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Intelligent Diagnosis of Alzheimer45s Disease Based on machine learning 4

Intelligent Diagnosis of Alzheimer45s Disease Based on Machi...

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4th international Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2023

作者： Li, Mingyang Liu, Hongyu Li, Yixuan Wang, Zejun Yuan, Yuan Dai, Honglin School of Liberal Arts and Sciences China University of Petroleum-Beijing at Karamay Xinjiang Karamay834000 China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum Beijing102249 China School of Business Administration China University of Petroleum-Beijing at Karamay Xinjiang Karamay834000 China

ISBN: (纸本)9798400708138

this study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance. © 2023 ACM.

关键词： Alzheimer's Disease machine learning Missing data Handling XGBoost

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

4th International Conference on Trends in Cognitive Computat...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

ISBN: (纸本)9789811994821

the proceedings contain 43 papers. the special focus in this conference is on Trends in Cognitive Computation Engineering. the topics include: Descriptive Analysis for Electric Bus During Non-Operational Stage;the Effectiveness Level on the Electric Buses Operation: Case Study for Affordability and Accessibility;IoMT-based Android Application for Monitoring COVID-19 Patients Using Real-Time data;A Cost-Effective Unmanned Ground Vehicle (UGV) Using Swarm Robotics Technology for Surveillance and Future Combat;neural Network-Based Obstacle and Pothole Avoiding Robot;a Comparative Study of Psychiatric Characteristics Classification for Predicting Psychiatric Disorder;Material Named Entity recognition (MNER) for Knowledge-Driven Materials Using Deep learning Approach;An Improved Optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid machine learning Approach;analyzing the Effectiveness of Several machine learning Methods for Heart Attack Prediction;Ensemble machine learning Technique for Identifying COVID-19 from CT Scan Images;solving the Royalty Payment Problem through Shooting Method;ECG Signal Classification Using Transfer learning and Convolutional Neural Networks;partitional Technique for Searching Initial Cluster Centers in K-means Algorithm;a Novel Ensemble Methodology to Validate Fuzzy Clusters of Big data;Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study;quantum-Inspired Neural Network on Handwriting datasets;an Efficient and Secure data Deduplication Scheme for Cloud Assisted Storage Systems with Access Control;priority-Based Intelligent Reflecting Surface for Uplink 6G Communication;identifying Duplicate Questions Leveraging Recurrent Neural Network;stegoPix2Pix: Image Steganography Method via Pix2Pix Networks;machine learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features;low-Cost Energy Efficient Encryption Algorithm for P

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Forensic Communication Analysis: Challenges and Opportunities

Forensic Communication Analysis: Challenges and Opportunitie...

引用

4th IEEE Annual international conference on Cyber Security and Resilience (IEEE CSR)

作者： Xi, Jian Jaeckel, Lukas Spranger, Michael Siegel, Melanie Labudde, Dirk Univ Appl Sci Mittweida Forens Sci Invest Lab FoSIL Mittweida Germany Univ Appl Sci Darmstadt Darmstadt Germany

ISBN: (纸本)9798350375367

Recently, mobile forensics has received considerable attention from researchers and practitioners due to its significant role in our society. With the advent of mobile devices and the breakthroughs in new communication technologies, various devices have risen to prominence in daily communication, where the intermingling of text, images, videos, audio, and calls occurs across different communication channels, resulting in massive data dumps. While this heterogeneity enriches the communication experience, it challenges Law Enforcement Agencies (LEAs) and Digital Forensic Practitioners (DFPs) to inspect and find evidence in the communication data dumps automatically. However, despite the rich diversity of existing tools and methodologies in analyzing mobile forensic data, there are several problems that these existing tools have in common, which are related to data analysis in evidence collection. therefore, this study discusses the current forensic solutions and new approaches from a practical point of view. Based on this, and considering the investigative work experience with LEAs in Germany, we share our expectations and knowledge about tackling big mobile forensic data. Instead of providing a step-by-step guide for conducting a specific forensic investigation, this study aims to inform the reader about the technologies and tools used for data analysis in mobile forensics, along with new research directions.

