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Teeth Reconstruction Using Artificial Intelligence: Trends, Perspectives, and Prospects

Journal of the California Dental Association 2023年第1期51卷

作者： Chau, Reinhard Chun Wang Thu, Khaing Myat Hsung, Richard Tai Chiu Lam, Walter Yu Hang Faculty of Dentistry The University of Hong Kong Hong Kong Department of Computer Science Hong Kong Chu Hai College Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Background: Artificial intelligence (AI) applications in dental restorative procedures have significantly developed over recent years. However, there is a lack of documentation regarding the types of AI used in tooth reconstruction. Types of Studies Reviewed: Studies using AI in tooth reconstruction were electronically searched over three databases PubMed, Cochrane library, and Google Scholar. The relevancy of theme tooth reconstruction was prioritized in searching. Only original research was included without the restriction of publication time or any filters. Results: A total of 18 studies were included in this review: 5 reported programmable AI systems based on mathematical models such as statistical estimation, 9 studies used biogeneric tooth libraries, and 4 presented the deep learning models. Practical Implications: AI has gained significant progress over the past two decades as a powerful tool for automated tooth reconstruction. However, further studies are required to compare different forms of AIs and to assess their clinical performance in the reconstruction of occlusal surfaces. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Artificial intelligence computer neural networks Dentistry 4.0 prosthodontics restorative dentistry

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper.

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p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

arXiv

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arXiv 2022年

作者： Munteanu, Alexander Omlor, Simon Peters, Christian Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany

We study the p-generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard normal cdf, by a p-generalized normal distribution for p ∈ [1, ∞). The p-generalized normal distributions (Subbotin, 1923) are of special interest in statistical modeling because they fit much more flexibly to data. Their tail behavior can be controlled by choice of the parameter p, which influences the model’s sensitivity to outliers. Special cases include the Laplace, the Gaussian, and the uniform distributions. We further show how the maximum likelihood estimator for p-generalized probit regression can be approximated efficiently up to a factor of (1 + Ε) on large data by combining sketching techniques with importance subsampling to obtain a small data summary called coreset. Copyright © 2022, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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An Energy-Efficient Event Reliability Protocol for Wireless Communication Networks

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Mobile Information Systems 2022年第1期2022卷

作者： Khan, Gulista Kumar Agarwal, Ambuj Khapre, Shailesh Arunkumar, G. Shankar, Achyut Khosravi, Mohammad R. Faculty of Engineering Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh Raipur India School of Computer Science and Engineering VIT University Vellore India Department of Computer Science and Engineering Amity University Noida India Faculty of Engineering and Built Environment University of Johannesburg Johannesburg South Africa Department of Computer Engineering Persian Gulf University Bushehr Iran

Nowadays, WSN (wireless sensor networks) are used in every aspect of life. It consists of tiny sensor nodes that are capable of sensing, aggregating, dispensation, and sending data for end-user queries. Two major constraints in WSN are power consumption and reliability. As we know, power is limited per sensor node, so efficient utilization of power is needed all the way. Another constraint is reliability. Both constraints are in conflict with each other. If we try to increase reliability by sending the same packet on a different path, then it will increase power consumption. Many algorithms have been defined in the past to reduce power consumption and have reliable transmission. Basically, there are two types of reliability mechanisms: packet reliability and occurrence reliability. Package reliability explains about all packages must be reached at the destination. Event reliability ensures that at least one event should be successful in a specific region. This paper proposes an analysis model to evaluate the reliability of wireless sensor networks. In addition, this paper has proposed a reliability technique for events. The proposed algorithm has been simulated, analyzed, and compared with the already-published algorithms, namely BaseModel, SWIA, and ERP techniques. © 2022 Gulista Khan et al.

关键词： Electric power utilization

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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Big data in Social Media: Analyzing Trends, Patterns and Challenges 2

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big data analytics and Machine Learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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ADOD: Adaptive Density Outlier Detection

ADOD: Adaptive Density Outlier Detection

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IEEE International Conference on data Mining (ICDM)

作者： Li Qian Jing Qian Xin Sun Wengang Guo Christian Böhm Institute of Informatics Ludwig Maximilian University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Carbon Neutral R&D Center China Railway Hi-Tech Industry Corporation Limited Beijing China Faculty of Data Science City University of Macau Macau China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Outlier detection plays a dual role in data analysis: cleansing data to optimize the performance of downstream tasks and identifying potentially rare valuable events or patterns. Proximity-based methods, which are independent of data distribution assumptions, are plagued by parameter selection and performance challenges when handling data with varying densities. This study proposed a novel unsupervised algorithm named Adaptive Density Outlier Detection (ADOD) to address these challenges. The core innovation of ADOD involves two main aspects: adaptive neighborhood boundaries and density consistency scoring. First, instead of relying on a predefined fixed radius, ADOD employs perplexity to calculate the local scale of each data point. It then dynamically adjusts the neighborhood boundaries according to this scale to adapt to data with varying densities. Second, ADOD estimates local density using a mutual neighbor graph and combines the density differences between data points and their neighbors to compute outlier scores, effectively distinguishing outliers that significantly deviate from their surroundings. This study evaluated ADOD on one synthetic and 32 real datasets, and compared it with 14 classical and state-of-the-art algorithms from different categories. Extensive experimental results demonstrated the superior performance of ADOD, achieving the highest average accuracy across ROC, P@N, and AP metrics. This study promotes the development of outlier detection techniques and expands their potential for real-time applications.

关键词： Measurement Technological innovation Accuracy data analysis Sensitivity analysis Heuristic algorithms Memory management Nearest neighbor methods Real-time systems Anomaly detection

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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