms Training data Artificial neural networks Prediction algorithms Real-time systems Trajectory

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A Novel Smartphone-Based Human Activity recognition Using Deep learning in Health care 3rd

A Novel Smartphone-Based Human Activity Recognition Using De...

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3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Soni, Vaibhav Yadav, Himanshu Semwal, Vijay Bhaskar Roy, Bholanath Choubey, Dilip Kumar Mallick, Dheeresh K. Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India Department of Computer Science and Engineering Indian Institute of Information Technology Bihar Bhagalpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811958670

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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The Application of Deep learning in Micro-Expression recognition 3

The Application of Deep Learning in Micro-Expression Recogni...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Zhao, Xinyuan Sun, Furong Shen, Xunbing Jiangxi Univ Chinese Med Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665417907

With the advent of the era of big data, the application of artificial intelligence and big data technology has led to widespread interest in facial expression recognition. In addition to the common macro-expressions in daily life, facial expressions also have an imperceptible subtle expression called micro-expressions. Micro-expression is a very fast expression, lasting only 1/25 seconds to 1/5 seconds, expressing the real emotions that people try to suppress and hide. Micro-expressions have important applications in public safety, judicial criminal investigation, clinical medicine, etc. Therefore, the research on micro-expression recognition has been increasing in recent years. In the year of 2006, Hinton's proposal of deep learning in an article in "Science" made it appear in front of the world as a new field of machine learning, which set off a wave of deep learning. Deep learning is currently one of the hottest research directions in artificial intelligence and machine learning, and it is widely used in speech recognition, natural language processing, image recognition and other fields. Because of the characteristics of micro-expressions such as short time and subtle changes, traditional machine learning algorithms have poor robustness. Therefore, this paper summarizes the research on microexpression recognition based on deep learning methods, mainly including some mainstream algorithms such as DBN, CNN, and discusses the development problems and trends of deep learning on micro-expression recognition, hoping to provide reference for the subsequent research in this field.

关键词： artificial intelligence micro-expression machine learning deep learning Deep Belief Network(DBN) Convolutional Neural Network(CNN)

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Computer Aided Image recognition Technology for Food Microbial Detection

Computer Aided Image Recognition Technology for Food Microbi...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yanchun Ruan Jiangsu Vocational College Tourism Yangzhou China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This article aims to explore the application potential of computer-aided image recognition technology in the field of food microbial detection, in order to overcome the limitations of traditional detection methods, such as long time-consuming and cumbersome operation. In order to achieve this goal, an image recognition system of food microorganisms based on advanced image processing algorithm and machine learning model is designed and constructed. In terms of methods, this article uses high-resolution microscope and digital camera to collect images, and uses image preprocessing, segmentation, feature extraction and other technologies to process images; Then, DL (Deep learning) model is applied to learn and classify the extracted microbial characteristics. The experimental results show that the recognition accuracy of the system for Escherichia coli, Salmonella and other food microorganisms is over 95%, and the detection time is significantly shorter than that of the decision tree method. This achievement proves the effectiveness and practicability of computer-aided image recognition technology in food microbial detection. It has a certain contribution to improving the detection ability of food microorganisms.

关键词： Technological innovation Salmonella Image recognition Microorganisms machine learning algorithms Accuracy Computational modeling Feature extraction Food safety Usability

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