关键词： Mobile Forensics Joint Semantic Analysis Multi-modal machine learning Text mining Digital Investigation

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Restaurant Queuing Time Prediction Using Random Forest Regression 12

Restaurant Queuing Time Prediction Using Random Forest Regre...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Xue, Yijia Zhang, Xiang DukeKunshan Univ Suzhou 215316 Peoples R China Southeast Univ Nanjing 210096 Peoples R China

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

Unknown queuing time often brings negative consumer experience in restaurant service. Prediction of queuing time has been a critical task to benefit both consumers and restaurants. However, current research mainly focuses on the queuing theory, which requires collecting prior knowledge and survey. this paper proposes a queuing time prediction model based on Random Forest Regression with features selection method to ease the limitations of previous research. Random Forest Algorithm have achieved enormous success in various fields. Experiments on a real-life restaurant queuing database validate the practicality of the model. the proposed method outperforms the baseline model on the experimental data. We conclude the intrinsic information of the queuing model with the feature selection method, which will help improve other application models in queuing scenarios.

关键词： Queuing time prediction feature selection random forest regression data mining machine learning

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mining Social Media for data-Driven Analysis of Educational data with machine learning 4

Mining Social Media for Data-Driven Analysis of Educational ...

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4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Maceda, Lany L. Cordovilla, Jo-Ann M. Locaba, John Ruel L. Satuito, Arlene A. Abisado, Mideth B. Bicol University Computer Science and Information Technology Department Legazpi City Philippines Bicol University Physics Department Legazpi City Philippines College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9798350391183

the Philippine Republic Act 10931, otherwise known as the Universal Access to Quality Tertiary Education (UAQTE) Act aims to provide free higher education opportunities and subsidies to undergraduate students. Despite the recent increase in its national budget allocation, there remains uncertainty on its effectiveness in the perspective of its stakeholders. In this study, we performed topic modeling analysis using Latent Dirichlet Allocation on a corpus of 7,754 preprocessed texts sourced from Facebook and Twitter. Among the identified themes, Clientele Satisfaction stands out as the dominant topic, followed by Information and Inquiries, Social Media Presence, Gratitude and Appreciation, and Educational Opportunities. the emerging dominant topics per region conveyed expressions of gratitude and appreciation for the support provided by the program. However, based on the analysis, the policymakers and program implementers should consider improving the dissemination of information to their stakeholders. this study offers insights into public perceptions, emphasizing the programs' impacts and areas necessitating improvements. © 2024 IEEE.

关键词： Adversarial machine learning

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MultiPoseSeg: Feedback Knowledge Transfer for Multi-Person Pose Estimation and Instance Segmentation 26

MultiPoseSeg: Feedback Knowledge Transfer for Multi-Person P...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Ahmad, Niaz Khan, Jawad Kim, Jeremy Yuhyun Lee, Youngmoon Hanyang Univ Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-person pose estimation and instance segmentation suffer large performance loss when images are with an increasing number of people and their uncontrolled complex appearance. Yet, existing models cannot efficiently leverage unbalanced training images, i.e., few of them are with multi-person, and most are with single-person, making them ineffective for challenging multi-person scenarios. To tackle multi-person cases with a limited portion of them, we propose MultiPoseSeg, a data preparation and feedback knowledge transfer system designed for multi-person pose estimation and instance segmentation. First, MultiPoseSeg categorically performs random data reduction to reduce the single-person bias from the train dataset. Second, MultiPoseSeg employs the knowledge transfer from ancestor models to converge the model learning with a limited amount of data and time. this way, our model learns and train on human pose and instance segmentation to advance the training and testing accuracy. Finally, MultiPoseSeg proposes keypoint maps to identify the keypoint coordinates for soft and hard keypoints and segmentation maps to assign centroid to each human instance, which helps to cluster the pixels to a particular instance. We have evaluated MultiPoseSeg using COCO and OCHuman challenging datasets and demonstrated MultiPoseSeg outperforms state-of-the-art bottom-up models in terms of both accuracy and runtime performance, achieving 0.728 mAP for pose and 0.445 mAP for segmentation on COCO dataset. All the unbiased data and code has been made available at: https://***/RaiseLab/MultiPoseSeg

关键词： Training Image segmentation Runtime Codes Pose estimation data models pattern recognition

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A method of integrating machine learning algorithm to predict user behavior 4

A method of integrating machine learning algorithm to predic...

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4th international Academic Exchange conference on Science and Technology Innovation, IAECST 2022

作者： Qi, Yue Wang, Xiaocui Li, Na Zhang, Xue Shandong Institute of Commerce and Technology Jinan250001 China Shandong Communication and Media College Jinan250001 China

ISBN: (纸本)9798350320008

At present, mobile operators usually use data mining algorithms and expert experience to analyze and predict user behavior. Market frontline of two kinds of algorithms. the application satisfaction and effect are limited. this paper fully combines the advantages of the two methods, and proposes to carry out user behavior prediction based on the recursive partition feature of distribution degree. It improves the prediction efficiency of the model. © 2022 IEEE.

关键词： machine learning

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4th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2022

4th EAI International Conference on Multimedia Technology an...

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4th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2022

ISBN: (纸本)9783031181221

the proceedings contain 59 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Fuzzy Evaluation Model of Teaching Quality of Physical Education Course Based on Deep Reinforcement learning;design of Online Teaching Mode recognition System for Ideological and Political Curriculum Based on Hash Algorithm;design of Cross Language Education Resource Sharing Platform Based on Hadoop Framework;design of Interactive Language Education Assistant System Based on data Classification;basketball Posture recognition Method in Physical Education Based on machine Vision;image recognition Method of Educational Scene Based on machine learning;human Motion recognition Method in Physical Education Based on Wearable Perception;acquiring the Cultural Characteristics of English Online Courses Based on Wavelet Transformation;sentiment Analysis of Opinions over Time Toward Saudi Women’s Sports;research on Fault Monitoring Device of Highway Bridge Expansion Joint Based on Internet of things Technology;"I Show You How I Solved It!": Empowering Novice University Students to Learn Programming and Mathematics through Self-produced Videos to Potentially Teach to their Peers;design of Enterprise Economic Dynamic Management System Based on Spark Technology;design of Enterprise Financial and Economic data Accurate Classification Management System Based on Random Forest;marketing Risk Assessment Method of Industrial and Commercial Enterprises Based on Convolutional Neural Network;evaluation Method of Enterprise Economic Management Model Effectiveness Based on Deep data mining;Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network;real-Time Detection and recognition of License Plates for Traffic Monitoring.

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Topic Modelling and Interpretable Cost Estimation for Medical Insurance Fraud Detection 1

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16th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Kemp, James Barker, Christopher Good, Norm Bain, Michael Univ New South Wales Sydney NSW Australia Australian Govt Dept Hlth & Aged Care Sydney NSW Australia Commonwealth Sci & Ind Res Org Canberra Qld Australia

ISBN: (数字)9783031670886

ISBN: (纸本)9783031670879;9783031670886

Medical insurance incurs significant costs and can be susceptible to fraud or waste. machine learning approaches to automating fraud detection are becoming commonplace. Real-world pipelines including decision support systems for compliance activities on medical insurance claims may include requirements such human-interpretability and estimates of recoverable costs, to assist with prioritisation and investigation. We previously developed a framework for learning claim contexts and provider roles, incorporating domain knowledge through the insurance item ontology, and ranking providers for audit based on cost differences between similar providers. We extend this by comparing an interpretable pattern-identification cost estimator to the original scoring method and evaluating on a large real-world claims dataset. Compared to our previous cost estimator performance was similar, but with the advantage of immediately interpretable results. Results show incorporating context discovery and domain knowledge into fraud detection algorithms assists identification of comparable providers and generation of interpretable results for subject-matter experts in the decision-support process.

关键词： Unsupervised machine learning data mining Orthopedic procedures National health insurance Fraud

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Detection and Classification of Faults in Dynamically Controlled Systems using Gaussian Process Regression with Hidden Markov Model 4

Detection and Classification of Faults in Dynamically Contro...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Almusawi, Muntadher Muhssan Karuna, Gotlur Suganya, E. Yenugu, Bhanu Chandar thamizhkani, B. College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Srkr Engineering College Department of Electrical and Electronics Engineering Bhimavaram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, cyber security has evolved over decades in response to increasing complexity and frequency of cyber threats. Traditional protection methods are related to firewalls, anti-virus programs, and intrusion detection systems, rely on pattern matching to counter known attacks. However, these methods are limited in detecting unknown threats, highlighting the need for advanced cyber security measures. In addressing emerging and unknown threats, emphasizing the need for more proactive and adaptive security approaches. To overcome these drawbacks, a Gaussian Process Regression with Hidden Markov Model (GPR-HMM) is proposed for fault detection and classification of faults in dynamically controlled systems using Electrical Fault Detection and Classification (EFDCD) dataset. Initially, for preprocessing, data cleaning, normalization, and scaling are performed using Short-Time Fourier Transform (STFT). then, for feature extraction Multi Scale Entropy (MSE) is considered to quantify the complexity of time series data at multiple scales. At last, GPR-HMM is employed as classifier for detection and classification of faults. From the results, it clearly shows that the proposed GPR-HMM obtained accuracy value in the context faults detection when compared to existing data Entry and Office Automation (DEOA). © 2024 IEEE.

关键词： Hidden Markov models

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